Slides of the tutorial given in Delhi, India, at the International Conference & Workshop on Web Technologies, on the 11^th of November, 2005.

These slides are online at: http://www.w3.org/2005/Talks/1111-Delhi-IH/ with active links. A printable version (in PDF) is also available.

Towards a Semantic Web

• “The future of the Web is a universal medium for the exchange of data”
• “The future of the Web is a universal medium for the

  exchange of data”

• But… the current Web represents information using
  • natural language (English, Hungarian, Hindi,…)
  • graphics, multimedia, page layout
• This is okay for humans; difficult for machines!

Example: Searching

• The best-known example…
  • Google et al. are great, but there are too many false hits
  • adding descriptions to resources should improve this
• However: the Semantic Web is not only about searching!

Example: Data(base) Integration

• Databases are very different in structure, in content
• Lots of applications require managing several databases
  • after company mergers
  • combination of administrative data for e-Government
  • biochemical, genetic, pharmaceutical research
  • etc.
• Most of these data are now on the Web
• The semantics of the data(bases) should be known (how this semantics is mapped on internal structures is immaterial)

Example: Digital Libraries

• It is a bit like the search example
• It means catalogs on the Web
  • librarians have known how to do that for centuries
  • goal is to have this on the Web, World-wide
  • extend it to multimedia data, too
• But it is more: software agents should also be librarians!
  • help you in finding the right publications

Example: Semantics of Web Services

• Web services technology is great
• But if services are ubiquitous, searching issue comes up, for example:
  • “find me the most elegant Schrödinger equation solver”
  • but what does it mean to be
    • “elegant”?
    • “most elegant”?
  • mathematicians ask these questions all the time…
• It is necessary to characterize the service
  • not only in terms of input and output parameters…
  • …but also in terms of its semantics

What Is Needed (Technically)?

• To make metadata machine processable, we need:
  • unambiguous names for resources (URIs)
  • a common data model for expressing metadata (RDF)
    • and ways to access the metadata on the Web
  • common vocabularies (Ontologies)
• The “Semantic Web” is a metadata based infrastructure for reasoning on the Web
• It extends the current Web (and does not replace it)

Basic Metadata Architecture: RDF

A Problem Example for the Tutorial

• Convey the meaning of a figure through text
• (important for accessibility)
  • add metadata to the image describing the content
  • let a tool produce some simple output using the metadata
  • use a standard metadata formalism

Statements

• The metadata is a set of statements
• In a graphical example:
  • “the type of the full slide is a chart, and the chart type is «line»”
  • “the chart is labeled with an (SVG) text element”
  • “the legend is also a hyperlink”
  • “the target of the hyperlink is «URI»”
  • “the full slide consists of the legend, axes, and data lines”
  • “the data lines describe full and affiliate members, all members”
• The statements are about resources:
  • SVG elements, general URI-s, …

Resource Description Framework

• Statements can be modeled (mathematically) with:
  • Resources: an element, a URI, a literal, …
  • Properties: directed relations between two resources
  • Statements: “triples” of two resources bound by a property
    • usual terminology: (s,p,o) for subject, properties, object
    • you can also think about a property/value pair attached to a resource
• RDF is a general model for such statements
  • … with machine readable formats (e.g., RDF/XML, Turtle, n3, RXR, …)
  • RDF/XML is the “official” W3C format

RDF is a Graph

• An (s,p,o) triple can be viewed as a labeled edge in a graph
  • i.e., a set of RDF statements is a directed, labeled graph
    • both “objects” and “subjects” are the graph nodes
    • “properties” are the edges
• One should “think” in terms of graphs; XML or Turtle syntax are only the tools for practical usage!
• RDF authoring tools usually work with graphs, too (XML or Turtle is done “behind the scenes”)

A Simple RDF Example

URI-s Play a Fundamental Role

• One can uniquely identify all resources on the web
• Uniqueness is vital to make consistent statements
• Anybody can create metadata on any resource on the Web
  • e.g., the same SVG file could be annotated through other terms
• URI-s ground RDF into the Web
  • e.g., information can be retrieved using existing tools
  • this makes the “Semantic Web”, well… “Semantic Web”

URI-s: Merging

• It becomes easy to merge metadata
  • e.g., applications may merge the SVG annotations
• Merge can be done because statements refer to the same URI-s
  • nodes with identical URI-s are considered identical
• Merging is a very powerful feature of RDF
  • metadata may be defined by several (independent) parties…
  • …and combined by an application
  • one of the areas where RDF is much handier than pure XML

What Merge Can Do...

RDF/XML Principles

• Encode nodes and edges as XML elements or with literals:

«Element for #FullSlide»

  «Element for labelledBy»

      «Element for #BottomLegend»

  «/Element for labelledBy»

«/Element for #FullSlide»

«Element for #FullSlide»

  «Element for graphicsType»

      Chart

  «/Element for graphicsType»

«/Element for #FullSlide»

RDF/XML Principles (cont)

• Encode the resources (i.e., the nodes):

<rdf:RDF

  xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">

    <rdf:Description rdf:about="#FullSlide">

       «Element for labelledBy»

           <rdf:Description rdf:about="#BottomLegend"/>

       «/Element for labelledBy»

    </rdf:Description>

<rdf:RDF>

• Note the usage of namespaces!

RDF/XML Principles (cont)

• Encode the property (i.e., edge) in its own namespace:

<rdf:RDF

  xmlns:axsvg="http://svg.example.org#"

  xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">

    <rdf:Description rdf:about="#FullSlide">

       <axsvg:labelledBy>

           <rdf:Description rdf:about="#BottomLegend"/>

       </axsvg:labelledBy>

    </rdf:Description>

<rdf:RDF>

• (To save space, we will omit namespace declarations…)

RDF/XML Syntax Simplifications

• The RDF/XML Syntax introduces a number of “simplifications”:
  • e.g., “merging” several properties into the same rdf:Description element
  • descriptions can be nested
  • and a lot more!

       <rdf:Description rdf:about="#FullSlide">

          <axsvg:LabelledBy>

              <rdf:Description rdf:about="#BottomLegend">

                 <axsvg:IsAnchor>True</axsvg:IsAnchor>

              </rdf:Description>

          </axsvg:LabelledBy>

          <axsvg:GraphicsType>

              Chart

          </axsvg:GraphicsType>

       </rdf:Description>

RDF Vocabulary Description Language (RDFS)

(a.k.a. RDFS)

Need for RDF Schemas

• Adding metadata and using it from a program works… provided the program knows what terms to use!
• We used terms like:
  • Chart, labelledBy, isAnchor, ...
  • chartType, graphicsType, ...
  • etc
• Are they all known? Are they all correct? Are there (logical) relationships among the terms?
• This is where RDF Schemas come in
  • officially: “RDF Vocabulary Description Language”; the term “Schema” is retained for historical reasons…

Classes, Resources, …

• Think of well known in traditional ontologies:
  • use the term “snake”, “cobra”
  • “every cobra is a snake”
  • “Nag and Nagaina are cobras” (at least in the Jungle Book)
  • etc.
• RDFS defines resources and classes:
  • everything in RDF is a “resource”
  • “classes” are also resources, but…
  • they are also a collection of possible resources (i.e., “individuals”)
    • “snake”, “cobra”, …

Classes, Resources, … (cont.)

• Relationships are defined among classes/resources:
  • “typing”: an individual belongs to a specific class (“Nag is a cobra”)
  • “subclassing”: instance of one is also the instance of the other (“every cobra is a snake”)
• RDFS formalizes these notions for and in RDF

Classes, Resources in RDF(S)

• RDFS defines rdfs:Resource, rdfs:Class as nodes; rdf:type, rdfs:subClassOf as properties
• User should create RDF Schema file for the user types (note: RDFS is also RDF!)

Schema Example in RDF/XML

• In axsvg-schema.rdf (“application’s data types”):

<rdf:Description rdf:ID="SVGEntity">

  <rdf:type

    rdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/>

</rdf:Description>

• In the rdf data on a specific graphics (“using the type”):

<rdf:Description rdf:about="#Datalines">

   <rdf:type rdf:resource="axsvg-schema.rdf#SVGEntity"/>

</rdf:Description>

• In traditional knowledge representation this separation is often referred to as:
  • “Terminological axioms”, “Terminological box”, “TBox”
  • “Assertions”, “Assertion box”, “ABox”

Inferred Properties

• • (#AnimatedLines rdf:type #SVGEntity)
• is not in the original RDF data…
• …but can be inferred from the RDFS rules
• Better RDF environments will return that triplet, too

Inference: Let Us Be Formal…

• The RDF Semantics document has a list of (44) entailment rules:
  • “if such and such triplets are in the graph, add this and this triplet”
  • do that recursively
  • this can be done in polynomial time for a specific graph
• The relevant rule for our example:

If:

  uuu rdfs:subClassOf xxx .

  vvv rdf:type uuu .

Then add:

  vvv rdf:type xxx .

• Whether those extra triplets are physically added to the graph, or deduced when needed is an implementation issue

Properties (Predicates)

• Property is a special class (rdf:Property)
  • i.e., properties are also resources
• Properties are constrained by their range and domain
  • i.e., what individuals can be on the “left” or on the “right”
• There is also a possibility for a “sub-property”
  • all resources bound by the “sub” are also bound by the other

Property Specification Example

• Note that one cannot define what literals can be used
• This requires ontologies (see later)

Literals in RDF (and RDF/XML)

• Literals may have a data type (floats, int, etc; all types defined in XML Schemas)

  <rdf:Description rdf:about="#Datalines">

     <axsvg:IsAnchor

      rdf:datatype="http://www.w3.org/2001/XMLSchema#boolean">

           false

     </axsvg:IsAnchor>

  </rdf:Description>

• XML Literals:

  <rdf:Description rdf:about="#Datalines">

     <axsvg:SVGContent rdf:parseType="Literal"

        xmlns:svg="http:....">

            <svg:svg ...>

                <svg:line x1="..."/>

                <svg:path d="..."/>

                 ...

            </svg:svg>

     </axsvg:SVGContent>

  </rdf:Description>

Querying RDF Data: SPARQL

How to retrieve triples?

• In simple cases: a programming environment gives direct access. E.g.:

triples = graph.triples((Resource,Property,None))

• to retrieve all objects for (Resource,Property)
• This is programming language dependent
• It becomes too complex for complex data
• Does not work if the data are remote
• We need query facilities

Querying RDF Graphs

• The fundamental idea: use graph patterns to define subgraphs:
  • a pattern contains unbound symbols
  • by binding the symbols, subgraphs of the RDF graph may be matched
  • if there is such a match, the query returns the bound resources or a subgraph
• This is the how SPARQL (Query Language for RDF) is defined
  • is programming language-independent query language
  • is in a last call working draft phase (Recommendation in 2006?)

Simple SPARQL Example

SELECT ?cat ?val

WHERE { ?x rdf:value ?val. ?x category ?cat }

• Returns: [["Total Members",100],["Total Members",200],…,["Full Members",10],…]
• Note the role of ?x: it helps defining the pattern, but is not returned

Other SPARQL Features

• Define optional patterns
• Limit the number of returned results; remove duplicates, sort them,…
• Add functional constraints to pattern matching
• Return a full subgraph (instead of a list of bound variables)
• Use datatypes and/or language tags when matching a pattern
• SPARQL is in last call Working Draft, i.e., the technical aspects are now fixed (modulo further community comments and/or implementation problems)
  • but there are a number of implementations already!

SPARQL Usage in Practice

• Locally, i.e., bound to a programming environment like RDFLib or Jena
  • details are language dependent
• Remotely, e.g., over the network or into a database
  • very important usage: a growing number of RDF depositories…
  • separate documents define the protocol and the result format
    • SPARQL Protocol for RDF
    • SPARQL Results XML Format
  • return is in XML: can be fed, e.g., into XSLT for direct display
• An application pattern evolves: use (XHTML) forms to create a SPARQL Query to a database and display the result in XHTML
  • there are a number of application experiments, demos, etc., already; see, e.g., the W3C’s Talk database

Ontologies (OWL)

Ontologies

• RDFS is useful, but does not solve all the issues
• Complex applications may want more possibilities:
  • can a program reason about some terms? E.g.:
    • “if «A» is left of «B» and «B» is left of «C», is «A» left of «C»?”
    • obviously true for humans, not obvious for a program …
    • … programs should be able to deduce such statements
  • if somebody else defines a set of terms: are they the same?
    • obvious issue in an international context
    • necessary for complex merging
  • construct classes, not just name them
  • restrict a property range when used for a specific class
  • disjointness or equivalence of classes
  • etc.

W3C’s Ontology Language (OWL)

• A layer on top of RDFS with additional possibilities
• Outcome of various projects:
  1. SHOE project: an early attempt to add semantics to HTML
  2. DAML-ONT (a DARPA project) and OIL (an EU project)
  3. an attempt to merge the two: DAML+OIL
  4. the latter was submitted to W3C
  5. lots of coordination with the core RDF work
  6. recommendation since early 2004

Classes in OWL

• In RDFS, you can subclass existing classes… that’s all
• In OWL, you can construct classes from existing ones:
  • enumerate its content
  • through intersection, union, complement
  • through property restrictions
• To do so, OWL introduces its own Class and Thing to differentiate the classes from individuals

Example: (OWL) Enumeration

• The OWL solution, where possible content is explicitly listed:

Union of Classes

• Essentially, a set-theoretical union:

• One can also define the intersection of classes

Property Restrictions

• (Sub)classes created by restricting the property value on that class
• For example, “an Egyptian cobra (naja haje) is a cobra living in the Sahara or Syria” means:
  • restrict the value of “living in” when applied to “cobra”…
  • …thereby define the class of “Egyptian cobra”

Property Restrictions in OWL

• Restriction may be by:
  • value constraints (i.e., further restrictions on the range)
    • all values must be from a class
    • at least one value must be from a class
  • cardinality constraints
  • (i.e., how many times the property can be used on an instance?)
    • minimum cardinality
    • maximum cardinality
    • exact cardinality

Property Restriction Example

• “An Egyptian Cobra is a Cobra living in the Sahara or in the Syrian Desert”:

Property Characterization

• In RDFS, properties are constrained by domain and range
• In OWL, one can also characterize their behavior (symmetric, transitive, …)
• OWL separates data properties
  • “datatype property” means that its range are typed literals

Characterization Example

• “There should be only one chart type”

OWL: Additional Features

• Ontologies may be extremely a large:
  • their management requires special care
  • they may consist of several modules
  • come from different places and must be integrated
• Ontologies are on the Web. That means
  • applications may use several, different ontologies, or…
  • … same ontologies but in different languages
  • equivalence of, and relations among terms become an issue
• OWL includes possibilites for class/property equivalence, version and deprecation control, etc.

Example: Connecting to Hungarian

However: Ontologies are Hard!

• Hard to implement a full ontology management system
  • may be superfluous for some applications
• Hence the “onion” model of increasingly complex specs:
  • no property expressions or datatypes in RDF Schemas
  • not all set operators, restricted cardinality in OWL Lite
  • some restrictions, but a computational guarantee in OWL DL
  • full expressive power in OWL Full (but no computational guarantee)

Ontologies are Hard! (cont)

• “Lite” < “DL” < “Full”, but not completely true for RDFS
  • RDFS is “almost” a subcategory
  • not all RDFS statements are valid in DL…
  • …but they are for Full
• Applications may take what they really need!

RDF/OWL in Practice

RDF in Programming Practice

• Programming environments usually provide:
  • creation of a “Graph”, “Triple Store”, or “Model” (the terms used are different in various implementations)
  • the RDF file(s) are parsed and results stored in the Graph
  • the Graph offers methods to retrieve and/or remove triples, resources, etc
  • “merge” is automatic if several files are parsed
  • the rest is conventional programming…

Example: Jena

• RDF toolkit in Java from HP’s Bristol lab

  // create a model (a.k.a. Triple Store)

  Model model = new ModelMem();

  Resource subject = model.createResource("URI_of_Subject")

  // 'in' refers to the input file

  model.read(new InputStreamReader(in));

  StmtIterator iter = model.listStatements(subject,null,null);

  while(iter.hasNext()) { 

     st = iter.next();

     p = st.getProperty();

     o = st.getObject();

     do_something(p,o);

  }

Jena (cont)

• But Jena is much more than just the basics; it has
  • a huge number of classes/methods
    • listing, removing associated properties, objects, comparing full RDF graphs, manage typed literals, etc.
  • an “RDFS Reasoner”
    • all the “inferred” properties, types are accessible and errors are checked
  • a SPARQL implementation
  • an OWL reasoner
  • a layer (Joseki) for remote access of triples (essentially, and triple database)
• Probably the most widely used RDF environment in Java today

Lots of Other tools

• There are lots of other tools:
  • RDF frameworks for specific languages: RDFStore (Perl), RAP (PHP), SWI-Prolog (Prolog), …
  • Redland: general RDF Framework, with bindings to C, C++, C#, Python, …, and with SPARQL facilities (Rasqal)
  • RDF storage systems:
    • Sesame, Kowari, Tucana, Gateway, @Semantics RDFStore, 3Store, Jena’s Joseki, InferEd, Oracle Database 10g , …
    • some of these are based on an internal sql engine (3Store, Oracle), others are made bottom up as triple stores
    • most of them have or plan for SPARQL facilities
    • some are part of a larger system (InferEd, @Semantics), others are “simply” database systems
• See, for example, the tool list at W3C or the Free University of Berlin list
• It has become easy to quickly prototype an RDF based application!

Current/future Developments

Simple Knowledge Organisation System (SKOS)

• Goal: porting (“Webifying”) thesauri: representing and sharing classifications, glossaries, thesauri, etc, as developed in the “Print World”. For example:
  • Dewey Decimal Classification, Art and Architecture Thesaurus, ACM classification of keywords and terms…
  • DMOZ categories (a.k.a. Open Directory Project)
• The system must be simple to allow for a quick port of traditional data (done by “traditional” people…)
• This is where SKOS comes in

Example: Entries in a Glossary (1)

“Assertion”

“(i) Any expression which is claimed to be true. (ii) The act of claiming something to be true.”

“Class”

“A general concept, category or classification. Something used primarily to classify or categorize other things.”

“Resource”

“(i) An entity; anything in the universe. (ii) As a class name: the class of everything; the most inclusive category possible.”

(from the RDF Semantics Glossary)

Example: Entries in a Glossary (2)

Example: Entries in a Glossary (3)

Example: Taxonomy (1)

Illustrates “broader” and “narrower”

General

• Travelling
• Politics

SemWeb

• RDF
  • OWL

(From MortenF’s weblog categories. Note that the categorization is arbitrary!)

Example: Taxonomy (2)

Example: Thesaurus (1)

Term

Economic cooperation

Used For

Economic co-operation

Broader terms

Economic policy

Narrower terms

Economic integration, European economic cooperation, European industrial cooperation, Industrial cooperation

Related terms

Interdependence

Scope Note

Includes cooperative measures in banking, trade, industry etc., between and among countries

(from UK Archival Thesaurus)

Example: Thesaurus (2)

SKOS Core Overview

• Basic description (Concept, ConceptScheme, inScheme, hasTopConcept)
• Labelling (prefLabel, altLabel, prefSymbol, altSymbol …)
• Documentation (definition, scopeNote, changeNote, historyNote, editorialNote, publicNote, privateNote)
• Semantic relations (broader, narrower, related)
• Subject indexing (subject, isSubjectOf, primarySubject, isPrimarySubjectOf)
• Grouping (Collection, OrderedCollection, CollectableProperty, member, memberList)

Complementarity of SKOS and OWL (Why Having SKOS?)

• OWL’s precision not always necessary or even appropriate
  • “OWL a sledge hammer / SKOS a nutcracker”, or “OWL a Harley / SKOS a bike”
  • complement each other, can be used in combination to optimize cost/benefit
• Role of SKOS is
  • to bring the worlds of library classification and Web technology together
  • to be simple and undemanding enough in terms of cost and required expertise
• SKOS should be finalized in 2006

“Core” Vocabularies

• A number of public “core” vocabularies evolve to be used by applications, e.g.:
  • SKOS Core: about knowledge systems
  • Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management for, e.g., books, mainstream journal content by PRISM
  • FOAF: about people and their organizations
  • DOAP: on the descriptions of (mainly open source) software projects
  • MusicBrainz: on the description of CDs, music tracks, …
  • …
• They share the underlying RDF model (provides mechanisms for extensibillity, sharing, …)

Rules

• OWL can be used for simple inferences
• Applications may want to express domain-specific knowledge, like “Horn clauses”:
  • (prem-1 ∧ prem-2 ∧ …) ⇒ concl
  • e.g.: for any «X», «Y» and «Z»: “if «Y» is a parent of «X», and «Z» is a brother of «Y» then «Z» is the uncle of «X»”
• But it gets more complicated; there is a large corpus of rule based systems and languages (mostly vendor specific), though not necessarily bound to the Web (yet)
• Several attempts already to combine Semantic Web with Rules (Metalog, RuleML, SWRL, WRL, cwm, …)
  • note: cwm, for example, defines Horn predicates in terms of graph patterns, a connection to SPARQL…

W3C’s Rules Workshop

• W3C held a Workshop in April 2005; lots of issues and user cases were identified
• Some interesting scenarios have already been identified:
  • exchange mail filtering rules among clients
  • rules to search into data (databases, RDF stores)
  • rules to analyse medical, banking, or other data data (merging rules of different origins)
• interest from financial services, business rules, life science community,…

Rules Interchange Format Working Group

• The W3C Working Group started at the beginning of November 2005
• Work is planned in two “phases”:
  1. construct an extensible format for rule interchange
  2. define more complex extensions

RIF Phase 1 Goals

• An interchange format to exchange rules among rule engines and systems
  • probably based on “full Horn Logic” with some simple datatypes (int, boolean, strings, …)
  • make it relatively simple, leave the more complex issues to Phase 2
• An extensible format to allow more complex alternatives to be defined
  • e.g., fuzzy and/or temporal logic
• Recommendation in May 2007

RIF Phase 2 Goals

• Define more complex extensions
  • towards First Order Logic (FOL), Logic Programming systems…
  • syntactic extensions to Horn logic like Lloyd-Topor (i.e., making the rule bodies and head richer), HiLog (i.e., some form of higher order syntax)
• First recommendation(s) in May 2008

Lots of Theoretical Questions to Solve

• Open vs. Closed Worlds, monotonicity vs. non-monotonicity
• How to use various logic systems (Description Logic, F-Logic, Horn,…)
  • there is need for a coherent structure
• Relationship to RDFS and OWL: “One Tower” vs. “Two Towers” model:

Beyond Rules: Trust

• Can I trust a metadata on the Web?
  • is the author the one who claims he/she is, can I check his/her credentials?
  • can I trust the inference engine?
  • etc.
• Some of the building blocks are available (e.g., Signature/Encryption) but some are missing:
  • how to “name” a full graph
  • a “canonical” form of triplets (necessary for unambiguous signatures or to compare graphs)
  • how to “express” trust? (e.g., trust in context)
  • protocols to check, for example, a signature
  • …
• It is on the “future” stack of W3C and the SW Community …

A Number of Other Issues…

• Lot of R&D is going on:
  • improve the inference algorithms and implementations
  • improve scalability, reasoning with OWL Full
  • temporal & spatial reasoning, fuzzy logic
  • better modularization (import or refer to part of ontologies)
  • procedural attachments (e.g., in rules)
  • exact relationships between Description and other types logics (and their usage on the Web)
  • ontology management on the Web
  • …
• This mostly happens outside of W3C, though
  • W3C is not a research entity…

Available Documents

Some Books

• J. Davies, D. Fensel, F. van Harmelen: Towards the Semantic Web (2002)
• S. Powers: Practical RDF (2003)
• D. Fensel, J. Hendler: Spinning the Semantic Web (2003)
• F. Baader, D. Calvanese, D. McGuinness, D. Nardi, P. Patel-Schneider: The Description Logic Handbook (2003)
• G. Antoniu, F. van Harmelen: Semantic Web Primer (2004)
• A. Gómez-Pérez, M. Fernández-López, O. Corcho: Ontological Engineering (2004)
• …

Further Information

• Dave Beckett’s Resources at Bristol University
  • huge list of documents, publications, tools, …
• Semantic Web Community Portals, e.g.:
  • Semanticweb.org
  • “Business model IG” (part of semanticweb.org)
  • list documents, software, host project pages, etc,…

SWBP Working Group Documents

• Documents for ontology engineering
  • “Managing a Vocabulary for the Semantic Web”
  • “Defining N-ary relations”
  • “Representing Classes As Property Values”
  • “Representing "value partitions" and "value sets"”
  • “XML Schema Datatypes in RDF and OWL”
• SKOS “Quick Guide”, “Core Guide”, and “Vocabulary Specification”
• Semantic Web Tutorials (list of references)
• Metadata modules in XHTML2 (jointly with the HTML WG)
• Links with OMG’s “Ontology Definition Metamodel”
• “Ontology Driven Architectures in Software Engineering”

Further Information (cont)

• Full, interactive view of the RDFS and OWL definitions
  • requires an SVG client
• Description Logic links:
  • online course by Enrico Franconi,
  • teaching material and links by Ian Horrocks
• “Ontology Development 101”
• OWL Reasoning Examples
• Lots of papers at WWW2003, WWW2004, and WWW2005

Public Fora at W3C

Semantic Web Interest Group

a forum for discussions on applications

RDF Logic

public (archived) mailing list for technical discussions

Some Application Examples

SW Applications

• Large number of applications emerge:
  • first applications were RDF only…
  • …but recent ones use ontologies, too
    • huge number of ontologies exist already, with proprietary formats
    • converting them to RDF/OWL is a significant task (but there are converters)
• Most applications are still “centralized”, not many decentralized applications yet
• For further examples, see, for example, the SW Technology Conference
  • not a scientific conference, but commercial people making real money!

Data integration

• Semantic integration of corporate resources or different databases
• RDF/RDFS/OWL based vocabularies as an “interlingua” among system components (early experimentation at Boeing, see, e.g., a WWW11 paper)
• Similar approaches: Sculpteur project, MITRE Corp., MuseoSuomi, …
• There are companies specializing in the area

Portals

• Vodaphone's Live Mobile Portal
  • search application (e.g. ringtone, game, picture) using RDF
    • better search: page views per download decreased 50%
    • increased revenue: ringtone up 20% in 2 months
  • RDF was key factor in making this possible
• Sun’s SwordFish
  • Public queries for support, handbooks, etc, go through an internal RDF engine:
    • White Papers collection, System Handbooks collection
  • the queries are answered via an internal RDF database
• Nokia has a somewhat similar support portal

Improved Search via Ontology: GoPubMed

• Improved search on top of pubmed.org (the “google” for life science articles)
• Search results are ranked using the specialized ontologies (e.g., Gene Ontology)
• Extra search terms are generated (as an extra menu) and terms are highlighted in the abstracts
• Shows the importance of domain specific ontologies for search improvement

Adobe's XMP

• Adobe’s tool to add RDF-based metadata to most of their file formats
  • used for more effective organization
  • supported in Adobe Creative Suite
  • support from 30+ major asset management vendors
• The tool is available for all!

Further Information

These slides are at:

http://www.w3.org/2005/Talks/1111-Delhi-IH/

Semantic Web homepage

http://www.w3.org//2001/sw/

More information about W3C:

http://www.w3.org/Consortium/

Contact information:

http://www.w3.org/Consortium/Contact

Mail me:

ivan@w3.org