Invited talk at the EPIA 2011 conference

1. Doing Clever Things With the Semantic Web Mathieu d’Aquin Knowledge Media Institute, the Open University

2. The Semantic Web Using the Web to publish, share and exploit information/knowledge From machines to machines Using graph-based data modeling, knowledge representation (ontologies) and reasoning

3. Linked Data http://lucero-project.info/lb/what-is-linked-data/ As set of principles and technologies for a Web of Data Putting the “raw” data online in a standard representation (RDF) Make the data Web addressable (URIs) Link to other Data http://linkeddata.org

4. Galen NCI … Music DC WORDNET RSS TAP FOAF … … … … Metadata <rdf:RDF> <channel rdf:about=“http://watson.kmi.open.ac.uk/blog”> <title>Elementaries - The Watson Blog</title> <link>http://watson.kmi.open.ac.uk:8080/blog/</link> <description> "Oh dear! Where the Semantic Web is going to go now?" -- imaginary user 23 </description> <language>en</language> <copyright>Watson team</copyright> <lastBuildDate>Thu, 01 Mar 2007 13:49:52 GMT</lastBuildDate> <generator>Pebble (http://pebble.sourceforge.net)</generator> <docs>http://backend.userland.com/rss</docs> … <rdf:RDF> <foaf:Imagerdf:about='http://static.flickr.com/132/400582453_e1e1f8602c.jpg'> <dc:title>Zen wisteria</dc:title> <dc:description></dc:description> <foaf:pagerdf:resource='http://www.flickr.com/photos/xcv/400582453/'/> <foaf:topicrdf:resource='http://www.flickr.com/photos/tags/vittelgarden/'/> <foaf:topicrdf:resource='http://www.flickr.com/photos/tags/wisteria/'/> <dc:creator> <foaf:Person><foaf:name>Mathieu d'Aquin</foaf:name> … <rdf:RDF> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/> </owl:Ontology> <j.1:Organization rdf:ID="KMi"> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment> </j.1:Organization> <j.1:Document rdf:ID="KMiWebSite"> … UoD

5. The Semantic Web Knowledge/ Problem Solving Methods Semantic Web Applications Doing Clever Things With the Semantic Web Intelligent Agent Smart Features Clever Things…

6. What I Want to Talk About Using the Semantic Web as A Knowledge Base KMi Watson and Finding Ontologies Doing More with Links Exploring the Web of Data Back to the Future AI + Linked Data = Semantic Web?

7. Using the Semantic Web Need for a Gateway to the Semantic Web Dynamically retrieving, exploiting and combining relevant semantic resources from the Semantic Web

8. KMi Watson

9. Architecture

10. Interface http://watson.kmi.open.ac.uk Watson: More than a Semantic Web Search Engine, Semantic Web Journal

11. Watson as a Service Providing Web accessible APIs to a collection of online ontologies and semantic data sources

12. Chose an entity to search Integrate statements Into the edited ontology Get entities from online ontologies Example Application: Ontology Construction Reusing Knowledge from the Semanrtic Web with the Watson Plugin, Demo at ISWC 2008

13. Concept Relation Discovery SeaFood Meat wine.owl AcademicStaff Semantic Web Semantic Web Researcher ka2.rdf Meat SeaFood Ham pizza-to-go NALT AcademicStaff Researcher Ham SeaFood ISWC SWRC NALT Agrovoc

14. Exploring the Semantic Web as Background Knowledge for Ontology Matching, Journal of Data Semantics

15. PowerAqua: Question Answering

16. From Semantic Web Research to Linked Data Applications Watson as a platform to research applications and techniques on top of semantic web resources But how can the Semantic Web be exploited and used in real-world application? Starting from what we know best…

17. Applying Linked Data The Open University is the largest University in the UK, where all the courses are realized at a distance Creating the first University Linked Data platform: data.open.ac.uk Demonstrate the value of the technology and push the research through real-world scenarios

18. data.open.ac.uk

19. Applications Mobile and Personal Semantics Social Resource Discovery Research Exploration

20. Example application: Finding relevant resources Zablithet al, LinkedLearning 2011

21. Data as Web resources, accessible everywhere

22. See also: Zablith et al., COLD 2011

23. http://people.kmi.open.ac.uk/mathieu/about/discobro-discovering-linked-datat-resources-while-browsing/ http://discovery.ac.uk/developers/competition/

24. Supporting Researchers: The Reading Experience Database http://www.open.ac.uk/Arts/reading/ 40,000 accounts of somebody reading something at some time in some place Used by researchers in literature and history to explore research hypotheses

25. Event Location locatedIn subClassOf subClassOf Experience City Country date: Date readerInvolved originCountry textInvolved occupation givesBackgroundTo Person religion gender creator/editor LinkedEvent Ontology Document CITO Citation Ontology Dublin Core title: String description: String published: Date providesExcerptFor FOAF DBPedia

27. Back to the Future The Semantic Web is both a vision and a reality Making the Web more than a network of documents: the biggest, most distributed knowledge base ever What could AI do with such a knowledge base?

28. Linked Data Mining Finding unexpected patterns in the use of the distributed data graph

29. Linked Data Mining: Example Using Formal Concept Analysis + Reasoning to build a hierarchy of questions a linked dataset can answer Use statistical metrics to identify the ones that are most likely to be interesting Extracting Relevant Questions to an RDF Dataset Using Formal Concept Amalysis at KCAP 2011 http://lucero-project.info/lb/2011/06/what-to-ask-linked-data/

32. Reasoning To analyze and understand raw data in relation with online resources Example: Online personal information management Online Activities Ontology HTTP Ontology Parameters and Website info. Web Site Information Personal Information Trust Model Location Information

33. Enriched with linked data Google Services Entertainment Websites Web Analytics Internet Search Engine subject/category Video sharing Video Hosting www.google-analytics.com Company developer Web Search Engine Search Engine type subject/category google owner subsediaryOf www.youtube.com www.google.com parent DBpedia freebase

34. Basic processing/analysis Requests by time of day Requests by User Agents Interests Trust

35. http://uciad.info http://uciad.info

36. Understanding knowledge representation and data modeling The Semantic Web also represents a very large, collaborative base of formally represented knowledge This can also be mined, to discover things about knowledge representation and data modeling

37. Ontologies on the Semantic Web Underlying description logic Number of entities Domain covered

38. Relationships between ontologies DOOR: Towards a Formalization of Ontology Relations at KEOD 2009

39. Detecting versions of ontologies When published on the Web, the information about the evolution of ontologies is lost Using URI patterns to find candidate versions of ontologies http://loki.cae.drexel.edu/wbs/ontology/2003/10/iso-metadata http://loki.cae.drexel.edu/wbs/ontology/2004/01/iso-metadata Applying machine learning algorithms (SVM, Naïve Bayes and Decision tree to recognize chains of versions of ontologies Allocca at ESWC 2011 Obtained 90% Precision (SVM) Collected thousands of ontology version sequences to be analysed For example, distribution of similarity in version and non-version ontologies (right)

40. Agreement/Disagreement between ontologies Ontologies are knowledge artifacts, they express opinions and beliefs and contradict each others Assessing (dis)agreement in ontologies is very useful to understand how to combine knowledge from different sources

41. Assessing Statements related to SeaFood Nb1: #ontologies in which the statement appears.Nb2: #ontologies containing entities matching the subject and object of the statement. a: global agreement, d: global disagreement, cs: consensus, ct: controversy

42. 21 different ontologies with a SeaFood concept Disagreement Agreement Formally Measuring Agreement and Disagreement in Ontologies at K-CAP 2009

43. http://uciad.info Vegan subClass Vegetarian SeaFoodsubClassOf Meat SeaFooddisjointWith Meat

44. The brighter the blue the higher the positive consensus (higher agreement) The brighter the red the lower the negative consensus (higher disagreement) Dark = controversy: no clear cut between disagreement and agreement Example: The statements attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles) AKT Portal Using consensus to assess an ontology(a new NeOn toolkit plugin Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development at ONTOQUAL@EKAW 2010

45. So my point is… The Semantic Web is a fantastic open field for AI It is going to become omnipresent, hidden, personal Exploring, Exploiting and Excavating the Semantic Web for Research in technology (creating it and studying it) Research in other areas Everyday tasks Still, after 10 years of research, represent new directions for many fields of the AI community, with their own issues, challenges and applications

46. Thank You! More at: http://people.kmi.open.ac.uk/mathieu m.daquin@open.ac.uk @mdaquin