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Towards a new generation of semantic web applications

Towards a new generation of semantic web applications. Prof. Enrico Motta, PhD Knowledge Media Institute The Open University Milton Keynes, UK. The Semantic Web.

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Towards a new generation of semantic web applications

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  1. Towards a new generation ofsemantic web applications Prof. Enrico Motta, PhDKnowledge Media InstituteThe Open UniversityMilton Keynes, UK

  2. The Semantic Web A large scale, heterogenous collection of formal, machine processable, web accessible, ontology-based statements (semantic metadata) about web resources and other entities in the world, expressed in a XML-based syntax

  3. The Semantic Web (pragmatic def.) The collection of all statements expressed in one of the following formalisms:{OWL, RDF, DAML, DAML+OIL, RDF-A…}, which can be accessed on the web

  4. <akt:Person rdf:about="akt:EnricoMotta"> <rdfs:label>Enrico Motta</rdfs:label> <akt:hasAffiliation rdf:resource="akt:TheOpenUniversity"/> <akt:hasJobTitle>kmi director</akt:hasJobTitle> <akt:worksInOrgUnit rdf:resource="akt:KnowledgeMediaInstitute"/> <akt:hasGivenName>enrico</akt:hasGivenName> <akt:hasFamilyName>motta</akt:hasFamilyName> <akt:worksInProject rdf:resource="akt:Neon"/> <akt:worksInProject rdf:resource="akt:X-Media"/> <akt:hasPrettyName>Enrico Motta</akt:hasPrettyName> <akt:hasPostalAddress rdf:resource="akt:KmiPostalAddress"/> <akt:hasEmailAddress>e.motta@open.ac.uk</akt:hasEmailAddress> <akt:hasHomePage rdf:resource="http://kmi.open.ac.uk/people/motta/"/> </akt:Person> hasAffiliation Organization Person Ontology worksInOrgUnit hasJobTitle partOf String Organization-Unit Metadata

  5. Ontology <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> Metadata <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> UoD

  6. Proposition #1 The SW today has already reached a level of scale good enough to make it a very useful source of knowledge to support intelligent applications This is unprecedented in the history of AI

  7. So, let's have a look at the semantic web as it is today….

  8. Charting the web

  9. Charting the web (2)

  10. Proposition #2 The SW may well provide a solution to one of the classic AI challenges: how to construct and manage large volumes of knowledge to construct truly intelligent problem solvers and address the brittleness of traditional KBS

  11. 1982

  12. Knowledge Representation Hypothesis Any mechanically embodied intelligent process will be comprised of structural ingredients that we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and independent of such external semantic attribution, play a formal but causal and essential role in engendering the behaviour that manifests that knowledge Brian Smith, 1982

  13. Large Bodyof Knowledge Intelligence as a function of possessing domain knowledge KA Bottleneck Intelligent Behaviour

  14. Knowledge Large Bodyof Knowledge The Knowledge Acquisition Bottleneck KA Bottleneck Intelligent Behaviour

  15. SW as Enabler of Intelligent Behaviour Intelligent Behaviour

  16. KBS vs SW Systems

  17. Key Paradigm Shift

  18. Overall Goal Our research programme is to contribute to the development of this large-scale web of data and develop a new generation of web applications able to exploit it to provide intelligent functionalities

  19. So, how can we exploit this emerging, large scale semantic resource? Some examples….

  20. 1 0.5 0.9 0.5 0.9 0.9 1 • Label similarity methods • e.g., Full_Professor = FullProfessor • Structure similarity methods • Using taxonomic/property related information Ontology Matching

  21. R New paradigm: use of background knowledge Background Knowledge (external source) R B’ A’ B A

  22. External Source = One Ontology • Aleksovski et al. EKAW’06 • Map (anchor) terms into concepts from a richly axiomatized domain ontology • Derive a mapping based on the relation of the anchor terms Assumes that a suitable (rich, large) domain ontology (DO) is available.

  23. External Source = Web • van Hage et al. ISWC’05 • rely on Google and an online dictionary in the food domain to extract semantic relations between candidate terms using IR techniques + OnlineDictionary Does not rely on a rich Domain Ont, IR Methods • Precisionincreases significantly if domain specific sources are used: • 50% - Web; • 75% - domain texts. rel A B

  24. External Source = SW • Proposal: • rely on online ontologies (Semantic Web) to derive mappings • ontologies are dynamically discovered and combined Semantic Web Does not rely on any pre-selected knowledge sources. rel A B M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge inOntology Mapping", Ontology Mapping Workshop, ISWC’06. Best Paper Award

  25. How to combine online ontologies to derive mappings?

  26. Strategy 1 - Definition Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping. For each ontology use these rules: B1’ B2’ Bn’ Semantic Web … An’ A1’ A2’ O2 On O1 These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’: rel A B

  27. Food MeatOrPoultry AcademicStaff Semantic Web Semantic Web RedMeat Researcher Beef ka2.rdf Tap AcademicStaff Researcher Beef Food SWRC SR-16 FAO_Agrovoc ISWC Strategy 1- Examples

  28. Strategy 2 - Definition Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping. Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B. Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B. rel B’ C’ Semantic Web rel C B A’ rel A B

  29. Strategy 2 - Examples Ex1: Vs. (r1) (midlevel-onto) (Tap) (Same results for Duck, Goose, Turkey) Ex2: Vs. (pizza-to-go) (r1) (SUMO) Ex3: Vs. (pizza-to-go) (r3) (wine.owl)

  30. Large Scale Evaluation Matching AGROVOC (16k terms) and NALT(41k terms) (derived from 180 different ontologies) Evaluation: 1600 mappings, two teams Average precision: 70% (comparable to best in class) M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge“.

  31. Chart 2

  32. Proposition #3 Using the SW to provide dynamically background knowledge to tackle the Agrovoc/NALT mapping problem provides the first ever test case in which the SW, viewed as a large scale heterogeneus resource, has been successfully used to address a real-world problem

  33. Next Generation Semantic Web Applications NG SW Application • Able to exploit the SW at large • Dynamically retrieving the relevant semantic resources • Combining several, heterogeneous Ontologies

  34. Contrast with 1st generation SW Applications • Typically use a single ontology • Usually providing a homogeneous view over heterogeneous data sources. • Limited use of existing SW data • Typically closed to semantic resources 1st generation SW applications are far more similar to traditional KBS (closed semantic systems) than to 'real' SW applications (open semantic systems)

  35. It is still early days.. 1895 2007

  36. Current Gateway to the Semantic Web

  37. Limitations of Swoogle • Very limited quality control mechanisms • Many ontologies are duplicated • No quality information provided • Limited Query/Search mechanisms • Only keyword search; no distinction between types of elements • need for more powerful query methods (e.g., ability to pose formal queries; ability to distinguish between classes and instances, etc…) • Limited range of ontology ranking mechanisms • Swoogle only uses a 'popularity-based' one • No support for ontology modularization

  38. A New Gateway to the Semantic Web

  39. Ontology Structuring Relations inconsistent-with extends

  40. Ontology Structuring Relations inconsistent-with Inconsistent-with extends

  41. Formal Queries and relation discovery…

  42. Current state of Watson • Initial version implemented • Demo version available online • See http://watson.kmi.open.ac.uk/ • However still rather unstable….. • Stable version to be available within 4-6 weeks • Initial crawl of the SW has already produced interesting results….

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