A Research Programme for the Semantic Web

1. A Research Programme forthe Semantic Web Enrico Motta, Sofia Angeletou, Claudio Baldassarre, Mathieu D’Aquin, Martin Dzbor, Laurian Gridinoc, Davide Guidi, Vanessa Lopez, Marta Sabou Knowledge Media InstituteThe Open UniversityMilton Keynes, UK

2. Introduction This talk presents a number of projects, which are part of an integrated effort at exploring the possibilities opened by the Semantic Web, viewed as a domain-independent, large scale supplier of formally encoded background knowledge, with respect to enabling intelligent problem solving. We call the resulting applications: Next Generation Semantic Web Applications

3. Organization of the Talk • The Semantic Web • The Semantic Web in the context of AI research • Next Generation Semantic Web Applications • What are they? • Why are they different from 1st generation SW Applications? • Examples • Ontology Matching • Integrating Web2.0 and SW • Semantic Web Browsing • Question Answering • Conclusions

4. The Semantic Web The collection of all formal, machine processable, web accessible, ontology-based statements (semantic metadata) about web resources and other entities in the world, expressed in a knowledge representation language based on an XML syntax (e.g., OWL, DAML, DAML+OIL, RDF, etc…)

6. Semantic Web Document <foaf:PersonalProfileDocument rdf:about="http://kmi.open.ac.uk/people/rdf.cfm/idstring/sofia-angeletou/"> <dc:title>Sofia Angeletou&apos;s RDF Description</dc:title> <rdfs:label>Sofia Angeletous RDF Description</rdfs:label> <dc:description>RDF description for Sofia Angeletou in machine-readable RDF/XML</dc:description> <dc:creator rdf:resource="http://identifiers.kmi.open.ac.uk/people/sofia-angeletou/" /> <foaf:maker rdf:resource="http://identifiers.kmi.open.ac.uk/people/sofia-angeletou/"/> <foaf:primaryTopic rdf:resource="http://identifiers.kmi.open.ac.uk/people/sofia-angeletou/"/> </foaf:PersonalProfileDocument> <foaf:Person rdf:about="http://identifiers.kmi.open.ac.uk/people/sofia-angeletou/"> <foaf:name>Sofia Angeletou</foaf:name> <foaf:firstName>Sofia</foaf:firstName> <foaf:surname>Angeletou</foaf:surname> <foaf:mbox_sha1sum>F78114D4E45CFC6AC811E6191F50182FB9838938</foaf:mbox_sha1sum> <foaf:phone rdf:resource="tel:+44-(0)1908-654777"/> <foaf:jabberID>s.angeletou%open.ac.uk@msg.open.ac.uk</foaf:jabberID> <foaf:homepage rdf:resource="http://"/> <foaf:publications rdf:resource="http://kmi.open.ac.uk/publications/publications.cfm?id=167"/> <foaf:workplaceHomepage rdf:resource="http://kmi.open.ac.uk/"/> <foaf:workInfoHomepage rdf:resource="http://kmi.open.ac.uk/people/index.cfm?id=167"/> <foaf:depiction rdf:resource="http://kmi.open.ac.uk/img/members/Sofia4F8E0276.jpg"/>

7. Charting the web

8. Charting the web (2)

9. Increasing Semantic Content <rdf:RDF> <Feature rdf:about="http://sws.geonames.org/2638049/"> <name>Shenley Church End</name> <alternateName>Shenley</alternateName> <inCountry rdf:resource="http://www.geonames.org/countries/#GB"/> </rdf:RDF>

10. The Rise of Semantics

11. Thesis #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 In other words: the Semantic Web is no longer an aspiration but a reality The availability of such large scale amounts of formalised knowledge is unprecedented in the history of AI

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

13. Knowledge Representation Hypothesis in AI 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

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

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

17. SW as Enabler of Intelligent Behaviour Intelligent Behaviour

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. Architecture of NGSW Apps Semantic Web Gateway

21. Issue: Semantic Web Infrastructure

22. Current Gateway to the Semantic Web

23. Limitations of Swoogle • Limited quality control mechanisms • Many ontologies are duplicated • Limited Query/Search mechanisms • Only keyword search; no distinction between types of elements • No support for formal query languages (such as SPARQL) • Limited range of ontology ranking mechanisms • Swoogle only uses a 'popularity-based' one • Limited API • No support for ontology modularization

24. A New Gateway to the Semantic Web http://watson.kmi.open.ac.uk

25. Sophisticated quality control mechanism • Detects duplications • Fixes obvious syntax problems • E.g., duplicated ontology IDs, namespaces, etc.. • Structures ontologies in a network • Using relations such as: extends, inconsistentWith, duplicates • Provides sophisticated API • Supports formal queries (SPARQL) • Variety of ontology ranking mechanisms • Modularization support • Very cool logo!

26. Examples of Next Generation Semantic Web Applications

27. Example #1: Ontology Matching

28. 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

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

30. 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.

31. 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

32. 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

33. How to combine online ontologies to derive mappings?

34. 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

35. 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

36. 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

37. 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)

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

39. Chart 2

40. Thesis #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 heterogeneous resource, has been successfully used to address a real-world problem

41. Example #2: Integrating SW and Web2.0

42. Features of Web2.0 sites • Tagging as opposed to rigid classification • Dynamic vocabulary does not require much annotation effort and evolves easily • Shared vocabulary emerge over time • certain tags become particularly popular

43. Limitations of tagging • Different granularity of tagging • rome vs colosseum vs roman monument • Flower vs tulip • Etc.. • Multilinguality • Spelling errors, different terminology, plural vs singular, etc… • This has a number of negative implications for the effective use of tagged resources • e.g., Search exhibits very poor recall

44. Giving meaning to tags

45. 2. Linking two "SW tags" using semantic relations {japan, asia} <japan subRegionOf asia> What does it mean to add semantics to tags? 1. Mapping a tag to a SW element "japan" <akt:Country Japan>

46. Applications of the approach • To improve recall in keyword search • To support annotation by dynamically suggesting relevant tags or visualizing the structure of relevant tags • To enable formal queries over a space of tags • Hence, going beyond keyword search • To support new forms of intelligent navigation • i.e., using the 'semantic layer' to support navigation

47. Pre-processing Tags Clean tags Group similar tags Filter infrequent tags Concise tags Folksonomy Clustering Analyze co-occurrence of tags Co-occurence matrix Cluster tags Cluster1 Cluster2 … Clustern • Concept and relation identification Yes SW search engine 2 “related” tags Remaining tags? Wikipedia Find mappings & relation for pair of tags No Google END <concept, relation, concept>

48. Experiments Scope: Subsets of Flickr and del.icio.us tags. Pre-processing (thresholds): To be “similar”, Levenshtein >= 0.83; A tag has to occur at least 10 times.

49. Examples Information Object archive in-event has-mention-of event resource partici- patesIn applica- tion participant activity typeRange user component creator admin interface example innovation planning developer

50. Examples activities4 education learning4 teaching4 training1,4 school2 qualification corporate1 postSecondary School2 institution studiesAt college2 student3 university2,3 takesCourse offersCourse course3 1http://gate.ac.uk/projects/htechsight/Employment.daml. 2http://reliant.teknowledge.com/DAML/Mid-level-ontology.daml. 3http://www.mondeca.com/owl/moses/ita.owl. 4http://www.cs.utexas.edu/users/mfkb/RKF/tree/CLib-core-office.owl.