1 / 56

Next Generation Semantic Web Applications

Next Generation Semantic Web Applications. Prof. Enrico Motta Director, Knowledge Media Institute The Open University Milton Keynes, UK. Structure of the Talk. Quick Recap: What is the Semantic Web? State of the art: 1st Generation SW Applications Emphasis on ontology-driven data aggregation

colleen-beach
Download Presentation

Next Generation Semantic Web Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Next Generation Semantic Web Applications Prof. Enrico MottaDirector, Knowledge Media InstituteThe Open UniversityMilton Keynes, UK

  2. Structure of the Talk • Quick Recap: What is the Semantic Web? • State of the art: 1st Generation SW Applications • Emphasis on ontology-driven data aggregation • Limited with respect to their ability to exploit large scale, heterogeneous semantic markup • Key research issues • What needs to be done to enable the effective development of the next generation of SW Applications • Need for a different approach to some key res. areas • How the SW itself can be exploited to address such key research issues

  3. Quick Recap: What is the Semantic Web?

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

  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. <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 worksInOrgUnit hasJobTitle partOf String Organization-Unit

  7. SW = A Conceptual Layer over the web

  8. SW is Heterogeneous!

  9. Generating semantic markup <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> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple> <RDF triple>

  10. Key aspects of the SW • Size (= Huge) • Sem. markup (eventually to reach) the same order of magnitude as the web • Conceptual Heterogeneity (= Big) • Sem. markup based on many different ontologies • Rate of change (= Very High) • Data generated all the time from human and artificial agents… • Provenance (= Very Heterogeneous) • ….Hence provenance itself is extremely heterogeneous • Trust (= very variable and subjective) • A side-effect of heterogeneous provenance • Data Quality (= very variable) • No guarantee of correctness • Intelligence (= by-product of size and heterogeneity) • Rather than a by-product of sophisticated problem solving

  11. Compare with traditional KBS • Size (= Small or Medium) • KBS normally small to medium size • Conceptual Heterogeneity (= Not an issue) • KBS normally based on a single conceptual model • Rate of change (= Very Low) • Change rate under developers' control (hence, low) • Provenance (= Not an issue) • KBS are normally created ad hoc for an application by a centralised team of developers • Trust (= not a major issue) • Centralisation of devpt. process implies no significant trust issues • Data Quality (= not a major issue) • Again, centralisation guarantees data quality across the board • Intelligence (= by-product of complex, task-centric reasoning) • E.g., sophisticated diagnostic, planning systems…

  12. The Semantic Web today 1st Generation SW Applications

  13. Bibliographic Data CS Dept Data AKT Reference Ontology RDF Data

  14. Features of 1st generation SW Applications • Typically use a single ontology • Usually providing a homogeneous view over heterogeneous data sources Limited use of existing SW data • Closed to semantic resources • Limited interactivity • In contrast with typical web 2.0 applications Hence: current SW applications are far more similar to traditional KBS (closed semantic systems) than to 'real' SW applications (open semantic systems)

  15. It is still early days.. 1895 2006

  16. Next Generation SW Applications

  17. Next generation SW applications • Able to exploit the SW at large • Hence: Multi-Ontology • Supporting interactivity • E.g., allowing users to add semantic data • Hence, open with respect to SW resources • Ideally also able to exploit non-SW data • E.g., folksonomies • Hence, embedding powerful information extraction engines NG SW Application

  18. Two systems we have built Magpie AquaLog

  19. Magpie Components Ontology cache (Lexicon) Enriched Web Page Magpie Hub Web Page Problem Domain & Resources Jabber Server (found-item 3275578832 localhost #u"http://localhost/people/motta/" john-domingue john-domingue) (found-item 3275578832 localhost Ontology based Proxy Server Semantic Log

  20. AquaLog: Ontology-Driven Question Answering Which is the capital of Spain? Madrid NL SENTENCE INPUT ANSWER (?, capital, Spain) <Spain, has-capital-city, Madrid> QUERY TRIPLES RESULT TRIPLES Linguistic Analysis Mapping Engine NL Generation

  21. PowerMagpie: Semantic browsing on the 'open' SW Need for mechanisms for automatically identifying semantic markup relevant to the current page, user, browsing session, etc..

  22. PowerAqua: QA on the 'open' semantic web Need for mechanisms for automatically locating ontologies relevant to the current query, map user terminology to ontologies,integrate info from different ontologies, etc..

  23. What needs to be done to facilitate the development of such 2nd generation SW applications?

  24. Dynamic Ontology Selection • First: powerful support for ontology selection • Both PowerAqua and PowerMagpie heavily rely on ontology selection to locate possibly relevant knowledge in response to • User queries (PowerAqua) • Accessing web pages (PowerMagpie) • Hence, ontology selection is a crucial task for both systems

  25. Current support for ontology selection

  26. Limitations of Swoogle • Query/Search • Only keyword search, we need more powerful query methods (e.g., ability to pose formal queries) • Repository structure • Very weak in Swoogle, not even duplicates are dealt with • Need for automatic derivation of relations between ontologies • E.g., same-ontology-as, ontology-extends, ontology-incompatible-with, etc….. • We need these relations to structure the repository and to support more powerful ranking methods (see next bp) • Ontology ranking • Swoogle only uses a 'popularity-based' one, we need other methods as well

  27. We also need: • Methods for fast extraction of ontology modules • Typically we only want the part of the ontology relevant to our current needs • Methods for the integration of information derived from different ontologies • In the context of QA this problem typically reduces to that of deciding whether two instances denote the same entity

  28. Even more importantly.. • Need to look at a number of key research issues in the context provided by NG-SW applications • Example: Ontology Mapping • Current work focuses on design-time mapping of complete ontologies • Example: Ontology Selection • Current work focuses on user-mediated ontology selection • Example: Ontology Modularization • Current work by and large assumes that the user is in the loop

  29. A new application scenario • NG-SW applications require algorithms able to perform tasks such as selecting, modularizing, and mapping ontologies at run time • Moreover, in such a context, mapping is concerned with mapping ontology fragments, rather than complete ontologies

  30. So What? • Time to go beyond 1st generation applications • 2nd generation SW applications will exploit much more fully the large scale semantic markup provided by the SW • Many issues to be addressed: • Better ontology crawling, indexing, retrieving and ranking support • Mapping, selection, and modularization methods appropriate for NG-SW applications • Further acceleration needed in the generation of semantic markup

  31. Exploiting the SW itself to tackle its heterogeneity • Interestingly, a NG-SW-based approach can also be used also to tackle key SW tasks, such as Ontology Mapping • Based on the use of the SW itself as background knowledge

  32. Exploiting Large-Scale Semantics Case Study: Using the Semantic Web as background knowledge in Ontology Mapping

  33. Ontology Mapping: State of the Art • State-of-the-art methods rely on a combination of: • Label similarity methods • e.g., Full_Professor = FullProfessor • Structure similarity methods • Using taxonomic information or information about domain and range of associated properties • However, as pointed out by Aleksovski et al (EKAW, 2006): • In many cases there is no sufficient lexical overlap • In many cases source and target ontology have not sufficient structure to allow effective structure-based mapping

  34. Background Knowledge ? B A Use of bkg. knowledge for ontology mapping

  35. External Source = One Ontology • Alekszovski et al. EKAW’06 • Map candidate terms into concepts from a richly axiomatized domain ontology (anchors) • Derive a mapping based on the relation of the anchor terms • Advantages: • Handles dissimilar ontologies • Returns semantic mappings B’ rel A’ = = • Disadvantages: • Assumes that a suitable domain ontology is available. • Approach only suitable for closed domains rel A B

  36. 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 mappings using IR techniques • Advantages: • General purpose + OnlineDictionary IR Methods • Disadvantages: • IR Methods introduce noise rel A B

  37. External Source = WordNet • Lopez et al. ESWC ’05 • use wordnet to map queries expressed in the user's terminology to a domain ontology to support question answering • Advantages: • General purpose WordNet • Disadvantages: • Knowledge sparseness • Works best with concepts, not so useful with relations • WordNet is not an ontology!!! rel A B

  38. Knowledge-poor ontology mapping • Actually isn’t a bit strange that such complex and knowledge-poor methods are devised, when the SW already provides so much background knowledge?….

  39. External Source = SW • Proposal: • rely on online ontologies (Semantic Web) to derive mappings • ontologies are dynamically discovered and combined Semantic Web • Advantages: • General purpose • Does not introduce noise • Works with any kind of domain entities (concepts, relations, instances) rel A B

  40. Strategy 1 - Definition Find ontologies that contain equivalent classes to 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

  41. Strategy 1- Variants Quick variant: Stop as soon as a relation is found B1’ Semantic Web A1’ O1 A B

  42. Strategy 1- Variants Precise variant: Derive all possible mappings from all ontologies and combine them into a final mapping. • Dealing with Contradictions: • Return all mappings even if contradictory • Return a mapping only when there is no contradiction • Return the most frequent mapping (i.e., the mapping derived from most ontologies) • Return the mappings with 'higher authority' (based on metrics of ontology evaluation or trust) • Try to combine mappings B1’ B2’ Semantic Web A1’ A2’ O1 O2 A B

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

  44. 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. rel B’ C’ Semantic Web rel C B A’ rel A B

More Related