1. Towards a new generation of semantic web applications Prof. Enrico Motta, PhD Knowledge Media Institute The Open University Milton 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. Enrico Motta kmi director enrico motta Enrico Motta e.motta@open.ac.uk <akt:hasHomePage rdf:resource="http://kmi.open.ac.uk/people/motta/"/> Person Organization String Organization-Unit partOf hasAffiliation worksInOrgUnit hasJobTitle Ontology Metadata

5. Ontology Metadata 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)

17. 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

19. 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

20. Intelligence as a function of possessing domain knowledge Large Body of Knowledge Intelligent Behaviour KA Bottleneck

21. The Knowledge Acquisition Bottleneck Large Body of Knowledge Intelligent Behaviour KA Bottleneck Knowledge

23. SW as Enabler of Intelligent Behaviour Intelligent Behaviour

24. KBS vs SW Systems Classic KBSSW Systems Representation'Clean''Dirty' SizeSmall/MediumExtra Huge Repr. SchemaHomogeneousHeterogeneous QualityHighVery Variable Degree of trustHighVery Variable

25. Key Paradigm Shift Classic KBSSW Systems IntelligenceA function of sophisticated, task-centric problem solving A side-effect of size and heterogeneity (Collective Intelligence)

26. 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

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

28. 1 0.9 1 –Label similarity methods e.g., Full_Professor = FullProfessor Ontology Matching 0.5 –Structure similarity methods Using taxonomic/property related information

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

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 AB rel + OnlineDictionary IR Methods Precision increases significantly if domain specific sources are used: 50% - Web; 75% - domain texts. Does not rely on a rich Domain Ont,

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

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. 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’:

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

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. 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. 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.

37. 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)

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

39. Chart 2

40. 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

41. 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

42. 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 Contrast with 1st generation SW Applications 1st generation SW applications are far more similar to traditional KBS (closed semantic systems) than to 'real' SW applications (open semantic systems)

43. 18952007 It is still early days..

44. Current Gateway to the Semantic Web

45. 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

46. A New Gateway to the Semantic Web

47. Ontology Structuring Relations extends inconsistent-with

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

49. Formal Queries and relation discovery…

50. Current state of Watson Initial version implemented Demo version available online –See http://watson.kmi.open.ac.uk/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….

51. Some initial figures… Lots of ontologies are in OWL FULL (3x the number of OWL Lite) … but most of the ontologies use only a very restricted sub-part of the expressivity of OWL and DAML, e.g., –only 147 go beyond ALC –role transitivity is used in only 11 ontologies…….. Almost 20% of semantic resources appear to be duplicates

52. Next Generation Semantic Web Applications PowerMagpie PowerAqua

53. Folksonomies Tags are great to organize data!!! But they don’t help much when searching…

54. Finding tagged images Flower Rose Lilac Flower Tulip Flowers CutFlower Tulip

55. Flower Rose Lilac Flower Tulip Flowers CutFlower Tulip Finding tagged images – FLOWER

56. What if … Rose Tulip Flower Lilac …folksonomies were semantically richer

57. Flower Rose Lilac Flower Tulip Flowers CutFlower Tulip Finding tagged images – FLOWER (II) Rose Tulip Flower Lilac

58. Learning Relations Between Tags Tags {camera, digital, photograph} {damage, flooding, hurricane, katrina, Louisiana} Clusters digital cameraphotograph takenWith Ontology NLP/Clustering Find and combine Online ontologies L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", ESWC 2007.

59. In More Detail…

60. Examples

63. Key Research Tasks Overall Infrastructure –crawling, storing, structuring, querying the SW Ontology Selection –In the context of dynamically identifying the sources of knowledge relevant to the needs of a system Ontology Mapping –When integrating information from different ontologies –When mapping query/specs to ontologies Ontology Modularization –Find the sub-modules relevant to a system's query. Semantic Markup Generation –From various types of sources

64. New task context Key point is that NG-SW applications require solutions in a new dynamic context (run-time rather than design-time) –Example: Ontology Mapping Much 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

65. References 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 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 Watson –d’Aquin, M., Sabou, M., Dzbor, M., Baldassarre, C., Gridinoc, L., Angeletou, S. and Motta, E.: "WATSON: A Gateway for the Semantic Web". Poster Session at ESWC 2007

66. References (2) Ontology Mapping –Lopez, V., Sabou, M., Motta, E. (2006). "Mapping the real semantic web on the fly". ISWC 2006 –Sabou, M., D'Aquin, M., Motta, E. (2006). "Using the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping. Intg. of folksonomies and SW –L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", ESWC 2007.

67. '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).

68. Conclusions SW provides an unprecedented opportunity to build a new generation of intelligent systems, able to exploit large scale, heterogeneous KBs This new class of systems is fundamentally different in many respects both from traditional KBS and even from early SW applications The size of the SW is increasing steadily and the infrastructure is getting more and more robust. These developments should enable more and more new generation SW applications to emerge within 2-3 years

70. Current Gateway to the Semantic Web