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Advanced topics in software engineering (Semantic web)

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Presentation on theme: "Advanced topics in software engineering (Semantic web)"— Presentation transcript:

1 Advanced topics in software engineering (Semantic web)

2 Outline Toward an ontology language for web Early developments Ontology inference layer (OIL) OIL Syntax and semantics Topics from chapter 14 of DLHB

3 Toward an ontology language for web XML represents structure without any formal meaning RDF has data-type (triples) RDFS allows making simple hierarchies A web ontology needs more expressive power OIL, DAML-ONT, DAML-OIL and now OWL has enough expressive power to be used as web ontologies

4 Early activities to give meaning to the web Untangle: – Originally a DL system for representing bibliographic information (1994) – By the rise of web Untangle shifted in 1995 to cataloging and classifying pages on the web –The first DL system which was used for the web

5 Early activities to give meaning to the web (cont) FindUR: – Developed at AT&T – The basic notion was query expansion: taking synonyms and hyponyms and including them in the input terms –Uses Wordnet (synsets and hyponyms) –The usage of DL was to maintain some amount of consitency (e.g. finding cycles in taxonomies)

6 Ontology inference layer European research community activities resulted in OIL (http://img.cs.man.ac.uk/oil)http://img.cs.man.ac.uk/oil OILEd is a tool developed to ease creation of OIL ontologies It is a frame-based language with well defined semantics in DL Serialization to RDF is specified As other frame based systems, concepts are represented as Frames whose main components consists of a list of super classes (more general concepts) and a list of slot/filler pairs. A frame corresponds to a Concept in DL A slot corresponds to a role in a DL A slot/filler pair corresponds to either a value restriction or an existential quantification

7 OIL extentions to basic frame syntax Arbitrary class expressions can be formed, and used anywhere that a class name can be used. In particular, class expressions can be used as slot fillers, whereas in typical frame languages slot fillers are restricted to being class (or individual) names. A slot-filler pair can itself be treated as a class: it can be used anywhere that a class name can be used, and can be combined with other classes in class expressions. Class definitions (frames) have an (optional) additional field that specifies whether the class definition is primitive (a subsumption axiom) or non-primitive (an equiv-alence axiom). If omitted, this defaults to primitive.

8 OIL extentions to basic frame syntax (cont.) Different types of slot constraint are provided, specifying value restriction, exis-tential quantification and various kinds of cardinality constraint. Global slot definitions are extended to allow the specification of superslots and properties like transitive and symmetrical Unlike many frame languages, there is no restriction on the ordering of class and slot definitions, so classes and slots can be used before they are “defined”. This means that OIL ontologies can contain cycles. In addition to standard class definitions (frames), OIL also provides axioms for asserting disjointness, equivalence and coverings with respect to class expressions.

9 An example OIL ontology

10 An example OIL ontology (cont.)

11 OIL Syntax and Semantics Class definitions Examples

12 OIL Syntax and Semantics (cont.) Slot constraint Examples

13 OIL Syntax and Semantics (cont.) Slot definitions Examples

14 Full translation of the example

15 Serializing OIL in XML

16 Serializing OIL in XML (cont.)

17 Serializing OIL in RDF

18 Serializing OIL in RDF (cont.)

19 Conclusion It has long been realised that the web would benefit from more structure It is widely agreed that ontologies will play a key role in providing this structure. OIL is one of the languages developed for this purpose OIL has well defined semantics in DL There are tools provided for creation and reasoning: OILEd with reasoning support of Fact system Protege (DAML+OIL) ONTOEdit (DAML+OIL) Current challenges: Finding sound and complete reasoning algorithms for the web applications How about large Aboxes Recognition of expressive power needed for each type of applications


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