1. OIL ontology inference and interchange

2. On-To-Knowledge2 Topics for this presentation OIL as a common core language (4) aspects of OIL (5) OIL on the Web l Why XML is not enough (3) l Why RDF(S) is not enough (9) l OIL as RDF(S) extension (3) WP1 results & plans (4)

3. On-To-Knowledge3 Remember: Ontologies are crucial for KM “consensual and formal specifications of conceptualisations” provide a “shared understanding of a domain for communication across people and systems” “consensual and formal specifications of conceptualisations” provide a “shared understanding of a domain for communication across people and systems”

4. Place in the project WP 1: l tools for ontology construction and interchange l foundational to all three layers of On-To-Knowledge architecture WP1.1= ontology language Partners = VU + AIdministrator Deliverable 1= ontology language version 1 user repository source info ontologies

5. Requirements for a common core Ontology-language Well defined syntax (pretty obvious) l read ontologies Well defined semantics l often overlooked but equally important l process (“understand”) ontologies Expressive enough l to capture many ontologies Easy mapping l to/from other ontology languages Efficient reasoning support

6. Why Reasoning Support? Reasoning support is key feature of OIL-core Important l as design support tool l for large ontologies l with multiple authors l for integrating and sharing ontologies Because it allows to l Establish inter-ontology relationships l Check for consistency l Check for (unexpected) implied relationships Shown useful for DB schema integration

7. Ingredients for a common core Frame Based Languages l intuitive for many users l Extensive set of modelling primitives l OKBC, OKBC-lite, XOL (Description) Logic-based languages l negation and disjunction (e.g disjointness) l properties for slots/relations e.g. transitivity for contained-in l Formal semantics l Reasoning support inconsistency-detection, implicit superclass-detection

8. On-To-Knowledge8 Proposed common core: OIL Based on standard frame languages (OKBC) l restricts & extends Has both XML and RDF(S) based syntax formalised by DL style logical constructs Still has frame “look and feel” Can still function as a basic frame language OIL core language restricted to allow: l reasoning support l No constructs with ill defined semantics OIL WWW DL Frames

9. On-To-Knowledge9 OIL: Restricts Frame Languages No defaults limited axioms/rules only definition of ontology (not individuals) Main reasons for this: Reasoning support Semantics OIL WWW DL Frames

10. On-To-Knowledge10 OIL: Extends Frame Languages Classes can be primitive (nec. conditions) l elephant  animal that has-colour grey or defined (nec. and sufficient conditions) l vegetarian  person who eats meat nor fish Classes allowed in slot constraints l slot-constraint eats has-value meat (eats some meat) l slot-constraint eats value-type meat (eats only meat) OIL WWW DL Frames

11. OIL: Extends Frame Languages Can use arbitrary class expressions instead of only class names l slot-constraint eats value-type NOT ( OR meat fish) Cardinality constraints can include value-types l slot-constraint eats max-cardinality 1 plant Supports sub-slot relation l daughter-of sub-slot of child-of Slot properties l transitive (e.g., part-of ) l symmetrical (e.g., connected-to) OIL WWW DL Frames

12. On-To-Knowledge12 OIL has a Formal semantics Defined by mapping to very expressive DL l slot-constraint eats has-value meat, fish =  eats:meat   eats:fish Mapping is used to provide reasoning support from a DL system (e.g., FaCT) OIL WWW DL Frames

13. OIL (explained by example) class-def animal% animals are a class class-def plant% plants are a class subclass-of NOT animal% that is disjoint from animals class-def tree subclass-of plant% trees are a type of plants class-def branch slot-constraint is-part-of% branches are parts of some tree has-value tree max-cardinality 1 class-def defined carnivore% carnivores are animals subclass-of animal slot-constraint eats % that eat any other animals value-type animal class-def defined herbivore % herbivores are animals subclass-of animal, NOT carnivore % that are not carnivores, and slot-constraint eats % they eat plants or parts of plants value-type plant OR (slot-constraint is-part-of has-value plant)

14. On-To-Knowledge14 How to put ontologies on the Web (internet, intranet, extranet) XML HTML XHTML SMIL RDF(S) PICS Declarative Languages (OIL, DAML-O) DC The W3C hierarchy of languages:

15. On-To-Knowledge15 XML: Document = labelled tree course teachertitlestudents namehttp............... DTD: simple grammars to describe legal trees So: why not use XML to represent ontologies ? node = label + attr/values + contents

16. On-To-Knowledge16 XML: limitations for semantic markup XML makes no commitment on:  Domain specific ontological vocabulary  Ontological modelling primitives  requires pre-arranged agreement on  &  Only feasible for closed collaboration l agents in a small & stable community l pages on a small & stable intranet not for sharable Web-resources

17. On-To-Knowledge17 Remember the W3C vision XML HTML XHTML SMIL RDF(S) PIC Declarative Languages OIL DC

18. On-To-Knowledge18 RDF(S): general background Intended for representation “meta-data”, basis for Web-based ontology-language W3C recommendation “Because it’s there”: l pushed hard by W3C (TBL Himself) l basis of $ 80M DAML program l Already embraced by some vendors (Netscape)

19. On-To-Knowledge19 Bluffer’s guide to RDF (1) Object --Attribute-> Value triples objects are web-resources Value is again an Object: l triples can be linked l data-model = graph pers05 ISBN... Author-of pers05 ISBN... MIT ISBN... Publ- by Author-of Publ- by

20. On-To-Knowledge20 Bluffer’s guide to RDF (2) Object --Attribute-> Value triples l objects are web-resources l triples can be linked l data-model = graph Any statement can be an object l graphs can be nested pers05 ISBN... Author-of NYT claims

21. On-To-Knowledge21 Bluffer’s guide to RDF Schema So, RDF : l (very small) commitment to modelling primitives l but: no commitment to domain vocabulary  RDF Schema Define vocabulary for RDF Organise this vocabulary in a typed hierarchy l Class, SubClassOf, type l Property, subPropertyOf, l domain, range

22. Bluffer’s guide to RDF(S) RDF Statement = Object - Attribute -Value triple Values can be objects (chaining) Statements can be objects (reification) RDF Statement = Object - Attribute -Value triple Values can be objects (chaining) Statements can be objects (reification) RDF Schema Define domain-specific vocabulary for OAV’s Type-hierarchy for this vocabulary using l Class, SubClassOf, type l Property, subPropertyOf, l domain, range RDF Schema Define domain-specific vocabulary for OAV’s Type-hierarchy for this vocabulary using l Class, SubClassOf, type l Property, subPropertyOf, l domain, range

23. RDF Schema syntax in XML

24. On-To-Knowledge24 State-of-the-art@W3C RDF: to represent “meta-data” RDF-S: to define vocabulary for RDF RDF is data-model + syntax l only a very weak semantic interpretation l no inference model RDF-S goes a step further, but still l no precisely described meaning l no inference model

25. Quote from Ora Lassila (RDF) Future: We Need More! Structural modeling obviously not enough l we need a “logic layer” on top of RDF l some type of description logic is a possibility (after all, we are talking about frame systems) Exposing a wide variety of data sources as RDF is useful, particularly if we have logic/rules which allow us to draw inference from this data My proposal: RDF + DL = “Frame System for WWW” l this is probably a good starting point for DAML as well (details to be worked out by this workshop) Future: We Need More! Structural modeling obviously not enough l we need a “logic layer” on top of RDF l some type of description logic is a possibility (after all, we are talking about frame systems) Exposing a wide variety of data sources as RDF is useful, particularly if we have logic/rules which allow us to draw inference from this data My proposal: RDF + DL = “Frame System for WWW” l this is probably a good starting point for DAML as well (details to be worked out by this workshop)

26. On-To-Knowledge26 Quote from Henry Thompson “The Semantic Web needs a logic on top” NB: “a” logic= the box with the crank  FOL OIL = modelling primitives from frames (OKBC-lite) + semantics and inference from Description Logic + syntax from RDF(S) & XML(S) OIL WWW DL Frames

27. On-To-Knowledge27 OIL for the Semantic Web XML HTML XHTML SMIL RDF PIC Declarative Languages OIL, DAML-O DC OIL WWW DL Frames

28. On-To-Knowledge28 OIL as RDF(S) extension (1/2) <rdf:type rdf:resource=” http://www.ontoknowledge.org /#DefinedClass”/>

30. OIL as RDF(S) extension (2/2) class-def subclass-of slot-def subslot-of domain range class-def subclass-of slot-def subslot-of domain range class-expressions AND, OR, NOT slot-constraints has-value, value-type cardinality slot-properties trans, symm class-expressions AND, OR, NOT slot-constraints has-value, value-type cardinality slot-properties trans, symm RDF(S) OIL

31. On-To-Knowledge31 OIL as the basis for DAML-O DAML = $80M DARPA program for Semantic Web Ontologies are regarded as fundamental first version of DAML-O out < end 2000 mandatory use for all DAML participants (W3C, Stanford, ISI, Lockheed, MIT, Nokia,...) OIL is used as the basis for DAML-O

32. On-To-Knowledge32 WP1: Results (1/2) Definition of language l semantics l XML encoding l RDF encoding 10 publications l EKAW, ECAI-WS, IEEE-IC, DL, KRDB, eBeW already take-up in work by others Madrid, Bremen, IBROW, Boeing,Daimler-Chrysler,... collaboration with DAML

33. On-To-Knowledge33 WP1: results (2/2) translators (Java, XSL-based): l XML OIL  FaCT l XML OIL  RDFS OIL case-studies l GIS ontology mapping l (KA) 2 ontology l CIA world fact book

34. OIL: currently available tools Definition of language l semantics l XML encoding l RDF encoding Tools: l translators (XSL based) l reasoner (FaCT, DL-based) l OntoEdit, OILed case-studies l GIS ontology mapping l (KA) 2 ontology l CIA world fact book

35. On-To-Knowledge35 OIL: some collaborating parties EU academics l University of Bremen l Univ. of A’dam l OU-UK l Univ. Manchester l A’dam Medical Centre EU IST Projects l IBROW l Comma l C-Web US academics: l Univ. of Stanford (DB, KSL, Med.Inf) l Univ. of Maryland l SRI outside academia l W3C (RDF Working group) l DARPA (DAML initiative) Industrials: l Boeing l Daimler-Chrysler l Interprice

36. On-To-Knowledge36 OIL: current & future work Layered approach to anguage extensions RDF(S)  OIL-lite  Standard OIL  OIL layer 1 ... l axioms, concrete domains, modules, defaults,... Ontology construction Ontology evolution Ontology mapping