1. Creating Knowledge out of Interlinked DataJIST 2012 Page 1 http://lod2.euNavigation-induced Knowledge Engineering by Example (NKE) Sebastian Hellmann, Jens Lehmann, Jrg Unbehauen, Claus Stadler, Thanh Nghia Lam, Markus Strohmaier http://slideshare.net/kurzumhttp://aksw.org/Projects/NKEhttp://lod2.eu AKSW, Universitt LeipzigLOD2 Presentation . 02.09.2010 . Pagehttp://lod2.eu

2. JIST 2012 Page 2 http://lod2.euProblem descriptionWhy is there a Knowledge Acquisition Bottleneck?Questions you might ask an Ontology Engineer: What is the purpose of my Ontology? For which application is it created? What are sensible categories? How do I design the concept hierarchy to be useful for browsing? How do I use my resources efficiently, yet still produce a reasonablegood result? With how many Domain experts do I have to communicate to reachconsensus? 3. JIST 2012 Page 3 http://lod2.eu 4. JIST 2012 Page 4 http://lod2.euHow many Ontology Engineers arenecessary to structure 31 Billion Facts? Who will guard the guards? Does their schema fit my use case?What kind of schemas do we need to effectively query and browse this data? 5. JIST 2012 Page 5 http://lod2.euNKENavigation-induced Knowledge Engineering by Example 6. JIST 2012 Page 6http://lod2.euNKE MethodologyBased on the idea that each information need of a user might be apotential ontological concept (set of instances)Search Ontological ConceptThere are three steps involved:I. Navigation: NKE starts by interpreting navigational behavior of users toinfer an initial (seed) set of positive and negative examples.II. Iterative Feedback: NKE supports users in interactively refining the seedset of examples such that the final set of objects satisfies the usersintentIII.Retention: NKE allows users to retain previously explored sets of objectsby grouping them and saving them for later retrieval. 7. JIST 2012 Page 7http://lod2.eu Future Work: DRUNKE = Drupal + NKE 8. JIST 2012 Page 8http://lod2.euOverviewCurrent prototype for NKEIntroduction to DL-LearnerShow more GUIs and MockupsEvaluation 9. JIST 2012 Page 9 http://lod2.eu Current NKE prototype 10. JIST 2012 Page 10http://lod2.eu HANNE http://hanne.aksw.org 11. JIST 2012 Page 11http://lod2.eu HANNE http://hanne.aksw.org 12. JIST 2012 Page 12http://lod2.eu GUIsStart Learning with DL-Learner 13. JIST 2012 Page 13http://lod2.euDL-LearnerDL-Learner is a tool for learning concepts in Description Logics (DLs) from user-provided examples. 14. JIST 2012 Page 14http://lod2.eu Introduction DL-Learner 15. JIST 2012 Page 15 http://lod2.eu Introduction DL-LearnerGood properties for active learning:- Biased towards high recall- Scales well: Number of training examples ismore important than the size of thebackground knowledgeDidier Cherix, Sebastian Hellmann und Jens Lehmann:Improving the Performance of a SPARQL Component for Semantic Web ApplicationsIn: JIST 2012 16. JIST 2012 Page 16http://lod2.eu Introduction DL-Learner 17. JIST 2012 Page 17 http://lod2.eu GUIsNortheast football league south 18. JIST 2012 Page 18http://lod2.eu HANNE http://hanne.aksw.org 19. JIST 2012 Page 19 http://lod2.eu GUIsWith only 2 positives and 4 negatives,it is possible to find 13 more instances, which arefootball clubs situated close to Saxony, GermanyPossible to add more positives and complete thelist 20. JIST 2012 Page 20 http://lod2.eu VisionIntegrate NKE processes seamlessly into existing applications 21. JIST 2012 Page 21http://lod2.eu GUIsdbo:President and dbo:geoRelated value United_States and dbo:spouse some ThingRetrieves 42 of 44 instances acceptable intensional definition 22. JIST 2012 Page 22 http://lod2.eu GUIs 23. JIST 2012 Page 23 http://lod2.eu GUIs 24. JIST 2012 Page 24 http://lod2.euGeizhalsSofter criteria: Retention / Saving is replaced by a hit count on the concept, which is anavigation suggestion (popularity) 25. JIST 2012 Page 25 http://lod2.eu Evaluation Based on Wikipedia Categories(1) the categories can be considered a hierarchical structure to more effectivelygroup and browse Wikipedia articles(2) the categories are maintained manually (which is very tedious and time-consuming)(3) they do not enforce a strict is-a relation to their member articles, whichmeans that the data contains errors from a supervised learning point ofview. list of 98 categories from DBpedia, which contained exactly 100 membersthat had an infobox as well as an abstract property 26. JIST 2012 Page 26http://lod2.euKeyword search vs. DL-LearnerKeyword search Find all Wrestlers at the 1938 British Empire Games { {Wrestler, 1938, British, Empire, Game}, {Wrestler, 1938, British, Empire}, {Wrestler, 1938, British, Game}, {Wrestler, 1938, Empire, Game}, } Total of 31 searches for five words (Power set minus the empty word) 27. JIST 2012 Page 27http://lod2.eu Keyword search vs. DL-LearnerKeyword search Limit = Based on the assumption that a user only looks at the first 20, 100, 200 examples 28. JIST 2012 Page 28 http://lod2.euKeyword search vs. DL-LearnerDL-Learner Used same metrics 5 randomly selected positive seed instances from the category (navigation history, string search or facet-based browsing ) 5 negatives from parallel sister categories (with same predecessor) 5 iterations (with a total of 25 positives and negatives) 29. JIST 2012 Page 29http://lod2.eu Keyword search vs. DL-LearnerQuantitative results 30. JIST 2012 Page 30http://lod2.eu Qualitative ResultsDetailed results are available at http://aksw.org/Projects/NKE 31. JIST 2012 Page 31 http://lod2.eu Qualitative Results - Examples 32. JIST 2012 Page 32http://lod2.euQualitative Results - Examples Single feature concepts Easy to learn If added as intensional definition, e.g. by an admin, they can help to identify errors and missing values in the database Automatically classify new instances 33. JIST 2012 Page 33 http://lod2.euQualitative Results - Examples Overly specific concepts Partially correct, Defoe is in Bay City, Michigan 53 of 100 matched Data inspection showed URIs as well as literals as objects 34. JIST 2012 Page 34 http://lod2.euQualitative Results - Examples Indirect solution concepts Read like paraphrases no feature (e.g. champion value US_Open) SubdividisonName is more frequently used by US cities in DBpedia 35. JIST 2012 Page 35http://lod2.euQualitative Results - Examples Zero member concepts Northland region is not a clear is-a relation, but rather a tag Second one does not have any good features in the data 36. JIST 2012 Page 36 http://lod2.eu Conclusions Definition of the NKE paradigm Proof of concept implementation Technical feasibility Web Demo: http://hanne.aksw.org We have made progress to bridge the gap between user interaction andknowledge engineering 37. JIST 2012 Page 37http://lod2.eu Future Work & Open Questions For which purpose can concepts created by users be exploited: Improve Navigation via suggestions or hierarchial browsing Create domain ontologies Create a GUI for different target groups: End-users Domain experts with some technical skill Further evaluation necessary, please contact us for collaborations Project page is http://aksw.org/Projects/NKEhttp://slideshare.net/kurzum