The Ontology Inference Layer OIL is the result of a collaboration between a number of academic and commercial institutions. OIL is a proposal for a web-based representation and inference layer for ontologies, which combines the widely used modelling primitives from frame-based languages with the formal semantics and reasoning services provided by description logics.
Ontologies have become an increasingly important research topic. This is a result both of their usefulness in a range of application domains, and of the pivotal role that they are set to play in the development of the Semantic Web
The Semantic Web vision, as articulated by Tim Berners-Lee, is of a Web in which resources are accessible not only to humans, but also to automated processes, e.g. automated agents which roam the web performing useful tasks such as improved search (in terms of precision) and resource discovery, information brokering and information filtering. The automation of tasks depends on elevating the status of the web from machine-readable to something we might call machine-understandable. The key idea is to have data on the web defined and linked in such a way that its meaning is explicitly interpretable by software processes rather than just being implicitly interpretable by humans.
To realise this vision, it will be necessary to annotate web resources with metadata (i.e., data describing their content/functionality). Standardisation proposals for annotation languages have already been submitted to the World Wide Web Consortium (W3C), in particular RDF (Resource Description Framework) and RDF Schema. However, such annotations will be of limited value to automated processes unless they share a common understanding as to their meaning. Ontologies, can help to meet this requirement by providing a representation of a shared conceptualisation of a particular domain that can be communicated across people and applications.
RDF Schema (RDFS) itself is already recognisable as an ontology/knowledge representation language: it talks about classes and properties (binary relations), range and domain constraints (on properties), and subclass and subproperty (subsumption) relations. However, RDFS is a relatively primitive language (the above is an almost complete description of its functionality), and more expressive power would clearly be necessary/desirable in order to describe resources in sufficient detail. Moreover, such descriptions should be amenable to automated reasoning if they are to be used effectively by automated processes.
These considerations have led to the development of OIL, an ontology language that extends RDFS with a much richer set of modelling primitives. A similar RDFS based web ontology language called DAML has been developed as part of the DARPA DAML project. These two languages are soon to merged under the name DAML+OIL.
Reasoning with terms from deployed ontologies will be important for the Semantic Web, but reasoning support is also extremely valuable at the ontology design phase, where it can be used to to detect logically inconsistent classes and to discover implicit subclass relations. This encourages a more descriptive approach to ontology design, with the reasoner being used to infer part of the subsumption lattice (such an approach has been successfully applied in the TAMBIS project): the resulting ontologies contain fewer errors, yet provide more detailed descriptions that can be exploited by automated processes in the Semantic Web. Finally, reasoning is of particular benefit when ontologies are large and/or multiply authored, and also facilitates ontology sharing, merging and integration; considerations that will be particularly important in the distributed web environment.
The development of OIL resulted from efforts to combine the best features of frame and DL based knowledge representation systems, while at the same time maximising compatibility with emerging web standards. The intention was to design a language that was intuitive to human users, and yet provided adequate expressive power for realistic applications
The resulting language combines a familiar frame like syntax (derived in part from the OKBC-lite, with the power and flexibility of a DL (i.e., boolean connectives, unlimited nesting of class elements, transitive and inverse slots, general axioms, etc.). The language is defined as an extension of RDFS, thereby making OIL ontologies (partially) accessible to any RDFS-aware application.
A frame syntax is less daunting to ontologists/domain experts than a DL style syntax, and it facilitates a modelling style in which ontologies start out simple (in terms of their descriptive content) and are gradually extended, both as the design itself is refined and as users become more familiar with the language's advanced features. The frame paradigm also facilitates the construction and adaption of tools.
On the other hand, basing the language on an underlying mapping to a very expressive DL (SHIQ) provides a well defined semantics and a clear understanding of its formal properties. The mapping also provides a mechanism for the provision of practical reasoning services by exploiting implemented DL systems, e.g., the FaCT system.
Further information regarding OIL can be found at the OIL home page.
The availability of tools is a crucial factor in the adoption of languages such as OIL. OIL has a frame-like syntax, which facilitates tool building, yet can be mapped onto an expressive description logic (DL), which facilitates the provision of reasoning services. OilEd is an ontology editing tool for OIL (and DAML+OIL) developed at the University of Manchester. It exploits both these features in order to provide a familiar and intuitive style of user interface with the added benefit of reasoning support. Its main novelty lies in the extension of the frame editor paradigm to deal with a very expressive language, and the use of a highly optimised DL reasoning engine (the FaCT reasoner) to provide sound and complete yet still empirically tractable reasoning services.
Last modified: Tue Dec 18 09:43:19 GMT Standard Time 2001