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This document defines the Semantic Web Services Language (SWSL), which is used to specify the Semantic Web Services Ontology (SWSO) as well as individual Web services. The language consists of two parts: SWSL-FOL, a full first-order logic language, and SWSL-Rules, as rule-based language. SWSL-FOL is primarily used for formal specification of the ontology and is intended to provide interoperability with other first-order based process models and service ontologies. In contrast, SWSL-Rules is designed to be an actual language for service specification.
This document is part of a member submission, offered by National Institute of Standards and Technology (NIST), National Research Council of Canada, SRI International, Stanford University, Toshiba Corporation, and University of Southampton on behalf of themselves and the authors.
This is one of four documents that make up the submission. These documents define the Semantic Web Services Framework (SWSF). This submission has been prepared by the Semantic Web Services Language Committee of the Semantic Web Services Initiative.
The W3C Team Comment discusses this submission in the context of W3C activities. Public comment on this document is invited on the mailing list public-sws-ig@w3.org (public archive). Announcements and current information may also be available on the SWSL Committee Web site.
By publishing this document, W3C acknowledges that National Institute of Standards and Technology (NIST), National Research Council of Canada, SRI International, Stanford University, Toshiba Corporation, and University of Southampton have made a formal submission to W3C for discussion. Publication of this document by W3C indicates no endorsement of its content by W3C, nor that W3C has, is, or will be allocating any resources to the issues addressed by it. This document is not the product of a chartered W3C group, but is published as potential input to the W3C Process. Publication of acknowledged Member Submissions at the W3C site is one of the benefits of W3C Membership. Please consult the requirements associated with Member Submissions of section 3.3 of the W3C Patent Policy. Please consult the complete list of acknowledged W3C Member Submissions.
1 Introduction
2 The Language
2.1
Overview of SWSL-Rules and SWSL-FOL
2.2
Basic Definitions
2.3
Horn Rules
2.4
The Monotonic Lloyd-Topor Layer
2.5
The NAF Layer
2.6
The Nonmonotonic Lloyd-Topor Layer
2.7
The Courteous Rules Layer
2.8
The HiLog Layer
2.9
The Equality Layer
2.10
The Frames Layer
2.11
Reification
2.12
Skolemization in SWSL-Rules
2.13
SWSL-Rules and XML Schema Data Types
2.16
Semantics of SWSL-Rules
2.15
SWSL-FOL: The First-order Subset of SWSL
2.16
Semantics of SWSL-FOL
2.17
Future Extensions
3 Combining SWSL-Rules and SWSL-FOL
4 Serialization of SWSL in RuleML
4.1
Serialization of the HiLog Layer
4.2
Serialization of Explicit Equality
4.3
Serialization of the Frames Layer
4.4
Serialization of Reification
4.5
Serialization of SWSL-FOL
5 Glossary
6 References
This document is part of the technical report of the Semantic Web Services Language (SWSL) Committee of the Semantic Web Services Initiative (SWSI). The overall structure of the report is described in the document titled Semantic Web Services Framework Overview.
SWSL is a logic-based language for specifying formal characterizations of Web service concepts and descriptions of individual services. It includes two sublanguages: SWSL-FOL -- a full first-order logic language, which is used to specify the service ontology (SWSO), and SWSL-Rules -- a rule-based sublanguage, which can be used both as a specification and an implementation language. As a language, SWSL is domain-independent and does not include any constructs specific to services. Those constructs are defined by the Semantic Web Service Ontology, which appears in a separate document.
SWSL-Rules includes a novel combination of features that hitherto have not been present in a single system. However, almost all of the features of SWSL-Rules have been implemented in either FLORA-2 [Yang04], SweetRules [Grosof2004b], or the commercial Ontobroker [Ontobroker] system. Extensive feedback collected from the users of these systems has been incorporated in the design of the corresponding features in SWSL-Rules.
In contrast to SWSL-Rules, we do not envision the need for a full first-order reasoner based on SWSL-FOL. Instead, SWSL-FOL is intended largely as a specification language for SWSO, and specialized reasoners will be used to reason with the service ontology. In addition, SWSL-FOL will serve as a common platform to support semantic interoperability among the different first-order based service ontologies, such as OWL-S [OWL-S 1.1].
Relationship between SWSL-FOL and SWSL-Rules. SWSL includes two separate sublanguages, because we believe that different tasks associated with Semantic Web services are better served by different knowledge representation formalisms.
SWSL-Rules is a rule-based language with non-monotonic semantics. Such languages are better suited for tasks that have programming flavor and that naturally rely on default information and inheritance. These tasks include service discovery, contracting, policy specification, and others. In addition, rule-based languages are quite common both in the industry and research, and many people are more comfortable using them even for tasks that may not require defaults, such as service profile specification. Applications of SWSL and of the ontology built using SWSL are discussed in the Application Scenarios document.
In contrast, first-order logic is found more suitable for specifying process ontologies. One of the most prominent examples that uses this approach is PSL [Gruninger03a]. SWSL-FOL was developed to satisfy this need. Unfortunately, first-order and nonmonotonic semantics cannot be used together in the same language, so SWSL provides a "bridge" between the two sublanguages by describing how one can work in either sublanguage and use specifications written in the other sublanguage. This bridge is described in section titled Combining SWSL-Rules and SWSL-FOL.
The basic idea is as follows. First, as shown in Figure 2.2, both sublanguages share a common and useful core where they coincide both syntactically and semantically. Second, section Combining SWSL-Rules and SWSL-FOL describes a methodology for translating SWSL-FOL specifications into SWSL-Rules with "minimal loss." This means that inferences made using the translated specification are sound with respect to the original SWSL-FOL specification, and the "lost" inferences (i.e., formulas that are derivable from the original but not from the translated specification) are, in some sense, minimized. This approach was used in translating the axioms of PSL Core and PSL Outer Core into SWSL-Rules in section PSL in SWSL-FOL and SWSL-Rules.
The layered structure of SWSL. Both SWSL-Rules and SWSL-FOL are presented as layered languages. Unlike OWL, the layers are not organized based on the expressive power and computational complexity. Instead, each layer includes a number of new concepts that enhance the modeling power of the language. This is done in order to make it easier to learn the language and to help understand the relationship between the different features. Furthermore, most layers that extend the core of the language (either SWSL-Rules or SWSL-FOL) are independent from each other -- they can be implemented all at once or in any partial combination. This can provide certain guidance to vendors who might be interested only in a particular subset of the features.
Complexity. The layers of SWSL are not organized around the complexity. In fact, except for the equality layer, which boosts the complexity, all layers have the same complexity and decidability properties. For SWSL-Rules, the most important reasoning task is query answering. The general problem of query answering is known to be only semi-decidable. However, there are large classes of problems that are decidable in polynomial time. The best-known, and perhaps the most useful, subclass consists of rules that do not use function symbols. However, many decidable classes of rules with function symbols are also known [Lindenstrauss97].
As mentioned in the introduction, SWSL consists of two separate sublanguages, which have layered structure. This section gives an overview of these layers.
The SWSL-Rules language is designed to provide support for a variety of tasks that range from service profile specification to service discovery, contracting, policy specification, and so on. The language is layered to make it easier to learn and to simplify the use of its various parts for specialized tasks that do not require the full expressive power of SWSL-Rules. The layers of SWSL-Rules are shown in Figure 2.1.
Figure 2.1: The Layered Structure of SWSL-Rules
The core of the language consists of the pure Horn subset of SWSL-Rules. The monotonic Lloyd-Topor (Mon LT) extension [Lloyd87] of the core permits disjunctions in the rule body and conjunction and implication in the rule head. NAF is an extension that allows negation in the rule body, which is interpreted as negation-as-failure. More specifically, negation is interpreted using the so called well-founded semantics [VanGelder91]. The nonmonotonic Lloyd-Topor extension (Nonmon LT) further permits quantifiers and implication in the rule body. The Courteous rules [Grosof99a] extension introduces two new features: restricted classical negation and prioritized rules. HiLog and Frames extend the language with a different kind of ideas. HiLog [Chen93] enables high degree of meta-programming by allowing variables to range over predicate symbols, function symbols, and even formulas. Despite these second-order features, the semantics of HiLog remains first-order and tractable. It has been argued [Chen93] that this semantics is more appropriate for many common tasks in knowledge representation than the classical second-order semantics. The Frames layer of SWSL-Rules introduces the most common object-oriented features, such as the frame syntax, types, and inheritance. The syntax and semantics of this extension is inspired by F-logic [Kifer95] and the followup works [Frohn94, Yang02, Yang03]. Finally, the Reification layer provides a mechanism for making objects out of a large class of SWSL-Rules formulas, which puts such formulas into the domain of discourse and allows reasoning about them.
All of the above layers have been implemented in one system or another and have been found highly valuable in knowledge representation. For instance, FLORA-2 [Yang04] includes all layers except Courteous rules and Nonmonotonic Lloyd-Topor. SweetRules [Grosof2004b] supports Courteous extensions, and Ontobroker [Ontobroker] supports Nonmonotonic Lloyd-Topor and frames.
Four points should be noted about the layering structure of SWSL-Rules.
naf
, for
negation-as-failure, and neg
, for classical negation.
Likewise, it distinguishes between the classical
implication, <==
and
==>
, and the if-then connective :-
used
for rules.
SWSL-FOL is used to specify the dynamic properties of services, namely, the processes that they are intended to carry out. SWSL-FOL also has layered structure, which is depicted in Figure 2.2.
Figure 2.2: The Layers of SWSL-FOL and Their Relationship to SWSL-Rules
The bottom of Figure 2.2 shows those layers of SWSL-Rules that have monotonic semantics and therefore can be extended to full first-order logic. Above each layer of SWSL-Rules, the figure shows corresponding SWSL-FOL extension. The most basic extension is SWSL-FOL. The other three layers, SWSL-FOL+Equality, SWSL-FOL+HiLog, and SWSL-FOL+Frames extend SWSL-FOL both syntactically and semantically. Some of these extensions can be further combined into more powerful FOL languages. We discuss these issues in Section SWSL-FOL: The First-order Subset of SWSL.
In this section we define the basic syntactic components that are common to all layers of SWSL-Rules. Additional syntax will be added as more layers are introduced.
A constant is either a numeric value, a symbol, a string, or a URI.
123
,
34.9
, 45e-11
. See
the section on SWSL data types for more details
on the relationship between SWSL data types and
the
primitive data types in XML Schema.
'abc#$%'
. Single quotes that are
part of a symbol are escaped with the backslash. For instance, the
symbol a'bc''d
is represented as 'a\'bc\'\'d'
. The backslash is escaped with
another backslash. Symbols that consist
exclusively of alphanumeric characters and the underscore (_
)
and begin with a letter or an underscore do not need to be quoted.
"ab'%#cd"
. A double quote
symbol that
occurs in a string must be escaped with the backslash. For instance, the
string ab"cd"""gf
is represented as "ab\"cd\"\"\"gf"
.
A full URI is a sequence of characters that has the form of
a URI, as specified by IETF, and is enclosed between _"
and "
.
For instance, _"http://w3.org/"
.
An sQName has the form prefix#local-name. Here prefix is an alphanumeric symbol that is defined to be a shortcut for a URI as specified below; local-name is a string that must be acceptable as a path component in a URI. If local-name contains non-alphanumeric symbols, it must be enclosed in double quotes: e.g., "ab%20". An sQName is treated as a macro that expands into a full URI by concatenating the expansion of prefix (the URI represented by the prefix) with local-name. For the rationale behind the use of sQName see the entry for sQName in the Glossary.
A prefix declaration is a statement of the form
prefix prefix-name = "URI".
The prefix can then be used instead of the URI in sQNames. For instance, if we define
prefix w3 = "http://www.w3.org/TR/".
then the SWSL-URI _"http://www.w3.org/TR/xquery/"
is considered
to be equivalent to w3#"TR/xquery/"
A variable is an alphanumeric symbol (plus the underscore), which
is prefixed with the ?
-sign. Examples: ?_
,
?abc23
.
A first-order term is either a constant, a variable, or an
expression of the form t(t_{1},...,t_{n})
,
where t
is a constant
and t_{1},...,t_{n}
are
first-order terms. Here the constant t
is said to be used as a
function symbol and t_{1},...,t_{n}
are
used as arguments.
Variable-free terms are also called ground.
Following Prolog, we also introduce special notation for lists:
[t_{1},...,t_{n}]
and
[t_{1},...,t_{n}|rest]
, where
t_{1},...,t_{n}
and rest
are
first-order terms. The first form shows all the elements of the list
explicitly and the latter shows explicitly only a prefix of the list and uses
the first-order term rest
to represent the tail.
We should note that, like in Prolog, this is just a convenient shorthand
notation. Lists are nothing but first-order terms that are representable with
function symbols. For instance, if cons
denotes a function symbol
that prepends a term to the head of a list then [a,b,c]
is
represented as first-order term cons(a,cons(b,c))
.
A first-order atomic formula has the same form as first-order terms except that a variable cannot be a first-order atomic formula. We do not distinguish predicates as a separate class of constants, as this is usually not necessary, since first-order atomic formulas can be distinguished from first-order terms by the context in which they appear.
As many other rule-based languages, SWSL-Rules has a
special unification operator, denoted =
.
The semantics of the unification operator is fixed and therefore
it cannot appear in a rule head.
An atomic formula of the form
=
term_{2}
where both terms are ground, is true if and only if the two terms are
identical. If term_{1} and term_{2}
have variables, then an occurrence of the above formula in a rule body is
interpreted as a test of whether a substitution exists that can make the
two terms identical. The =
predicate is related to the
equality predicate :=:
introduced by
the Equality Layer, which is discussed
later.
To test that two terms do not unify SWSL-Rules uses the
disunification operator !=
. For ground
terms, term_{1} !=
term_{2}
iff the two terms are not identical. For non-ground terms, this is
true if the two terms do not unify.
A conjunctive formula is either an atomic formula or a formula of the form
and
conjunctive formula
where and
is a conjunction connective.
Here and henceforth in similar definitions, italicized words will be
meta-symbols that denote classes of syntactic entities.
For instance, atomic formula above means ``any
atomic formula.''
An and/or formula is either a conjunctive formula or a
formula of either of the forms
or
and/or formula
and
and/or formula
In other words, an and/or formula can be an arbitrary Boolean combination of
atomic formulas that involves the connectives and
and or
.
Comments. SWSL-Rules has two kinds of comments: single line
comments and multiline comments. The syntax is the same as in Java.
A single-line comment is any text that starts with
a //
and continues to the end of the current
line. If //
starts within a string ("...") or a symbol ('...')
then these characters are considered to be part of the string or the symbol,
and in this case they do not start a comment. A multiline
comment begins with /*
and end with a
matching */
. The combination /*
does not start a
comment if it appears inside a string or a symbol. The /*
- */
pairs can be nested and a nested occurrence
of */
does not close the comment. For instance, in
/* start /* foobar */ end */
only the second */
closes the comment.
A Horn rule has the form
head :- body.
where head is an atomic formula and body is a conjunctive formula.
A Horn query is of the form
?- query.
where query is a conjunctive formula.
Rules can be recursive, i.e., the predicate in the head of a rule can occur (with the same arity) in the body of the rule; or they can be mutually recursive, i.e., a head predicate can depend on itself through a sequence of rules.
All variables in a rule are
considered implicitly quantified with ∀
outside of the rule, i.e., ∀?X,?Y,...(head
:- body)
. A variable that occurs in the body of a rule but
not its head can be equivalently considered as being
implicitly existentially quantified in the body. For instance,
∀?X,?Y ( p(?X) :- q(?X,?Y) )
is equivalent to
∀?X ( p(?X) :- ∃?Y q(?X,?Y) )
Sets of Horn rules have the nice property that their semantics can be characterized in three different and independent ways: through the regular first-order entailment, as a minimal model (which in this case happens to be the intersection of all Herbrand models of the rule set) and as a least fixpoint of the immediate consequence operator corresponding to the rule set [Lloyd87].
This layer extends the Horn layer with three kinds of syntactic sugar:
A classical implication is a statement of either of the following forms:
formula_{1} ==> formula_{2}
formula_{1} <== formula_{2}
The Lloyd-Topor implication (abbr., LT implication) is a special case of the classical implication where the formula in the head is a conjunction of atomic formulas and the formula in the body can contain both conjunctions and disjunctions of atomic formulas.
A classical bi-implication is a statement of the form
formula_{1} <==> formula_{2}
The Lloyd-Topor bi-implication (abbr., LT bi-implication) is a special case of the classical bi-implication where both formulas are conjunctions of atomic formulas.
The monotonic LT layer extends Horn rules in the following way. A rule still has the form
head :- body.
but head can now be a conjunction of atomic formulas and/or LT
implications (including bi-implications) and body can consist of
atomic formulas combined in arbitrary ways using the and
and
the or
connectives.
This extension is considered a syntactic sugar, since semantically any set of extended rules reduces to another set of pure Horn rules as follows:
head :- body_{1} or body_{2}.
reduces to
head :- body_{1}.
head :- body_{2}.
head_{1} and head_{2} :- body.
reduces to
head_{1} :- body.
head_{2} :- body.
(head_{1} <== head_{2}) :- body.
reduces to
head_{1} :- head_{2} and body.
(head_{1} ==> head_{2}) :- body.
reduces to
head_{2} :- head_{1} and body.
Complex formulas in the head are broken down using the last three reductions. Rule bodies that contain both disjunctions and conjunctions are first converted into disjunctive normal form and then are broken down using the first reduction rule.
The NAF layer add the negation-as-failure symbol, naf
.
For instance,
p(?X,?Y) :- q(?X,?Z) and naf r(?Z,?Y).
In SWSL-Rules we adopt the well-founded semantics [VanGelder91] as a way to interpret negation as failure. This semantics has good computational properties when no first-order terms of arity greater than 0 are involved, and the well-founded model is always defined and is unique. This model is three-valued, so some facts may have the ``unknown'' truth value.
We should note one important convention regarding the treatment of variables
that occur under the scope of naf
and that do not occur anywhere
outside of naf
in the same rule. The well-founded semantics was
defined only for ground atoms and the interpretation of unbound variables was
left open. Therefore, if Z
does not occur elsewhere in the rule
then the meaning of
... :- ... and naf r(?X) and ...
can be defined as
... :- ... and ∃ X (naf r(?X)) and ...
or as
... :- ... and ∀ X (naf r(?X)) and ...
In practice, the second interpretation is preferred, and this is also a convention used in SWSL-Rules.
This layer introduces explicit bounded quantifiers (both exist
and forall
), classical implication
symbols, <==
and ==>
, and the
bi-implication symbol <==>
in the rule body. This essentially
permits arbitrary first-order-looking formulas in the body of SWSL-rules. We
say "first-order-looking" because it should be kept in mind that the
semantics of SWSL-Rules is not first-order and, for example,
classical implication A <== B
is interpreted in a
non-classical way: as (A or naf B)
rather than (A or neg
B)
(where neg
denotes classical negation).
Recall that without explicit quantification, all variables in a rule are
considered implicitly quantified with forall
outside of the rule, i.e., forall ?X,?Y,...(head
:- body)
. A variable that occurs in the body of a rule but
not its head can be equivalently considered as being
implicitly existentially quantified in the body. For instance,
forall ?X,?Y ( p(?X) :- q(?X,?Y) )
is equivalent to
forall ?X ( p(?X) :- exist ?Y q(?X,?Y) )
In the scope of the naf
operator, unbound variables have
a different interpretation under negation as failure. For instance,
if ?X
is bound and ?Y
is unbound then
p(?X) :- naf q(?X,?Y)
is actually supposed to mean
forall ?X ( p(?X) :- naf exist ?Y q(?X,?Y) )
If we allow explicit universal quantification in the rule bodies then
implicit existential quantification is not enough and explicit existential
quantifier is needed. This is because forall
and exist
do not
commute and so, for example, forall ?X exist ?Y
and
exist ?Y forall ?X
mean different things. If only implicit
existential quantification were available, it would not be possible to
differentiate between the above two forms.
Formally, the Nonmonotonic Lloyd-Topor layer permits the following kinds of rules. The rule heads are the same as in the monotonic LT extension. The rule bodies are defined as follows.
and
g
or
g
naf
f
==>
g
<==
g
<==>
g
exist ?X_{1},...,?X_{n}(f)
?X_{1}
, ..., ?X_{n}
are variables that occur
positively (defined below) in f.
forall
?X_{1},...,?X_{n}(
g_{1} ==>
g_{2})
forall
?X_{1},...,?X_{n}(
g_{2} <==
g_{1})
?X_{1}
,
..., ?X_{n}
occur positively in g_{1}
Positive occurrence of a free variable in a formula is defined as follows:
and
g
iff it occurs positively in either f or g.
or
g
iff it occurs positively in both f and g.
==>
g iff it occurs positively
in g. Similarly for f <==
g,
except that now the variable must occur positively in f.
Since f <==>
g is a conjunction of two
clauses, the definition of positive occurrence follows from the previous
cases: the variable must occur positively in f or g.
exist
?X_{1},...,?X_{n}(f)
or
forall ?X_{1},...,?X_{n}(f)
iff it
occurs positively in f.
The semantics of Lloyd-Topor extensions is defined via a transformation into the NAF layer as shown below. The theory behind this transformation is described in [Lloyd87].
Lloyd-Topor transformation: The transformation is designed to eliminate the extended forms that may occur in the bodies of the rules compared to the NAF layer. These extended forms involve the various types of implication and the explicit quantifiers. Note that the rules, below, must be applied top-down, that is, to the conjuncts that appear directly in the rule body. For instance, if the rule body looks like
:-
... and
((forall X exist Y (foo(Y,Y) ==> bar(X,Z)))
<== foobar(Z))
and
...
then one should first apply the rule for <==
, then the
rules for forall
should be applied to the result, and finally the
rules for exist
.
Let the rule be of the form
:-
body_{1} and
(f ==> g)
and
body_{2}.
Then the LT transformation replaces it with the following pair of rules:
:-
body_{1} and
naf
f
and
body_{2}.
:-
body_{1} and
g
and
body_{2}.
The transformations for <== and <==> are similar.
Let the rule be
:-
body_{1} and
forall
?X_{1},...,?X_{n}(
g_{1} ==>
g_{2})
and
body_{2}.
?X_{1},...,?X_{n}
are free variables
that occur positively in g_{1}.
The LT transformation replaces this rule with the following pair of
rules, where
q(?X'_{1},...,?X'_{n})
is a new
predicate of arity n
and ?X'_{1},...,?X'_{n}
are new variables:
:-
body_{1} and
naf
q(?X'_{1},...,?X'_{n})
and
body_{2}
q(?X_{1},...,?X_{n})
:-
g_{1} and
naf
g_{2}.
The transformation for <== is similar.
Let the rule be
:-
body_{1} and
exist
?X_{1},...,?X_{n}(
f)
and
body_{2}.
where ?X_{1},...,?X_{n}
are free variables
that occur positively in f.
The LT transformation replaces this rule with the following:
:-
body_{1} and
f
and
body_{2}
That is, explicit existential quantification can be replaced in this case with implicit quantification.
The above transformations are inspired by (but are not derived from, due to a
significant difference between naf
and neg
!) the
classical tautologies (f ==> g) <==>
(neg f or g)
and forall X (f) <==>
neg exist neg X (f)
, and by the fact mentioned in
section The NAF Layer that naf
p(X)
, when X
does not occur anywhere else in the rule, is
interpreted as forall X (naf p(X))
.
The courteous layer introduces prioritized conflict handling. Four new features are introduced into the syntax:
The theory behind the courteous logic programs is described in [Grosof2004a, Grosof99a].
The courteous layer builds upon the NAF layer of SWSL.
Rule Labels: Each rule has an optional label, which is used for specifying prioritization in conjunction with the prioritization predicate (below). The syntactic form of a rule label is a term enclosed by a pair of braces: { ... }. Thus, a labeled rule has the following form:
{label} head :- body.
A label is a term, which may have variables. If so, these variables are interpreted as having the same scope as the implicitly quantified variables appearing in the rule expression. E.g., in the rule
{specialoffer(?X)}
pricediscount(?X,tenpercent) :- loyalcustomer(?X).
the label specialoffer(?X)
names the instance of the
rule corresponding to the instance ?X
.
However, the label term may not itself be a variable, so
the following is illegal syntax:
{?X}
pricediscount(?X,tenpercent) :- loyalcustomer(?X).
In general, labels are not unique; two or more rules (or instances of rules) may have the same label term. However, often it is convenient to specify rule labels uniquely within a particular given rulebase.
Classical Negation: The classical negation
connective, neg
, is permitted to appear within the head
and/or the body of a rule. Its scope is restricted to be an atomic
formula, however. Thus classical negation is restricted to appearing
within a classical literal. For example:
neg boy(?X) :- humanchild(?X) and neg male(?X).
{t14(?X,?Y)}
p(?X,?Y) :- q(?X,?Y) and naf neg r(?X,?Y).
However, the following example is illegal syntax because neg
negates a non-atomic formula.
u(?X) :- t(?X) and neg naf s(?X).
Note that the classical negation connective (neg
) is also used in
SWSL-FOL, the first-order subset of SWSL-Language. However, the semantics of
classical negation in Courteous LP (and thus SWSL-Rules) is somewhat weaker
than in FOL (and thus SWSL-FOL).
Prioritization Predicate:
The prioritization predicate
_"http://www.ruleml.org/spec/vocab/#overrides"
specifies the prioritization ordering
between rule labels, and thus between the rules labeled by those rule labels.
The name of the prioritization predicate is syntactically reserved.
In this document we will use the following prefix declaration
prefix r = "http://www.ruleml.org/spec/vocab/#"
and abbreviate the prioritization predicate using the sQName
r#overrides
.
In the future, we might adopt a different prefix, such as
"http://www.swsi.org/swsl/reserved/#"
.
A statement r#overrides(label1,label2)
indicates that the first
argument, label1
, has higher priority than the second argument,
label2
. For example,
consider the following rulebase RBC1:
{rep} neg pacifist(?X) :- republican(?X).
{qua} pacifist(?X) :- quaker(?X).
{pri1} r#overrides(rep,qua).
Here, the prioritization atom r#overrides(rep,qua)
specifies that rep
has higher priority than qua
.
Continuing that example, suppose the rulebase RBC1 also includes the
facts:
{fac1} republican(nixon).
{fac2} quaker(nixon).
Then, under the courteous semantics, the literal
neg pacifist(nixon)
is entailed as a conclusion, and the literal
pacifist(nixon)
is not entailed as a conclusion,
because the
rule labeled rep
has higher priority than the rule labeled
qua
.
The prioritization predicate r#overrides
, while its name is
syntactically reserved, is otherwise an ordinary predicate -- it can appear
freely in rules in the head and/or body. This is useful for reasoning about
the prioritization ordering.
Mutual exclusion (mutex) statements: The scope of what constitutes conflict is specified by mutual exclusion (mutex) statements, which are part of the rule base and can be viewed as a kind of integrity constraint. Each such statement says that it is contradictory for a particular pair of literals (known as the "opposers") to be inferred, if an optional condition (known as the "given") holds true. The courteous LP semantics enforce that the set of sanctioned conclusions respects (i.e., is consistent with) all the mutexes within the given rulebase. Common uses for mutexes include specifying that two unary predicates are disjoint, or that a relation is functional; examples of these uses are given below.
A mutex without a given condition has the following syntactic form:
!- lit1 and lit2 .
where lit1
and lit2
are classical literals.
Intuitively, this statement means that it is a contradiction to derive both
lit1
and lit2
.
For example:
!- pricediscount(?CUST,fivepercent) and pricediscount(?CUST,tenpercent).
says that it is a contradiction to conclude that the discount offered
to the same customer ?CUST
is both fivepercent
and
tenpercent
. As another example,
!- lion(?X) and elephant(?X).
specifies that it is a contradiction to conclude that the same individual is both a lion and an elephant.
A mutex with a condition has the following syntactic form:
!- lit1 and lit2 | condition .
Here
condition
is syntactically similar to a rule body,
and lit1
and lit2
are classical literals.
The symbol "|
" is a language keyword, which separates the
oposing literals from the condition. For example:
!- pricediscount(?CUST,?Y) and pricediscount(?CUST,?Z)
| ?Y != ?Z.
says that it is a contradiction to conclude that the discount offered
to the same customer, ?CUST
, is both ?Y
and
?Z
if ?Y
and ?Z
are distinct
values. This means that the relation
pricediscount
is functional.
Courteous LP also assumes that there is an implicit mutex between
each atom A
and its classical negation
neg A
. This implicit mutex is also known as
a "classical" mutex.
HiLog [Chen93] extends the first-order syntax with higher-order features. In particular, it allows variables to range over function symbols, predicate symbols, and even atomic formulas. These features are useful for supporting reification and in cases when an agent needs to explore the structure of an unknown piece of knowledge. HiLog further supports parameterized predicates, which are useful for generic definitions (illustrated below).
t(t_{1},...,t_{n})
, where
t
, t_{1}, ..., t_{n}
are
HiLog terms.
This definition may seem quite similar to the definition of complex
first-order terms, but, in fact, it defines a vastly larger set of
expressions. In first-order terms, t
must be a constant, while in
HiLog it can be any HiLog term. In particular, it can be a variable or even
another first-order term. For instance, the following are legal HiLog terms:
c, f(a,?X), ?X
?X(a,?Y), ?X(a,?Y(?X))
f(?X,a)(b,?X(c)), ?Z(?X,a)(b,?X(?Y)(d)), ?Z(f)(g,a)(p,?X)
We will see soon how such terms can be useful in knowledge representation.
Thus, expressions like ?X(a,?Y(?X))
are atomic
formulas and thus can have truth values (when the variables are
instantiated or quantified). What is less obvious is that
?X
is also an atomic formula. What all this means is that
atomic formulas are automatically reified and can be passed around by binding
them to variables and evaluated. For instance, the following HiLog query
?- q(?X) and ?X.
p(a).
q(p(a)).
succeeds with the above database and ?X
gets bound
to p(a)
.
Another interesting example of a HiLog rule is
call(?X) :- ?X.
This can be viewed as a logical definition of the
meta-predicate call/1
in Prolog. Such a definition does not make
sense in first-order logic (and is, in fact, illegal), but it is legal in HiLog
and provides the expected semantics for call/1
.
We will now illustrate one use of the parameterized predicates of the
form p(...)(...)
. The example shows a pair of rules that defines
a generic transitive closure of a binary predicate.
Depending on the actual predicate passed in as a parameter, we can get
different transitive closures.
closure(?P)(?X,?Y) :- ?P(?X,?Y).
closure(?P)(?X,?Y) :- ?P(?X,?Z) and closure(?P)(?Z,?Y).
For instance, for the parent
predicate, closure(parent)
is defined by the above rules to be
the ancestor relation; for the edge
relation that represents
edges in a graph, closure(edge)
will become the transitive
closure of the graph.
This layer introduces the full equality
predicate, :=:
.
The equality predicate obeys the usual
congruence axioms for equality. In particular, it is transitive,
symmetric, reflexive, and the logical entailment relation is invariant
with respect to the substitution of equals by equals. For instance, if
we are told that bob :=: father(tom)
(bob
is
the same individual as the one denoted by the
term father(tom)
) then if p(bob)
is known to be
true then we should be able to derive
p(father(tom))
. If we are also told that bob
:=: uncle(mary)
is true then we can derive father(tom):=:
uncle(mary)
.
Equality in a Semantic Web language is important to be able to state that two different identifiers represent the same resource. For that reason, equality was part of OWL [OWL Reference]. Although equality drastically increases the computational complexity, some forms of equality, such as ground equality, can be handled efficiently in a rule-based language.
The equality predicate :=:
is different from the unification
operator =
in several respects. First, for variable free
terms, term_{1} =
term_{2}
if and only if the two terms are identical. In contrast, as we have just
seen, two distinct terms can be equal with respect
to :=:
. Since :=:
is reflective, it follows
that the interpretation of :=:
always contains the
interpretation of =
. Second, the unification
operator =
cannot appear in a rule head, while the equality
predicate :=:
can. When :=:
occurs in the rule
head (or as a fact), it is an assertion (maybe conditional) that two
terms are equal. For instance, given the above definitions,
p(1,2).
p(2,3).
f(a,?X):=:g(?Y,b) :- p(?X,?Y).
entails the following equalities between distinct
terms: f(a,1):=:g(2,b)
and
f(a,2):=:g(3,b)
.
When term_{1} :=:
term_{2} occurs
in the body of a rule and term_{1}
and term_{2} have variables, this predicate is interpreted as
a test that there is a substitution that makes the two terms equal with
respect to :=:
(note: equal, not identical!). For instance,
in the query
q(1).
q(2).
q(3).
?- f(a,?X):=:g(?Y,b) and q(?Y).
one answer substitution is ?X/1,?Y/2
and the other is
?X/2,?Y/3
.
The Frames layer introduces object-oriented syntax modeled after F-logic [Kifer95] and its subsequent enhancements [Yang02, Yang03]. The main syntactic additions of this layer include
The object-oriented extensions introduced by the Frames layer are orthogonal to the other layers described so far and can be combined with them within the SWSL-Rules language.
As in most object-oriented languages, the three main concepts in the Frames layer of SWSL-Rules are objects, classes, and methods. (We are borrowing from the object-oriented terminology here rather than AI terminology, so we are refer to methods rather than slots.) Any class is also an object, and the same expression can denote an object or a class represented by this object in different contexts.
A method is a function that takes arguments and executes in the context of a particular object. When invoked, a method returns a result and can possibly alter the state of the knowledge base. A method that does not take arguments and does not change the knowledge base is called an attribute. An object is represented by its object Id, the values of its attributes, and by the definitions of its methods. Method and attribute names are represented as objects, so one can reason about them in the same language.
An object Id is syntactically represented by a ground term. Terms that do have variables are viewed as templates for collections of object Ids—one Id per ground instantiation of all the variables in the term. By term we mean any expression that can bind a variable. What constitutes a legal term depends on the layer. In the basic case, by term we mean just a first-order term. If the Frames layer is combined with HiLog, then terms are meant to be HiLog terms. Later, when we introduce reification, reification terms will also be considered.
Molecules. Molecules play the role of atomic formulas. We first describe atomic molecules and then introduce complex molecules. Although both atomic and complex molecules play the role of atomic formulas, complex molecules are not indivisible. This is why they are called molecules and not atoms. Molecules come in several different forms:
t
, m
, v
are terms then t[m
-> v]
is a value molecule.
Here t
denotes an object, m
denotes
a method invocation in the scope of the
object t
, and v
denotes a value that
belongs to a set returned by
this invocation. We call m
``a
method invocation'' because if
m = s(t_{1},...,t_{n})
, i.e., has
arguments, then
t[s(t_{1},...,t_{n}) -> v]
is
interpreted as an invocation of method s
on
arguments t_{1},...,t_{n}
in the context of
the object
t
, which returns a set of values that
contains v
.
The syntax t[m -> {v_{1},...,v_{k}}]
is
also supported; it means that if m
is invoked in the
context of the object t
then it returns a set that
contains v_{1},...,v_{k}
. Thus,
semantically, such a term is equivalent to a conjunction of t[m
-> v_{1}]
, ..., t[m ->
v_{k}]
, so the expressions t[m ->
{v_{1},...,v_{k}}]
is just a syntactic sugar.
t[m]
where t
and m
are
terms.
Boolean molecules are useful to specify things like
mary[female]
. The same could be alternatively written as
mary[female -> true]
, but this is less natural.
t
and s
are terms then t:s
is a membership molecule.
If t
and s
are variable free, then such a
molecule states that the object t
is a member of
class s
. If these terms contain variables, then such a
molecule can be viewed as many class membership
statements, one per ground instantiation of the variables.
t
and s
are terms
then t::s
is a subclass molecule.
If t
and s
are variable free, then such a
molecule states that the object t
is a subclass
of s
. As in the case of class membership molecules,
subclass molecules that have variables can be viewed as statements about
many subclass relationships.
t
, m
, v
are terms then t[m
=> v]
is a signature molecule.
If t
, m
, and v
are variable-free
terms then the informal meaning of the above signature molecule is
that t
represents a class, which has a method
invocation m
which returns a set of objects of
type v
(i.e., each object in the set belongs to
class v
).
If these terms are non-ground then the signature represents a collection
of statements—one statement per ground instantiation of the terms.
When m
itself has arguments, for instance m =
s(t_{1},...,t_{n})
, then the arguments are
interpreted
as types. Thus, t[s(t_{1},...,t_{n}) =>
v]
states that when the n
-ary method s
is invoked on object of class t
with
arguments that belong to
classes t_{1}
,
...,
t_{n}
, the
method returns a set of objects of class v
.
t[m=>]
. Its purpose is to provide type
information for Boolean valued molecules. Namely, if
m=s(t_{1},...,t_{n})
, then when the method
s
is
invoked on an object of class t
, the method arguments must
belong to classes t_{1}
,
...,
t_{n}
.
Cardinality constraints: Signature molecules can have associated cardinality constraints. Such molecules have the form
t[s(t_{1},...,t_{n}) {min : max}
=> v]
where min and max are non-negative integers such that
min ≤ max. Max can also be *
, which
means positive infinity.
Such a signature states not only that the invocation
of the method s
with arguments of type
t_{1},...,t_{n}
on an object of
class t
returns objects of class v
, but also that
the number of such objects in the result is no less than min and
no more than max.
The semantics of constraints in SWSL-Rules is similar to constraints in databases and is unlike the cardinality restrictions in OWL [OWL Reference]. For instance, if a cardinality constraint says that an attribute should have at least two values and the rule base derives only one then the constraint is violated. In contrast, OWL would infer that there is another, yet unknown, value. Likewise, if a cardinality constraint says that the number of elements is at most three while the rule base derives four unequal elements then the constraint is, again, violated. This should be compared to the OWL semantics, which will infer that some pair of derived values in fact consists of equal elements.
Signatures and type checking: Signatures are assertions about the expected types of the method arguments and method results. They typically do not have direct effect on the inference (unless signatures appear in rule bodies). The signature information is optional.
The semantics of signatures is defined as follows. First, the intended model of the knowledge base is computed (which in SWSL-Rules is taken to be the well-founded model). Then, if typing needs to be checked, we must verify that this intended model is well-typed. A well-typed model is one where the value molecules conform to their signatures. For the precise definition of well-typed models see [Kifer95]. (There can be several different notions of well-typed models. For instance, one for semi-structured data and another for completely structured data.)
A type-checker can be written in SWSL-Rules using just a few rules. Such a type checker is a query, which returns "No", if the model is well-typed and a counterexample otherwise. In particular, type-checking has the same complexity as querying. An example of such type checker can be found in the FLORA-2 manual [Yang04].
It is important to be aware of the fact that the semantics of the
cardinality constraints in signature molecules
is inspired by database theory and practice
and it is different from the semantics of such constraints in OWL
[OWL Reference]. In SWSL-Rules, cardinality constraints are restrictions on the
intended models of the knowledge base, but they are not part of the axioms
of the
knowledge base. Therefore, the intended models of the knowledge base
are determined without taking the cardinality constraints into the account.
Intended models that do not satisfy these restrictions are
discarded. In contrast, in OWL cardinality constraints are represented as
logical statements in the knowledge base and all models are computed by
taking the constraints into the account. Therefore, in OWL it is not
possible to talk about knowledge base updates that violate constraints.
For instance, the following signature married[spouse {1:1} =>
married]
states that every married person has exactly one spouse.
If john:married
is true but there is no information about
John's spouse then OWL will assume that john
has some unknown
spouse, while SWSL-Rules will reject the knowledge base as inconsistent.
If, instead, we know that john[spouse -> mary]
and
john[spouse -> sally]
then OWL will conclude that
mary
and sally
are the same object, while
SWSL-Rules will again rule the knowledge base to be inconsistent (because,
in the absence of the information to the contrary — for example, if
no :=:
-statements have been given — mary
and sally
will be deemed to be distinct objects).
Inheritance in SWSL-Rules: Inheritance is an optional feature, which is expressed by means of the syntactic features described below. In SWSL-Rules, methods and attributes can be inheritable and non-inheritable. Non-inheritable methods/attributes correspond to class methods in Java, while inheritable methods and attributes correspond to instance methods.
The value- and signature-molecules considered so far
involve non-inheritable attributes and methods. Inheritable methods
are defined using the *->
and *=>
arrow
types, i.e.,
t[m *-> v]
and t[m *=> v]
. For Boolean
methods we use
t[*m]
and t[m*=>]
.
Signatures obey the laws of monotonic inheritance, which are as follows:
t:s and s[m *=> v] entails t[m *=> v]
t::s and s[m *=> v] entails t[m => v]
These laws state that type declarations for inheritable methods are inherited to subclasses in an inheritable form, i.e., they can be further inherited. However, to the members of a class such declarations are inherited in a non-inheritable form. Thus, inheritance of signatures is propagated through subclasses, but stops once it hits class members.
Inheritance of value molecules is more involved. This type of inheritance is nonmonotonic and it can be overridden if the same method or attribute is defined for a more specific class. More precisely,
t:s and s[m *-> v] entails t[m *-> v]
unless overridden or in conflict
t::s and s[m *-> v] entails t[m -> v]
unless overridden or in conflict
Similarly to signatures, value molecules are inherited to subclasses in the
inheritable form and to members of the classes in the non-inheritable form.
However, the key difference is the phrase "unless overridden or in conflict."
Intuitively, this means that if, for example, there is a class w
in-between t
and s
such that the inheritable method
m
is defined there then the inheritance from s
is
blocked and m
should be inherited from w
instead.
Another situation when inheritance might be blocked arises due to multiple
inheritance conflicts. For instance, if t
is a subclass of both
s
and u
, and if both s
and u
define the method m
, then inheritance
of m
does not take place at all (either from s
or
from u
; this policy can be modified by specifying appropriate
rules, however). The precise model-theoretic semantics of inheritance with
overriding is based on an extended form of the Well-Founded Semantics.
Details can be found in
[Yang02].
Note that signature inheritance is not subject to overriding, so every inheritable molecule is inherited to subclasses and class instances. If multiple molecules are inherited to a class member or a subclass, then all of them are considered to be true.
Inheritance of Boolean methods is similar to the inheritance of methods and attributes that return non-Boolean values. Namely,
t:s and s[m*=>] entails t[m*=>]
t::s and s[m*=>] entails t[m =>]
t:s and s[*m] entails t[*m]
unless overridden
t::s and s[*m] entails t[m]
unless overridden
Complex molecules: SWSL-Rules molecules can be combined into complex molecules in two ways:
Grouping applies to molecules that describe the same object. For instance,
t[m1 -> v1] and t[m2 => v2] and t[m3 {6:9} => v3] and t[m4 -> v4]
is, by definition, equivalent to
t[m1 -> v1 and m2 => v2 and m3 {6:9} => v3 and m4 -> v4]
Molecules connected by the or
connective can also be combined
using the usual precedence rules:
t[m1 -> v1] and t[m2 => v2] or t[m3 {6:9} => v3] and t[m4 -> v4]
becomes
t[m1 -> v1 and m2 => v2 or m3 {6:9} => v3 and m4 -> v4]
The and
connective inside a complex molecule can also be
replaced with a comma, for brevity. For example,
t[m1 -> v1, m2 => v2]
Nesting applies to molecules in the following ``chaining'' situation, which is a common idiom in object-oriented databases:
t[m -> v] and v[q -> r]
is by definition equivalent to
t[m -> v[q -> r]]
Nesting can also be used to combine membership and subclass molecules with value and signature molecules in the following situations:
t:s and t[m -> v]
t::s and t[m -> v]
are equivalent to
t[m -> v]:s
t[m -> v]::s
respectively.
Molecules can also be nested inside predicates and terms with
a semantics similar to nesting inside other molecules. For instance,
p[a->c]
is considered to be equivalent to
p(a) and a[b->c]
. Deep nesting and, in fact, nesting in any
part of another molecule or predicate is also allowed. Thus, the
formulas
p(f(q,a[b -> c]),foo)
a[b -> foo(e[f -> g])]
a[foo(b[c -> d]) -> e]
a[foo[b -> c] -> e]
a[b -> c](q,r)
are considered to be equivalent to
p(f(q,a),foo) and a[b -> c]
a[b -> foo(e)] and e[f -> g]
a[foo(b) -> e] and b[c -> d]
a[foo -> e] and foo[b -> c]
a[b -> c] and a(q,r)
respectively. Note that molecule nesting leads to a completely
compositional syntax, which in our case
means that molecules are allowed in any place where terms are allowed.
(Not all of these nestings might look particularly natural, e.g., a[b
-> c](q,r)
or p(a[b -> c](?X))
, but there is no
good reason to reject these nestings and thus complicate the syntax either.)
Path expressions: Path expressions are useful shorthands that are widely used in object-oriented and Web languages. In a logic-based language, a path expression sometimes allows writing formulas more concisely by eliminating multiple nested molecules and explicit variables. SWSL-Rules defines path expressions only as replacements for value molecules, since this is where this shorthand is most useful in practice.
A path expression has the form
t.t_{1}.t_{2}. ... .t_{n}
or
t!t_{1}!t_{2}! ... !t_{n}
The former corresponds to non-inheritable molecules and the latter to inheritable ones. In fact, "." and "!" can be mixed within the same path expression.
A path expression can occur anywhere where a term is allowed to occur. For
instance, a[b -> c.d]
, a.b.c[e ->
d]
, p(a.b)
, and X=a.b
are all legal
formulas. The semantics of path expressions in the body of a rule and in its
head are similar, but slightly different. This difference is
explained next.
In the body of a rule, an occurrence of the first path expression above is treated as follows. The conjunction
t[t_{1} -> ?Var_{1}] and
?Var_{1}[t_{2} -> ?Var_{2}] and ... and
?Var_{n-1}[t_{n} -> ?Var_{n}]
is added to the body and the occurrence of the path expression is replaced with
the variable ?Var_{n}. In this conjunction, the variables
?Var_{1}
, ..., ?Var_{n}
are new and are used to represent intermediate values.
The second path expression is treated similarly, except that the conjunction
t[t_{1} *-> ?Var_{1}] and
?Var_{1}[t_{2} *-> ?Var_{2}] and ... and
?Var_{n-1}[t_{n} *-> ?Var_{n}]
is used. For instance, mary.father.mother = sally
in a rule body
is replaced with
mary[father -> ?F] and ?F[mother -> ?M] and ?M = sally
In the head of a rule, the semantics of path expressions is reduced
to the case of a body occurrence as follows.. If a path
expression, ρ
,
occurs in the head of a rule, it is replaced with a new
variable, ?V
, and the predicate ?V=ρ
is
conjoined to the body of the rule. For instance,
p(a.b) :- body.
is understood as
p(?V) :- body and ?V=a.b.
Note that since molecules can appear wherever terms can, path expressions of
the form a.b[c -> d].e.f[g -> h].k
are permitted.
They are conceptually similar to XPath expressions with
predicates that control the selection of intermediate nodes in XML documents.
Formally, such a path expression will be replaced with the
variable ?V
and will result in the addition of the following
conjunction:
a[b -> ?X[c -> d]] and ?X[e -> ?Y] and
?Y[f -> ?Z[g -> h]] and ?Z[k -> ?V]
It is instructive to compare SWSL-Rules path expressions with
XPath. SWSL-Rules path expressions were originally proposed for F-logic
[Kifer95] several years before XPath. The
purpose was to extend the familiar notation in object-oriented programming
languages and to adapt it to a logic-based language. It is easy to see that
the ``*'' idiom of XPath can be captured with the use of a variable. For
instance, b/*/c
applied to object e
is expressed
as e.b.?X.c
. The ``..'' idiom of XPath is also easy to
express. For instance, a//Submission/2005/b/c
applied to object d
is expressed as ?_[?_ -> d.a].b.c
. On the other hand, there
is no counterpart for the //
idiom of XPath. The reason is that
this idiom is not well-defined when there are cycles in the data (for
instance, a[b -> a]
). However, recursive descent into the
object graph can be defined via recursive rules.
The reification layer allows SWSL-Rules to treat certain kinds of formulas as terms and therefore to manipulate them, pass them as parameters, and perform various kinds of reasoning with them. In fact, the HiLog layer already allows certain formulas to be reified. Indeed, since any HiLog term is also a HiLog atomic formula, such atomic formulas are already reifiable. However, the reification layer goes several steps further by supporting reification of arbitrary rule or formula that can occur in the rule head or rule body. (Provided that it does not contain explicit quantifiers -- see below.)
Formally, if F is a formula that has the syntactic form of a rule head, a rule body, or of a rule then F is also considered to be a term. This means that such a formula can be used wherever a term can occur.
Note that a reified formula represents an objectification of the corresponding formulas. This is useful for specifying ontologies where objects represent theories that can be true in some worlds, but are not true in the present world (and thus those theories cannot be asserted in the present world). Examples include the effects of actions: effects of an action might be true in the world that will result after the execution of an action, but they are not necessarily true now.
In general, reification of formulas can lead to logical paradoxes [Perlis85]. The form of reification used in SWSL-Rules does not cause paradoxes, but other unpleasantries can occur. For instance, the presences of a truth axiom (true(?X) <--> ?X) can render innocently looking rule-bases inconsistent. However, as shown in [Yang03], the form of reification in SWSL-Rules does not cause paradoxes as long as
?X :- body
(which are legal in HiLog) are
disallowed.
We therefore adopt the above restrictions for all layers of SWSL-Rules (but not for SWSL-FOL).
As presented above, reification introduces syntactic ambiguity, which arises due to the nesting conventions for molecules. For instance, consider the following molecule:
a[b -> t]
Suppose that t
is a reification of another
molecule, c[d -> e]
. Since we have earlier said that
any formula suitable to appear in the rule body can also be viewed as a
term, we can expand the above formula into
a[b -> c[d -> e]]
But this is ambiguous, since earlier we defined the above as a commonly used object-oriented idiom, a syntactic sugar for
a[b -> c] and c[d -> e]
Similarly, if we want to write something like t[b -> c]
where t
is a reification of f[g -> h]
then we
cannot write f[g -> h][b -> c]
because this nested molecule is a syntactic sugar for f[g -> h]
and f[b -> c]
.
To resolve this ambiguity, we introduce the reification
operator, ${...}
, whose only role is to tell the parser that
a particular occurrence of a nested molecule is to be treated as
a term that represents a reified formula rather than
as syntactic sugar for the object-oriented idiom.
Note that the explicit reification operator is not required for HiLog
predicates because there is no ambiguity. For instance, we do not need to
write ${p(?X)}
below (although it is permitted and
is considered the same as p(?X)
):
a[b -> p(?X)]
This is because a[b -> p(?X)]
does not mean
a[b -> p(?X)] and p(?X)
, since the sugar is used
only for nested molecules.
In contrast, explicit reification is needed below, if we want to reify
p(?X[foo -> bar])
:
a[b -> p(?X[foo -> bar])]
Otherwise p(?X[foo -> bar])
would be treated as syntactic
sugar for sugar for
a[b -> p(?X)] and ?X[foo -> bar]
Therefore, to reify p(?X[foo -> bar])
in the above
molecule one must write this instead:
a[b -> ${p(?X[foo -> bar])}]
Example. Reification in SWSL-Rules is very powerful and yet it doesn't add to the complexity of the language. The following fragment of a knowledge base models an agent who believes in the modus ponens rule:
john[believes -> ${p(a)}].
john[believes -> ${p(?X) ==> q(?X)}].
// modus ponens
john[believes -> ?A] :-
john[believes -> ${?B ==> ?A}] and john[believes -> ?B].
Since the agent believes in p(a)
and in the modus ponens
rule, it can infer q(a)
. Note that in the above we did not
need explicit reification of p(a)
, since no ambiguity can
arise. However, we used the explicit reification anyway, for clarity.
Syntactic rules. Currently SWSL-Rules does not permit explicit quantifiers under the scope of the reification operator, because the semantics for reification given in [Yang03, Kifer04] does not cover this case. So not every formula can be reified. More specifically, the formulas that are allowed under the scope of the reification operator are:
The implication of these restrictions is that every term that represents a reification of a SWSL-Rules formula has only free variables, which can be bound outside of the term. Each such term can therefore be viewed as a (possibly infinite) set of reifications of the ground instances of that formula.
It is often necessary to specify existential information in the head of a rule
or in a fact. Due to the limitations of the logic programming paradigm, which
trades the expressive power for executional efficiency, such information
cannot be specified directly. However, existential variables in the rule heads
can be approximated through the technique known as Skolemization
[Chang73].
The idea of Skolemization is that in a formula of the form
∀ Y_{1}...Y_{n}∃ X ...
φ
the existential variable X
can be removed and replaced
everywhere in φ with the function
term f(Y_{1}...Y_{n})
, where f
is a
new function symbol that does not occur anywhere else in the
specification. The rationale for such a substitution is that,
for any query, the original rule
base is unsatisfiable if and only if the transformed rule base is
unsatisfiable
[Chang73].
This implies that the query to the original rule base can be answered if and
only if it can be answered when posed against the Skolemized rule base.
However, from the point of view of logical entailment, the Skolemized
rule base is stronger than the original one, and this is why we say that
Skolemization only approximates existential quantification, but is
not equivalent to it.
Skolemization is defined for formulas in prenex normal form, i.e., formulas where all the quantifiers are collected in a prefix to the formula and apply to the entire formula. A formula that is not in the prenex normal form can be converted to one in the prenex normal form by a series of equivalence transformations [Chang73].
SWSL-Rules supports Skolemization by providing special constants
_#
and _#1
, _#2
, _#3
,
and so on. As with other constants in SWSL, these symbols can be used
both in argument positions and in the position of a
function. For instance, _#(a,_#,_#2(c,_#2))
is a legal function term.
Each occurrence of the symbol _#
denotes a new
constant. Generation of such a constant is the responsibility of the
SWSL-Rules compiler. For instance, in _#(a,_#,_#2(c,_#2))
,
the two occurrences of _#
denote two different constants
that do not appear anywhere else. In the first case, the constant is in
the position of a function symbol. The numbered Skolem constants, such
as _#2
in our example, also denote a new constant that does
not occur anywhere else in the rule base. However, the different
occurrences of the same numbered symbol in the same rule denote
the same new constant. Thus, in the above example the two
occurrences of _#2
denote the same new symbol. Here is a
more complete example:
holds(a,_#1) and between(1,_#1,5).
between(minusInf, _#(?Y), ?Y) :- timepoint(?Y) ?Y !=
minusInf.
In the first line, the two occurrences of _#1
denote the
same new Skolem constant, since they occur in the scope of the same
rule. In the second line, the occurrence of _#
denotes a new
Skolem function symbol. Since we used _#
here, this symbol
is distinct from any other constant. Note, however, that even if we
used _#1
in the second rule, that symbol would have denoted
a distinct new function symbol, since it occurs in a separate rule and
there is no other occurrence of _#1
in that rule.
The Skolem constants in SWSL-Rules are in some ways analogous to the blank nodes in RDF. However, they have the semantics suitable for a rule-based language and it has been argued in [Yang03] that the Skolem semantics is superior to RDF, which relies on existential variables in the rule heads [Hayes04].
SWSL-Rules supports the primitive XML Schema data types. However, since SWSL-Rules is quite different from XML, it adapts the lexical representation for XML data types to the form that is more suitable for a logic-based language. The translation from the XML lexical representation of primitive data types to SWSL-Rules is straightforward.
The general rule is that each primitive value is represented by a function
term whose functor symbol is the name of the primitive data type prefixed with
an underscore (_
). The arguments of the term represent the various
components of the primitive data type. For
instance, _string("abc")
, _date(2005,7,18)
,
_decimal(123.56)
, _integer(321)
,
_float(23e5)
, and so on.
The string, decimal, integer, and float data types have a shorthand notation
(some of which had been seen before). Thus, _string("abc")
is
abbreviated to "abc"
, _decimal(123.56)
to 123.56
, _integer(321)
to 321
,
and _float(23e5)
to 23e5
.
Other primitive data types are represented using a similar notation. For
instance, the duration of 1 year, 2 months, 3 days, 10 hours, and 30 minutes
is represented as _duration(1,2,3,10,30,0)
where the first
argument of _duration
represents years and the last seconds. The
same negative duration is represented
as -_duration(1,2,3,10,30,0)
.
For another example, the
values of the dateTime type are represented as
_dateTime(2005,10,29,15,55,40)
.
It is often necessary to exchange values of primitive data types
between applications. Since the internal representations of the data types
vary from language to language, serialization into a commonly
agreed representation has been used for this purpose. SWSL-Rules supports
serialization of primitive data types via the built-in
predicate _serialize
. It takes three arguments: a SWSL-Rules
value of a SWSL-Rules data type, a URI that denotes the target of
serialization, and a result, which is a string that contains the serialized
value. Currently, the only target
is http://www.w3.org/2001/XMLSchema
, which refers to XML Schema
1.0. Other targets will be added as necessary (for example, for XML
Schema 1.1 when it is released). Example:
_serialize(_date(2005,1,1),_"http://www.w3.org/2001/XMLSchema",?Result)
binds ?Result
to "2005-01-01"
.
The predicate _serialize
is intended to work both ways: for
serialization and deserialization. Deserialization occurs when the last
argument is bound to a string representation of a data type and the first
argument is unbound. For instance,
_serialize(?Result,_"http://www.w3.org/2001/XMLSchema","2005-01-01")
binds ?Result
to _date(2005,1,1)
.
A single point of reference for the model-theoretic semantics of SWSL-Rules will be given in a separate document. Here we will only give an overview and point to the papers where the semantics of the different layers were defined separately.
First, we note that the semantics of the Lloyd-Topor leyers -- both monotonic and nonmonotonic -- is transformational and was given in Sections 2.4 and 2.6. Similarly, the Courteous layer is defined transformationally and is described in [Grosof2004a].
The model theory of NAF is given by the well-founded semantics as described in [VanGelder91]. The model theory behind HiLog is described in [Chen93] and F-logic is described in [Kifer95]. The semantics of inheritance that is used in SWSL-Rules is defined in [Yang02]. The model theory of reification is given in [Yang03] and was further extended to reification of rules in [Kifer04].
The semantics of the Equality layer is based on the standard semantics (for
instance, [Chang73]) but is modified
by the unique name assumption, which states that
syntactically distinct terms are unequal. This modification is described in
[Kifer95], and we summarize it here.
First, without equality, SWSL-Rules makes the unique name assumption. With
equality, the unique name assumption is modified to say that terms that
cannot be proved equal with respect to :=:
are assumed to be
unequal. In other words, SWSL-Rules makes a closed world assumption about
explicit equality.
Other than that, the semantics of :=:
is standard. The
interpretation of this predicate is assumed to be an equivalence relation
with congruence properties. A layman's term for this
is "substitution of equals by equals." This means that if, for
example,
t:=:s
is derived for some terms t
and s
then, for any formula φ, it is true if and only if
ψ is true, where ψ is obtained from φ by replacing some
occurrences of t
with s
.
Overall, the semantics of SWSL-Rules has nonmonotonic flavor even without NAF and its extension layers. This is manifested by the use of the unique name assumption (modified appropriately in the presence of equality) and the treatment of constraints. To explain the semantics of constraints, we first need to explain the idea of canonic models.
In classical logic, all models of a set of formulas are created equal and are given equal consideration. Nonmonotonic logics, on the other hand, carefully define a subset of models, which are declared to be canonical and logical entailment is considered only with respect to this subset of models. Normally, the canonical models are so-called minimal models, but not all minimal models are canonical.
Any rule set that does not use the features of the NAF layer and its extensions is known to have a unique minimal model, which is also its canonical model. This is an extension of the well-known fact for Horn clauses in classical logic programming [Lloyd87]. With NAF, a rule set may have multiple incomparable minimal models, and it is well-known that not all of these models appropriately capture the intended meaning of rules. However, it turns out that one such model can be distinguished, and it is called the well-founded model [VanGelder91]. A formula is considered to be true according to the SWSL-Rules semantics if and only if it is true in that one single model, and the formula is false if and only if it is false in that model.
Now, in the presence of constraints, the semantics of SWSL-Rules is defined as follows. Given a rulebase, first its canonical model is determined. In this process, all constraints are ignored. Next, the constraints are checked in the canonical model. If all of them are true, the rulebase is said to be consistent. If at least one constraint is false in the canonical model, the constraint is said to be violated and the rulebase is said to be inconsistent.
The SWSL language includes all the connectives used in first-order logic and,
therefore, syntactically first-order logic is a subset of SWSL.
When the semantics of first-order connectives differs from their
nonmonotonic interpretation, new nonmonotonic connectives are
introduced. For instance, first-order negation, neg
, has a
nonmonotonic counterpart naf
and first-order implications
<==
and ==>
have a nonmonotonic counterpart :-
.
It follows from the above that SWSL-Rules and SWSL-FOL share
significant portions of their syntax. In particular, every connective
used in SWSL-FOL can also be used in SWSL-Rules. However, not every
first-order formula in SWSL-FOL is a rule and the rules in SWSL-Rules are
not first-order formulas (because of ":-
"). Therefore,
neither SWSL-FOL is a subset of SWSL-Rules nor the other way
around. Furthermore, even though the classical connectives
neg
and ==>
/<==
can
occur in SWSL-Rules, they are embedded into an overall nonmonotonic
language and their semantics cannot be said to be exactly first-order.
Formally, SWSL-FOL consists of the following formulas:
and
ψ, φ or
ψ,
neg
φ, φ ==>
ψ,
φ <==
ψ, and φ <==>
ψ.
X
is a variable, then
the following are also SWSL-FOL formulas: exist ?X
(φ) and forall ?X
(φ). SWSL-FOL
allows to combine quantifiers of the same sort, so exist
?X,?Y
(φ) is the same as
exist ?X exist ?Y
(φ).
As in the case of SWSL-Rules, we will ise the period (".") to designate the end of a SWSL-FOL formula.
SWSL defines three extensions of SWSL-FOL. The first extension adds
the equality operator, :=:
, the second incorporates the
object-oriented syntax from the Frames layer of SWSL-Rules, the third does
the same for the HiLog layer.
Formally, SWSL-FOL+Equality has the same syntax as SWSL-FOL, but, in addition, the following atomic formulas are allowed:
SWSL-FOL+Frames has the same syntax as SWSL-FOL except that, in addition, the following is allowed:
SWSL-FOL+HiLog extends SWSL-FOL by allowing HiLog terms and HiLog atomic formulas instead of first-order terms and first-order atomic formulas.
Each of these extensions is not only a syntactic extension of SWSL-FOL but
also a semantic extension. This means that if φ and ψ are formulas
in SWSL-FOL
then φ |=
ψ in SWSL-FOL if and only if the
same holds in SWSL-FOL+Equality, SWSL-FOL+Frames, and SWSL-FOL+HiLog.
We will say that SWSL-FOL+Equality, SWSL-FOL+Frames, and SWSL-FOL+HiLog are
conservative semantic extensions of SWSL-FOL.
SWSL-FOL+HiLog and SWSL-FOL+Frames can be combined both syntactically and
semantically. The resulting language is a conservative semantic extension of
both SWSL-FOL+HiLog and SWSL-FOL+Frames. Similarly, SWSL-FOL+Equality and
SWSL-FOL+Frames can be combined and the resulting language is a conservative
extension of both. Interestingly, combining SWSL-FOL+Equality with
SWSL-FOL+HiLog leads to a conservative extension of
SWSL-FOL+HiLog, but not of SWSL-FOL+Equality!
More precisely, if φ and ψ are formulas in SWSL-FOL+Equality
and φ |=
ψ then the same holds in
SWSL-FOL+HiLog. However, there are formulas such that φ |=
ψ holds in SWSL-FOL+HiLog but not in SWSL-FOL+Equality
[Chen93].
The semantics of the first-order sublanguage of SWSL is based on the standard first-order model theory and is monotonic. The only new elements here are the higher-order extension that is based on HiLog [Chen93] and the frame-based extension based on F-logic [Kifer95]. The respective references provide a complete model theory for these extensions, which extends the standard model theory for first-order logic.
To enhance the power of the SWSL-Rules language, a number of extensions are being planned, as described below.
If-then-else. The if test
then test1
is sometimes more convenient and familiar
than the ==>
operator. More important, however, is the
fact that the more complete idiom, if test
then test1 else test2
, is known to be
very useful and common in rule-based languages. Although
the else
-part can be expressed with negation as failure,
this is not natural and most well-developed languages support
the if-then-else idiom directly. This idiom may be added to
SWSL-Rules later.
Aggregate operators. Aggregate operators, such as sum, average, etc., are important database operations. XML languages such as XPath, XSLT, and XQuery all have support for aggregation. Of these, only XQuery permits aggregation over general set comprehension. A future extension of SWSL-Rules will allow aggregation in the style of FLORA-2, which supports explicit set comprehension and nested aggregation. The general syntax of such aggregation is:
?Result = aggregate{?Var [GroupingVarList] | Query }
where aggregate can
be max
, min
, avg
, count
,
sum
, collectset
, collectbag
. The
last two aggregates return lists of the instantiations
of ?Var
that satisfy Query without the duplicates
(collectset
) and with possible duplicates
(collectbag
).
The grouping variables provide the functionality similar to GROUP
BY
of SQL. They have the effect that the aggregation produces one list
of results per every instantiation of the variables
in GroupingVarList for which Query has a solution. The
variable ?Result
gets successively bound to each such list (one
list at a time).
Constraints. Constraints play a very important role in database and knowledge base applications. As a future extension, SWSL-Rules will have database-style constraints. Database constraints are different in nature from restrictions used in Description Logic. Whereas restrictions in Description Logic are part of the same logical theory as the rest of the statements and are used to derive new statements, constraints in databases are not used to derive new information. Instead, they serve as tests of correctness for the canonical models of the knowledge base. In this framework, canonical models (e.g., the well-founded model [VanGelder91]) are first computed without taking constraints into account. These models are then checked against the constraints. The models that do not satisfy the constraints are discarded. In the case of the well-founded semantics, which always yields a single model, testing satisfaction of the constraints validates whether the knowledge base is in a consistent state.
Procedural attachments, state changes à la Transaction Logic, situated logic programs. A procedural attachment is a predicate or a method that is implemented by an external procedure (e.g., in Java or Python). Such a procedure can have a side effect on the real world (e.g., sending an email or activating a device) or it can receive information from the outside world. First formalizations of these ideas in the context of database and rule based languages appeared in [Maier81, Chimenti89]. These ideas were recently explored in [Grosof2004a] in the context of e-commerce. Transaction Logic [Bonner98] provides a seamless integration of these concept into the logic.
An attached procedure can be specified by a link statement, which associates a predicate or a method with an external program. The exact details of the syntax have not been finalized, but the following is a possibility:
attachment relation/Arity name-of-java-procedure(integer,string,...)
This syntax can be generalized to include object-oriented methods.
Another necessary extension involves update primitives - primitives for changing the underlying state of the knowledge. These primitives can add or delete facts, and even add or delete rules. A declarative account of such update operations in the context of a rule-based language is given by Transaction Logic [Bonner98]. This logic also can also be used to represent triggers (also known as ECA rules) [Bonner93].
Predicates with named arguments.
For predicates with ordered arguments,
named attributes are essentially supported by the current syntax.
For instance, if -> is viewed as an infix binary function symbol, then
p(foo -> 1, bar -> 2)
is a valid term in SWSL-Rules.
Predicates with unordered arguments can make unification exponential
and are unlikely to be supported in the future.
The "rest"-variables. The ``rest'' notation à la SCL can be useful in metaprogramming. A rest-variable binds to a list of variables or terms and it always occurs as the last variable of a term. During unification with another term, such a variable binds to a list of arguments of that term beginning with argument corresponding to the variable till the rest of the term (whence the name of such variables). For instance, in the following term, ?R is a rest-variable:
p(?X,?Y | ?R)
If this term is unified
with p(?Z,f,?Z,q)
, then ?X binds
to ?Z
, ?Y
to f
, and ?R
to the list [?Z,q]
.
SWSL includes two fundamentally distinct knowledge representation languages:
In this section, we discuss how -- and also why -- to combine knowledge expressed in SWSL-Rules with knowledge expressed in SWSL-FOL.
First, it is worthwhile to review the motivations for having the two distinct knowledge representation languages.
SWSL-Rules is especially well suited to represent available knowledge and desired patterns of reasoning for several tasks in semantic Web services:
In particular, the capabilities of SWSL-Rules for logical nonmonotonicity (negation-as-failure and/or Courteous prioritized conflict handling) is used heavily in many use case scenarios for each of the above tasks and the associated kinds of knowledge.
SWSL-FOL is especially well suited to represent available knowledge and desired patterns of reasoning for several other tasks in semantic Web services, especially revolving around the process model:
In particular, the capabilities of SWSL-FOL for disjunction, reasoning by cases, contrapositive reasoning, and/or existentials are used heavily in many use case scenarios for each of the above tasks and its associated kinds of knowledge.
SWSL-Rules and SWSL-FOL overlap largely in syntax, and SWSL-Rules includes almost all of the connectives of SWSL-FOL. The deeper issue, however, is the semantic relationship between SWSL-Rules and SWSL-FOL.
For several purposes it is desirable to combine knowledge expressed in the SWSL-Rules form with knowledge expressed in the SWSL-FOL form. One important such purpose is:
For example, the predicates might be classes or properties defined via OWL-DL axioms, i.e., expressed in the Description Logic fragment of FOL.
In terms of semantics, it is desirable to have reasoning in SWSL-Rules respect as much as possible the information contained in such background FOL ontologies. In particular, it is desirable to enable sufficient completeness in the semantic combination to ensure that the conclusions drawn in SWSL-Rules will be (classically) not inconsistent with the SWSL-FOL ontologies.
Ideally, there would be one well-understood overall knowledge representation formalism that subsumes both SWSL-Rules and SWSL-FOL. This would provide the general theoretical basis for combining arbitrary SWSL-Rules knowledge with arbitrary SWSL-FOL knowledge. Unfortunately, finding such an umbrella formalism is still an open issue for basic research. Instead, the current scientific understanding provides only a limited theoretical basis for combining SWSL-Rules knowledge with SWSL-FOL knowledge. On the bright side, there are limited expressive cases for which it is well-understood theoretically how to do such combination.
The Venn diagram of relationships between the different formalisms, given in Figure 2.1 illustrates the most salient aspects of the current scientific understanding.
Figure 2.1: The relationships among different formalisms
The shield shape represents first-order logic-based formalisms. The (diagonally-rotated) bread-slice shape shows the expressivity of the logic programming based paradigms. These overlap partially -- in the Horn rules subset. FOL includes expressiveness beyond the overlap, notably: positive disjunctions; existentials; and entailment of non-ground and non-atomic conclusions. Likewise, LP includes expressiveness beyond the overlap, such as negation-as-failure, which is logically nonmonotonic. Description Logic (cf. OWL-DL), depicted as an oval shape, is a fragment of FOL.
Horn FOL is another fragment of FOL. Horn LP is a slight weakening of Horn FOL. "Weakening" here means that the conclusions from a given set of Horn premises that are entailed according to the Horn LP formalism are a subset of the conclusions entailed (from that same set of premises) according to the Horn FOL formalism. However, the set of ground atomic conclusions is the same in the Horn LP as in the Horn FOL. For most practical purposes (e.g., relational database query answering), Horn LP is thus essentially similar in its power to the Horn FOL.
Horn LP is a fragment of both FOL and nonmonotonic LP -- i.e., of both SWSL-Rules and SWSL-FOL. Horn LP is thus a limited "bridge" that provides a way to pass information -- either premises, or ground-atomic conclusions -- from FOL to LP, or vice versa. Knowledge from FOL that is in the Horn LP subset of expressiveness can be easily combined with general LP knowledge. Vice versa, knowledge from LP that is in the Horn LP subset of expressiveness can be easily combined with general FOL knowledge. Description Logic Programs (DLP) [Grosof2003a] represent a fragment of Horn LP. It likewise acts as a "bridge" between Description Logic (i.e., OWL-DL) and LP.
Note that, technically, LP uses a different logical connective for implication (":-" in SWSL syntax) than FOL uses. When we speak of Horn LP as a fragment of FOL, we are viewing this LP implication connective as mapped into the FOL implication connective (also known as material implication).
Horn LP as "bridge". To summarize, there is some initial good news about semantic combination:
Builtin predicates. Another case of well behaved semantic combination is for builtin predicates that are purely informational, e.g., that represent arithmetic comparisons or operations such as less-than or multiplication. Technically, in LP these can be viewed as procedural attachments. But alternatively, they can be viewed as predicates that have fixed extensions. Their semantics in both FOL and LP can thus be viewed essentially as virtual knowledge base consisting of a set of ground facts. This thus falls into the Horn LP fragment.
Hypermonotonic reasoning as "bridge". Recently, a new theoretical approach called hypermonotonic reasoning [Grosof2004c] has been developed to enable a case of "bridging" between (nonmon) LP and FOL that is considerably more expressive than Horn LP.
We will now describe in more detail some preliminary results about this hypermonotonic reasoning approach that bear upon the relationship of LP to FOL and thus upon how to combine LP knowledge with FOL knowledge.
Courteous LP (including its fragment: LP with negation-as-failure) can be viewed as a weakening of FOL, under a simple mapping of Courteous LP rules/conclusions into FOL. "Weakening" here means that for a given set of premises, the set of conclusions entailed in the Courteous LP formalism is in general a subset of the set of conclusions entailed by the FOL formalism. In other words:
This fundamental relationship between the formalisms provides an augmentation to the theoretical basis for combining knowledge in LP (i.e., SWSL-Rules) with knowledge in FOL.
Consider a set of rules S in LP and a set of formulas B in FOL. Let T be a translation mapping from the language of S to the language of B. S is said to be hypermonotonic with respect to B and T when S is sound but incomplete relative to B, under the mapping T. That is, when the conclusions entailed in S from a given set of premises P are in general always a subset of the conclusions entailed in B from the translated premises of S.
Define CLP2 to be the fragment of the Courteous LP formalism in which explicit negation-as-failure is omitted (i.e., prohibited). Each rule and mutex in CLP2 can be mapped quite straightforwardly and intuitively to a clause in FOL: simply replace the LP implication connective (":-" in SWSL-Rules syntax) by the FOL implication connective. Observe that this is the same mapping/translation that was considered in relating the Horn LP to FOL. Each ground-literal conclusion in CLP2 can also be mapped, in the same fashion, into a ground-literal in FOL.
The restriction on Courteous LP to avoid explicit negation-as-failure is not very onerous essentially, since the great majority of use cases in which explicit negation-as-failure is employed can be reformulated during manual authoring of rules so as to avoid it as a construct. More generally, the mapping can be extended, by complicating it a bit, to permit explicit negation-as-failure.
Going in the reverse direction, every clause in FOL can also be mapped into CLP2, in such a way that the resulting CLP information is a weakening of the FOL clause that nevertheless preserves much of the strength of the FOL clause. This reverse-translation mapping from FOL to CLP is complicated somewhat by the directional nature of the LP implication connective. "Directional" here means having a direction from body towards head. Each LP rule can be viewed as a directed clause. Consider a FOL clause C that consists of a disjunction of m literals:
Here, each Li is an atom or a classically-negated atom. When mapping c to CLP2, there are m possible choices of one for each possible choice of which literal is to be made head of the LP rule. Each possible choice corresponds to a different rule -- the LP rule in which literal Li is chosen as head has the form:
Li :- neg L1, ..., neg Li-1, neg Li+1, ..., neg Lm .
Altogether, the FOL clause C is mapped into a set of m LP rules:
L1 :- neg L2, neg L3, ..., neg Lm .
L2 :- neg L1, neg L3, ..., neg Lm .
Lm :- neg L1, neg L2, ..., neg Lm-1 .
where neg (neg A) is replaced equivalently by A. This set of rules is called the "omni-directional" set of rules for that clause -- or, more briefly, the "omni rules" for that clause.
In general, FOL axioms need not be clausal since they may include existential quantifiers. However, often skolemization can be performed to represent such existentials in a manner that preserves soundness (as is usual for skolemization). A refinement of the reverse translation mapping above is to exploit such skolemization in order to relax the requirement of clausal form. We use such skolemization particularly for head existentials.
Automatic weakened translation of FOL ontologies into SWSL-Rules. In the ontologies aspect of SWSL, it is desirable to have a "bridging" technique to automatically translate FOL ontologies into SWSL-Rules in such a manner as to preserve soundness (from an FOL viewpoint) but to be nevertheless fairly strong (i.e., capture much of the strength/content of the original FOL axioms). We have adopted, as an experimental "bridging" approach, the reverse translation mapping technique described above in order to map FOL ontologies into SWSL-Rules (heavily using the Courteous feature). In particular, we have applied this technique to map the axioms of PSL Core and Outer Core into SWSL-Rules so as to create a weakened version of that ontology that can be utilized within SWSL-Rules. Because some of these PSL axioms include existentials, we utilize the skolemization refinement described above, particularly for head existentials. The mapping from the PSL axioms to SWSL-Rules is given in appendix PSL in SWSL-FOL and SWSL-Rules.
The precise algorithm used to obtain the SWSL-Rules translation for a given axiom in SWSL-FOL is as follows:
Input: a formula F in SWSL-FOL. Output: a set of rules R, expressed in SWSL-Rules. 1) Translate F into formula F1 in Prenex Normal Form. 2) Skolemize F1 to get F2, which is in Skolem Normal Form. 3) Write F2 as a set S of clauses. 4) For each clause C in S, produce the omnidirectional set of rules for C (as defined above). R then is the union of all the omnidirectional sets of rules produced by (4).
SWSL is serialized in XML using RuleML. RuleML-style serialization of SWSL enables interoperation with other XML applications for rules and provides an encoding for transporting SWSL-Rules via the SOAP infrastructure of Web services.
RuleML integrates various rule paradigms via common set of concepts and defines a family of rule-based, Web-enabled sublanguages with various degrees of expressiveness. This section applies the RuleML approach to serialization of SWSL. This is done mostly by reusing and sometimes extending the existing RuleML sublanguages. In addition, a new sublanguage for the serialization of HiLog is developed.
Serialization of the presentation syntax of SWSL-Rules amounts to construction of explicit parse trees and then representing these trees linearly as XML markup that is compliant with XML Schema of the appropriate RuleML sublanguages. Starting with Version 0.89, the XML Schema specification of RuleML supports SWSL-Rules.
Serialization of SWSL-FOL does not require any new constructs, and it is done by repurposing existing RuleML features. Serialization of SWSL-FOL is discussed at the end of this section, in Section 4.5.
Conceptually, RuleML models XML trees as objects and thus divides all XML tags into class descriptors, called type tags, and property descriptors, called role tags. This conceptual object-oriented model implies that type tags and role tags must alternate, which is known as striped XML syntax. For instance, in F-logic and RDF, classes can have properties, which point to classes, which have properties that point to classes, etc. Similarly, in the striped XML syntax, a type tag has role tags as subelements, whose children are again type tags, etc. When the role of a subelement is clear from the context, its tag may be skipped for brevity, as in RDF's StripeSkipping.
HiLog terms. The HiLog serialization uses the
type tag Hterm
for HiLog terms, Con
for constants, and Var
for variables. Since HiLog allows
arbitrary terms to be used in the position of predicate and function
symbols, the RuleML serialization allows not only constants but also
variables and Hterms under the op
role tag . The
following illustrates the main aspects of the HiLog serialization.
Regular first-order terms. For instance, the HiLog
terms c
, f(a,?X)
, ?X
are
represented by the following three XML fragments, respectively:
<Con>c</Con>
<Hterm> <op><Con>f</Con></op> <Con>a</Con> <Var>X</Var> </Hterm>
<Var>X</Var>
Variables over function symbols. For
instance, the terms ?X(a,?Y)
,
?X(a,?Y(?X))
are serialized as follows:
<Hterm> <op><Var>X</Var></op> <Con>a</Con> <Var>Y</Var> </Hterm>
<Hterm> <op><Var>X</Var></op> <Con>a</Con> <Hterm> <op><Var>Y</Var></op> <Var>X</Var> </Hterm> </Hterm>
Parameterized function symbols. For
instance, the HiLog terms f(?X,a)(b,?X(c))
,
?Z(?X,a)(b,?X(?Y)(d))
, ?Z(f)(g,a)(p,?X)
will be serialized as shown below:
<Hterm> <op> <Hterm> <op><Con>f</Con></op> <Var>X</Var> <Con>a</Con> </Hterm> </op> <Con>b</Con> <Hterm> <op><Var>X</Var></op> <Con>c</Con> </Hterm> </Hterm>
<Hterm> <op> <Hterm> <op><Var>Z</Var></op> <Var>X</Var> <Con>a</Con> </Hterm> </op> <Con>b</Con> <Hterm> <op> <Hterm> <op><Var>X</Var></op> <Var>Y</Var> </Hterm> </op> <Con>d</Con> </Hterm> </Hterm>
<Hterm> <op> <Hterm> <op> <Hterm> <op><Var>Z</Var></op> <Con>f</Con> </Hterm> </op> <Con>g</Con> <Con>a</Con> </Hterm> </op> <Con>p</Con> <Var>X</Var> </Hterm>
HiLog atomic formulas. Since any HiLog term is also a HiLog atomic
formula, the RuleML serialization for these formulas is the same as for HiLog terms.
The following example shows an encoding of a query, which uses
the Query
element of RuleML:
?- q(?X) and ?X.
<Query> <And> <Hterm> <op><Con>q</Con></op> <Var>X</Var> </Hterm> <Var>X</Var> </And> <Query>
Another interesting example is a HiLog rule
call(?X) :- ?X.
which is a logical definition of the meta-predicate call/1
in
Prolog. This is translated using the RuleML
tags Implies, head, and body, as follows:
<Implies> <head> <Hterm> <op><Con>call</Con></op> <Var>X</Var> </Hterm> </head> <body> <Var>X</Var> </body> </Implies>
The explicit equality predicate :=:
is serialized using the
RuleML's element Equal
. For example,
f(a,?X):=:g(?Y,b) :- p(?X,?Y).
is serialized as
<Implies> <head> <Equal> <Hterm> <op><Con>f</Con></op> <Con>a</Con> <Var>X</Var> </Hterm> <Hterm> <op><Con>g</Con></op> <Var>Y</Var> <Con>b</Con> </Hterm> </Equal> </head> <body> <Hterm> <op><Con>p</Con></op> <Var>X</Var> <Var>Y</Var> </Hterm> </body> </Implies>
To serialize the Frames layer of SWSL-Rules we need to show the serialization of the various molecules and path expressions introduced by F-logic.
Molecules. The serialization of molecules uses slotted
atoms, which have an oid
but often do not have
an op
. The overall structure of F-logic molecules (except for
class membership and subclassing) is as follows:
Atom ::= oid op? slot*
Value molecules.
If t
, m
, v
are terms then the
value molecule t[m -> v]
is serialized as follows:
<Atom><oid>t'</oid><slot>m' v'</slot></Atom>
Here and elsewhere we use primes to represent recursive RuleML
serialization. For instance, t'
, m'
,
and v'
denote RuleML serializations
of t
, m
, and v
,
respectively. For instance, o[f(a,b) -> 3]
would be
represented by the following fragment:
<Atom> <oid><Con>o</Con></oid> <slot> <Hterm><Con>f</Con><Con>a</Con><Con>b</Con></Hterm> <Con>3</Con> </slot> </Atom>
The syntax t[m -> {v_{1},...,v_{k}}]
is also supported: the set-valued result is serialized using
the Set
tag:
<Atom> <oid>t'</oid> <slot> m' <Set>v_{1}',...,v_{k}'</Set> </slot> </Atom>
Boolean molecules. These molecules have the form
t[m]
where t
and m
are
terms. They are serialized using singleton slot
elements. For instance,
mary[female]
is represented as follows:
<Atom> <oid><Con>mary</Con></oid> <slot> <Con>female</Con> </slot> </Atom>
Class membership molecule. Class membership
molecules of the form
t:s
are serialized using the InstanceOf element:
<InstanceOf>t' s'</InstanceOf>
where t'
and s'
represent RuleML
serializations of t
and s
.
Subclass molecule. The subclass molecules of the
form t::s
are represented using
the SubclassOf
element as follows:
<SubclassOf>t' s'</SubclassOf>
As before, t'
and s'
represent RuleML
serializations of t
and s
, respectively.
Signature molecule. Signature molecules of the
form t[m => v]
are represented using
the Signature
element, where the prime represents
RuleML serialization of the corresponding term:
<Signature><oid>t'</oid><slot>m' v'</slot></Signature>
Boolean signature molecules. A Boolean signature
molecule has the form t[=>m]
. Its RuleML
serialization uses singleton slot
elements within a Signature
element:
<Signature><oid>t'</oid><slot>m'</slot></Signature>
Cardinality constraints. Signature molecules can have associated cardinality constraints. Such molecules have the form
t[s(t_{1},...,t_{n}) {min : max}
=> v]
In RuleML this becomes:
<Signature> <oid>t'</oid> <slot mincard="min" maxcard="max"> <Hterm><Con>s'</Con>t_{1}'...t_{n}'</Hterm> v' </slot> </Signature>
Nested molecules. Direct serialization of nested molecules is not currently supported. Instead, they must first be broken into conjunctions of non-nested molecules and then serialized.
Slot access and path expressions. Serialization of slot access
uses the RuleML Get
primitive. Serialization of path expressions
is supported via the polyadic RuleML SlotProd
element.
For example, room.ceiling.color
becomes the following:
<Get> <oid><Con>room</Con></oid> <SlotProd><Con>ceiling</Con><Con>color</Con></SlotProd> </Get>
SWSL-Rules supports reification of F-logic molecules, formulas that can
occur in the head or the body of a rule, and of the rules themselves. The
only restriction is that explicit quantifiers cannot occur under the
scope of the reification operator. The main idea behind RuleML
serialization of such statements is that the corresponding serialized
statement must be embedded within a Reify
element.
To illustrate, consider the following molecule:
a[b -> ${p(X[foo -> bar])}]
Since the reified statement (p(X[foo -> bar])
is an
Hterm
, this tag becomes the child of the Reify
element.
<Hterm> <oid><Con>a</Con></oid> <slot> <Con>b</Con> <Reify> <Hterm> <op><Con>p</Con></op> <Hterm> <oid><Var>X</Var></oid> <slot><Con>foo</Con><Con>bar</Con></slot> </Hterm> </Hterm> </Reify> </slot> </Hterm>
The following example illustrates serialization of a reified rule.
john[believes -> ${p(?X) implies q(?X)}].
The corresponding serialization is shown below.
Since the top-level tag of a rule is Implies
,
this tag becomes the child of the Reify
element.
<Hterm> <oid>john</oid> <slot> <Con>believes</Con> <Reify> <Implies> <body> <Hterm> <op><Con>p</Con></op> <Var>X</Var> </Hterm> </body> <head> <Hterm> <op><Con>q</Con></op> <Var>X</Var> </Hterm> </head> </Implies> </Reify> </slot> </Hterm>
Serialization of SWSL-FOL reuses the existing FOL RuleML sublanguage. The serialization is accomplished through the following rules:
<Atom>...</Atom>
.
What actually goes between the Atom tags depends on the type
of the atomic formula (i.e., whether it is a predicate formula or a
F-logic frame).
<And>
φ ψ </And>
,
<Or>
φ ψ </Or>
,
<Neg>
φ </Neg>
,
<Implies>
φ
ψ </Implies>
(SWSL-FOL's
φ impliedBy
ψ must be first replaced
with ψ implies
φ), and <Equivalent>
φ
ψ </Equivalent>
(for SWSL-FOL's iff
).
X
is a variable and φ is a serialized SWSL-FOL
formula, then the following are also SWSL-FOL serialized formulas:
<Exists>
<Var>X</Var>
φ </Exists>
and
<Forall>
<Var>X</Var>
φ </Forall>
. Serialized SWSL-FOL also allows to
combine quantifiers of the same sort and reduce repetitiveness. For
instance, <Exists>
<Var>X</Var>
<Var>Y</Var>
φ </Exists>
is a shorthand for
<Exists>
<Var>X</Var>
<Exists>
<Var>Y</Var>
φ </Exists>
</Exists>
.
occurrence_of
.)
The occurrences of an activity may impact
fluents (which provide an abstract
representation of the "real world").
In FLOWS, with each service there is an associated
activity (called the "service activity" of that service).
The service activity may specify aspects of the internal
process flow of the service, and also aspects of the
messaging interface of that service to other services.