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Navigation-induced Knowledge Engineering by Example


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Talk for the research paper @ Jist 2012

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Navigation-induced Knowledge Engineering by Example

  1. 1. Creating Knowledge out of Interlinked Data JIST 2012 – Page 1 http://lod2.euNavigation-induced Knowledge Engineering by Example (NKE) Sebastian Hellmann, Jens Lehmann, Jörg Unbehauen, Claus Stadler, Thanh Nghia Lam, Markus Strohmaier AKSW, Universität Leipzig LOD2 Presentation . 02.09.2010 . Page
  2. 2. JIST 2012 – Page 2 Problem 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 reasonable good result?• With how many Domain experts do I have to communicate to reach consensus?
  3. 3. JIST 2012 – Page 3
  4. 4. JIST 2012 – Page 4 How many Ontology Engineers are necessary 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. 5. JIST 2012 – Page 5 NKENavigation-induced Knowledge Engineering by Example
  6. 6. JIST 2012 – Page 6 NKE 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 to infer an initial (seed) set of positive and negative examples.II. Iterative Feedback: NKE supports users in interactively refining the seed set of examples such that the final set of objects satisfies the users’ intentIII.Retention: NKE allows users to retain previously explored sets of objects by grouping them and saving them for later retrieval.
  7. 7. JIST 2012 – Page 7 Future Work: DRUNKE = Drupal + NKE
  8. 8. JIST 2012 – Page 8 Overview● Current prototype for NKE● Introduction to DL-Learner● Show more GUIs and Mockups● Evaluation
  9. 9. JIST 2012 – Page 9 Current NKE prototype
  10. 10. JIST 2012 – Page 10 HANNE –
  11. 11. JIST 2012 – Page 11 HANNE –
  12. 12. JIST 2012 – Page 12 GUIs Start Learning with DL-Learner
  13. 13. JIST 2012 – Page 13 DL-LearnerDL-Learner is a tool for learning concepts in Description Logics (DLs) from user-provided examples.
  14. 14. JIST 2012 – Page 14 Introduction DL-Learner
  15. 15. JIST 2012 – Page 15 Introduction DL-Learner Good properties for active learning: - Biased towards high recall - Scales well: Number of training examples is more important than the size of the background knowledgeDidier Cherix, Sebastian Hellmann und Jens Lehmann:Improving the Performance of a SPARQL Component for Semantic Web ApplicationsIn: JIST 2012
  16. 16. JIST 2012 – Page 16 Introduction DL-Learner
  17. 17. JIST 2012 – Page 17 GUIs Northeast football league south
  18. 18. JIST 2012 – Page 18 HANNE –
  19. 19. JIST 2012 – Page 19 GUIs With only 2 positives and 4 negatives, it is possible to find 13 more instances, which are football clubs situated close to Saxony, Germany Possible to add more positives and complete the list
  20. 20. JIST 2012 – Page 20 VisionIntegrate NKE processes seamlessly into existing applications
  21. 21. JIST 2012 – Page 21 GUIsdbo:President and dbo:geoRelated value United_States and dbo:spouse some ThingRetrieves 42 of 44 instances → acceptable intensional definition
  22. 22. JIST 2012 – Page 22 GUIs
  23. 23. JIST 2012 – Page 23 GUIs
  24. 24. JIST 2012 – Page 24 GeizhalsSofter criteria: Retention / “Saving” is replaced by a hit count on the concept, which is anavigation suggestion (popularity)
  25. 25. JIST 2012 – Page 25 Evaluation• Based on Wikipedia Categories(1) the categories can be considered a hierarchical structure to more effectively group 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, which means that the data contains errors from a supervised learning point of view.• list of 98 categories from DBpedia, which contained exactly 100 members that had an infobox as well as an abstract property
  26. 26. JIST 2012 – Page 26 Keyword 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. 27. JIST 2012 – Page 27 Keyword search vs. DL-LearnerKeyword search Limit = Based on the assumption that a user only looks at the first 20, 100, 200 examples
  28. 28. JIST 2012 – Page 28 Keyword 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. 29. JIST 2012 – Page 29 Keyword search vs. DL-LearnerQuantitative results
  30. 30. JIST 2012 – Page 30 Qualitative ResultsDetailed results are available at
  31. 31. JIST 2012 – Page 31 Qualitative Results - Examples
  32. 32. JIST 2012 – Page 32 Qualitative 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. 33. JIST 2012 – Page 33 Qualitative 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. 34. JIST 2012 – Page 34 Qualitative 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. 35. JIST 2012 – Page 35 Qualitative 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. 36. JIST 2012 – Page 36 Conclusions• Definition of the NKE paradigm• Proof of concept implementation • Technical feasibility • Web Demo:• We have made progress to bridge the gap between user interaction and knowledge engineering
  37. 37. JIST 2012 – Page 37 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