1. Next Generation Semantic Web Applications Prof. Enrico Motta Director, Knowledge Media Institute The Open University Milton 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 Metadata UoD

6. Person Organization String Organization-Unit partOf hasAffiliation worksInOrgUnit hasJobTitle Enrico Motta kmi director enrico motta Enrico Motta e.motta@open.ac.uk <akt:hasHomePage rdf:resource="http://kmi.open.ac.uk/people/motta/"/>

7. SW = A Conceptual Layer over the web

8. SW is Heterogeneous!

9. Generating semantic markup

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

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

17. 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 Features of 1st generation SW Applications Hence: current SW applications are far more similar to traditional KBS (closed semantic systems) than to 'real' SW applications (open semantic systems)

18. 18952006 It is still early days..

19. Next Generation SW Applications

20. Next generation SW applications NG SW Application 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

21. Two systems we have built MagpieAquaLog

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

26. AquaLog: Ontology-Driven Question Answering Which is the capital of Spain? NL SENTENCE INPUT QUERY TRIPLES ANSWER (?, capital, Spain) Linguistic AnalysisMapping Engine RESULT TRIPLES NL Generation Madrid

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

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

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

30. 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

31. Current support for ontology selection

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

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

39. 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

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

41. 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 AB B’ A’ = = rel Advantages: Handles dissimilar ontologies Returns semantic mappings Disadvantages: Assumes that a suitable domain ontology is available. Approach only suitable for closed domains

42. 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 AB rel + OnlineDictionary IR Methods Advantages: General purpose Disadvantages: IR Methods introduce noise

43. 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 AB rel WordNet Advantages: General purpose Disadvantages: Knowledge sparseness Works best with concepts, not so useful with relations WordNet is not an ontology!!!

44. 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?….

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

46. Strategy 1 - Definition Find ontologies that contain equivalent classes to A and B and use their relationship in the ontologies to derive the mapping. AB rel Semantic Web A1’A1’ B1’B1’ A2’A2’ B2’B2’ An’An’ Bn’Bn’ O1O1 O2O2 OnOn For each ontology use these rules: … These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:

47. Strategy 1- Variants AB Quick variant: Stop as soon as a relation is found Semantic Web A1’A1’ B1’B1’ O1O1

48. Strategy 1- Variants Precise variant: Derive all possible mappings from all ontologies and combine them into a final mapping. AB Semantic Web A1’A1’ B1’B1’ O1O1 A2’A2’ B2’B2’ O2O2 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

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

50. Strategy 2 - Definition Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping. AB rel Semantic Web A’ BC C’ B’rel 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.

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

52. Conclusions Using the SW as background knowledge for ontology mapping has several benefits –Suitable for our NG-SW scenario as there is no need for design-time selection of a background knowledge –Even when design-time selection is feasible, it is suitable for those cases where a suitable domain ontology cannot be found –Reduces noise by exploiting only ontologies –Can be tailored to handle multiple solutions –Can be integrated with other approaches, based on lexical and structural analysis

54. If you would like to find out more.. 'Vision' papers –Motta, E., Sabou, M. (2006). "Next Generation Semantic Web Applications". 1st Asian Semantic Web Conference, Beijing. –Motta, E., Sabou, M. (2006). "Language Technologies and the Evolution of the Semantic Web". LREC 2006, Genoa, Italy. –Motta, E. (2006). "Knowledge Publishing and Access on the Semantic Web: A Socio-Technological Analysis". IEEE Intelligent Systems, Vol.21, 3, (88-90). Ontology Modularization –D' Aquin, M., Sabou, M., Motta, E. (2006). "Modularization: A key for the dynamic selection of relevant knowledge components". ISWC 2006 Workshop on Ontology Modularization

55. If you would like to find out more.. Ontology Mapping –Lopez, V., Sabou, M., Motta, E. (2006). "Mapping the real semantic web on the fly". International Semantic Web Conference, Georgia, Atlanta. –Sabou, M., D'Aquin, M., Motta, E. (2006). "Using the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping. Ontology Selection –Sabou, M., Lopez, V., Motta, E. (2006). "Ontology Selection for the Real Semantic Web: How to Cover the Queen’s Birthday Dinner?". Proceedings of EKAW 2006, Podebrady, Czech Republic.