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This is a position paper for the W3C Query Languages meeting in Boston, December 3-4th 1998.
The world wide web today is a network of hyperlinked resources. The content of these resources is in most part opaque to computers. Browsers display them and search engines locate occurances of words within them, but the level of "machine understanding" of the content, if any, is very limited. A search engine, for example, might know that a resource contained the textual string "
lion" but not that it was a representation of a lion, where lions are known to be members of the class of mammals. By enabling richer representation such as this, RDF makes it possible to express queries that go beyond simple text-matching.
This paper presents an overview of the query services that might be built on top of XML/RDF data. It does not present a specific proposal for an RDF query language; instead, it argues for a query language that is expressed in terms of the RDF logical data model rather than one particular concrete syntax
The content of images is typically very opaque to computers. Searching for images that contain particular kinds of scenes or items is usually done by searching for words which might occur on a page which refers to the image. This method is highly inaccurate. If the image were associated with a piece of RDF that clearly specified its content, significantly more precise retrieval would be possible. E.g., a photo of a lion could be annotated as depicting a lion. The following piece of RDF does this.
<!-- somewhere on the web.. some RDF statements about a picture --> <RDF xmlns:cx="http://www.wwc.org/cat.rdf" xmlns:P="http://www.images.org/image-desc-schema.rdf" xmlns:vocab="http://vocab.org/useful#" xmlns:rdf="http://www.w3.org/TR/rdf.rdf"> <P:Photograph rdf:about="http://www.imagelib.com/lion1.jpg"> <P:depicts> <cx:Lion> <vocab:color resource="http://vocab.org/useful#tan"/> <vocab:gender resource="http://vocab.org/useful#female"/> </cx:Lion> </P:depicts> <P:depicts rdf:resource="http://registries.org/people/Fred"/> </P:Photograph> <rdf:/RDF> <!-- a picture of a tan coloured female lion and a person identified only by URI --> <!-- somewhere else on the web... --> <rdf:RDF xmlns:vocab="http://vocab.org/useful#" xmlns:rdf="http://www.w3.org/TR/WD-rdf-syntax#"> <vocab:Person rdf:about="http://registries.org/people/Fred"> <vocab:gender rdf:resource="http://vocab.org/useful#male"/> <vocab:name> Fred </vocab:name> </vocab:Person> </rdf:RDF>see footnote for explanation of syntax
With this information, a search engine could do a very precise search for pictures of lions. Searching for the word "lion" on the other hand retrieves 784150 pages in Alta Vista, most of which are references to the Lions Club, Lion King, etc.
While this kind of simple matching based retrieval would be useful, it does not even come close to exploiting the full potential of having machine understandable content (MUC).
In order to fully exploit this, we need to be able to build inferential services on top of this MUC. Such a service would combine the "raw" MUC with a set of axioms/rules, enabling machines to infer knowledge that is implicit in the MUC.
Imagine an appropriately captioned photograph of
a child's birthday party. Now consider someone searching
for an image with decorations. Typically, children's birthday
parties have decorations. However, since the caption does
not explicitly state that the image scene contains decorations,
a simple matching based algorithm will not find this image.
An inferential service could draw on a rich set of rules about the world (including events like birthday parties) to infer that the photograph probably includes some decorations, thereby improving the retrieval.
The RDF Schema specification language provides facilities for machine-readable vocabularies to be specified using a hierarchical type system. This allows a resource to be described as member of some specific class (eg. 'Snow Leopard') and have it's membership of more general classes (eg. 'Big Cat', 'Mammal') implied by the RDF type system. This makes it feasible to express searches for resources using general categories, and have the results include resources whose membership of those broad categories is inferred from their membership of some more detailed sub-category.
Inferencing engines always work on a "logical model". The logical model is an (typically set-theoretic) abstraction.
The logical model is by definition an abstract entity. Logical models are typically grounded in one or more concrete syntaxes (aka physical models).
W3C logical models are based on RDF and syntax models are based on XML.
The distinction between the Logical Model vs the Syntax Model has evolved over decades of work in math and computer science and is found wherever representation of information is involved.
Any particular concrete manipulation is always on a physical model. Therefore, it is often tempting to either confuse the two or try make do with just the physical model. However, there are several reasons why complex applications such as inferencing engines are based on the logical model.
For example, if the logical model of a knowledge is a directed labelled graph (as with RDF), the aggregation of multiple knowledge bases can be defined cleanly as graph superposition at the logical level, even though it would be hard to define the concept of "aggregation" two XML files.
Given the importance of the logical model, it is clear that we need
query languages not just for XML but also for RDF.
This position paper suggests a general outline for an RDF querying system. RDF's simple yet powerful data model allows for an equally simple yet powerful query language. The query language is based on a single query mechanism : subgraph matching.
Every query is against an RDF knowledge base (KB), which in turn could be an aggregation of two or more RDF knowledge bases. Every RDF/XML block (i.e., the RDF within a <RDF> ...</RDF>) can be thought of as a serialised RDF knowledge base.
The query is itself simply an RDF model (i.e., a directed labelled graph), some of whose resources and properties may represent variables. There are two outputs to every query,
Here are a couple of salient points about the query language outlined above.
RDF Schema constructs such as subClassOf and subPropertyOf allow some simple inferences. In future, more complex rules will be expressible and more powerful inference engines will become possible. Ideally, the query language used by an inferencing system to access the knowledge base should be the same the query language the inferencing system responds to.
To enable this, a query can take an additional parameter which specifies whether its answer should be based on either the "raw RDF graph" or on the deductive closure of the knowledge base.
Note: The syntax of these queries could easily be represented in RDF/XML syntax. For the purposes of this paper we use a simple syntax in which '$x' and '$y' represent variables and properties are shown using namespace prefixes.
<rdf:RDF xmlns:P = "http://www.images.org/image-desc-schema.rdf#" xmlns:rdf = "http://www.w3.org/TR/WD-rdf-syntax#"> <cx:Lion/> </rdf:RDF> and (($y . [anonymous-resource]))
<rdf:RDF xmlns:cx = "http://www.wwc.org/cat.rdf" xmlns:P = "http://www.images.org/image-desc-schema.rdf#" xmlns:rdf = "http://www.w3.org/TR/WD-rdf-syntax#"> <P:Photograph rdf:about="http://www.imagelib.com/lion1.jpg"> <P:depicts> <cx:Lion> <vocab:color resource="http://vocab.org/useful#tan"/> <vocab:gender resource="http://vocab.org/useful#female"/> </cx:Lion> </P:depicts> <P:depicts> <cx:Person rdf:about="http://registries.org/people/Fred" > <vocab:gender rdf:resource="http://vocab.org/#male"/> </cx:Person> </P:depicts> </P:Photograph> </rdf:RDF> <!-- note that the sub-graph returned here includes information from two sources; statements about the photograph and about Fred when taken together tell us that this is a photograph of a male -->and
(($x . [http://www.imagelib.com/lion1.jpg])($y . [http://registries.org/people/Fred]))
The following is a human-readable interpretation of the RDF used in the example...
The first block of RDF uses four vocabularies to state that there is a resource (http://www.imagelib.com/lion1.jpg) which is a member of the class 'Photograph' and which depicts an object that is an member of the class 'Lion' and which in turn has a color property with value 'tan', and a gender property with value 'female'. The photograph also depicts a second object identified only by URI (http://registries.org/people/Fred). A second source of information provides further RDF statements about [http://registries.org/people/Fred]. In this case, we learn a name ("Fred") and that Fred is male.