Insemtive Prepayment Slideshare

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Insemtive Prepayment Slideshare

  1. 1. Tackling the Curse of Prepayment Collaborative Knowledge Formalization Beyond Lightweight Valentin Zacharias & Simone Braun
  2. 2. The Curse Of Prepayment The Curse Of Prepayment Semantic  Semantic Technologies promise  great functionality,  great functionality once a great amount  of knowledge is  formalized
  3. 3. FolksOntology lk l IkeWiki
  4. 4. Simple Collaborative Incremental Partial Immediate
  5. 5. Making Every Penny Count – Immediately  • Immediate benefit for formalizing  even small parts, becomes  ll t b justification for next part  – Tables, Hierarchical Organisation,  Advanced Search • D Datastore never fully formalized f ll f li d • Formalizing everything impossible
  6. 6. Text & & Informat tion Retrieval Tags & & Keyword ds Semi S Structured Dat (e.g g. Wiki) Taxonomies Thesa auri + Skos s Web 2.0 Ontology Engineering Structu ured Data (e.g. Free ebase) Rule La anguages OWL D DL The Problem of the Brick Wall The Problem of the Brick Wall FOL Ontologies
  7. 7. What Weight Problem? What Weight Problem? • Usability/Debuggability • Robustness • Performace • Language Expressivity Language Expressivity  vs. Performance – and  is this even the right is this even the right  question? • Mixed/ semi formality Mixed/ semi‐ Partly based on: Krötzsch, Schaffert, Vrandecic: Reasoning in Semantic Wikis
  8. 8. Curse Of Prepayment Again Curse Of Prepayment ‐ Again  • What is the goal, what is the ‘immediate  g p benefit of formalizing even small parts’? – Better retrieval? (e.g. clicking on a tag also returns  things annotated with subtags)  things annotated with subtags) – Better Navigation? (e.g. when I browse through  my bookmarks) my bookmarks) – Organization and Maintainability? (e.g. by defining  data only once and reusing it in tables) d t l d i it i t bl )
  9. 9. ‘Collaborative Incremental  Augmentation of Text Retrieval’
  10. 10. ‘Collaborative Incremental  Augmentation of Text Retrieval’
  11. 11. ‘Collaborative Incremental  Augmentation of Text Retrieval’
  12. 12. ‘Collaborative Incremental  Augmentation of Text Retrieval’
  13. 13. ‘Collaborative Incremental  Augmentation of Text Retrieval’
  14. 14. Collaborative, Incremental Augmentation of Text Retrieval = –S i E i f‘ Step wise Extension of ‘normal’ text retrieval in the direction of   l’ i l i h di i f Question Answering – Understanding the NL Query < > System query mapping also as Understanding the NL‐Query <‐> System query mapping also as  collaboratively matured artifacts.  Potential Advantages • Reasonable E pectations Expectations • Incremental and Partial • Immediate • Accepted Interface p Please see the paper for a bit more technical detail on how to realize this
  15. 15. • Collaboration: System functionality is  created during use by the users (not before  by programmers) by programmers) • Incremental: Functionality to answer  q questions is added step by step; back up  p y p; p information retrieval engine avoids   disappointment • Existing Queries: Not ‘speak to the Existing Queries: Not  speak to the  machine’, rather detection of queries done  anyway
  16. 16. • ‘Web2.0 Ontology gy Engineering’ tries to tackle  the Curse Of Prepayment  with a kind of KF where  ith ki d f KF h every bit of formalization  counts • Supporting more  heavyweight formalisms  y g entails many challenges, but  most important is the  finding of new answers to  fi di f t the Curse of Prepayment • ‘Collaborative Incremental Collaborative Incremental  Augmentation of Text  Thanks for your attention Retrieval’ could be one such  answer.  Valentin Zacharias V l ti Z h i Simone Braun

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