Controlled Natural Languages for Knowledge Capture

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Talk given at the Intelligence Augmentation Forum, a seminar at the School of Computing, University of Leeds. The talk is the third in a series where we are looking at several recently published approaches for capturing human knowledge. The talk focuses on Controlled Natural Languages: what they are, design issues of CNLs and how they relate to knowledge capture.

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Controlled Natural Languages for Knowledge Capture

  1. 1. Introduction Controlled Natural Languages Conclusion Controlled Natural Languages for Knowledge Capture Intelligence Augmentation Forum R. Denaux School of Computing University of Leeds Leeds, 7th of June 2010 Denaux Controlled Natural Languages
  2. 2. Introduction Controlled Natural Languages Conclusion Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  3. 3. Introduction Context Controlled Natural Languages Problem Conclusion Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  4. 4. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  5. 5. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  6. 6. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  7. 7. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  8. 8. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  9. 9. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing? Denaux Controlled Natural Languages
  10. 10. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing in OWL terminology? Denaux Controlled Natural Languages
  11. 11. Introduction Context Controlled Natural Languages Problem Conclusion Knowledge Capture What are we capturing in OWL terminology? Denaux Controlled Natural Languages
  12. 12. Introduction Context Controlled Natural Languages Problem Conclusion Why do we need it? Intelligence Augmentation Denaux Controlled Natural Languages
  13. 13. Introduction Context Controlled Natural Languages Problem Conclusion Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  14. 14. Introduction Context Controlled Natural Languages Problem Conclusion Formalisation of Knowledge People have the real knowledge (sometimes encoded in documents, wikis, databases, etc.) Knowledge formalisation is not trivial and needs understanding of logical formalism: 1st order logic, OWL, etc. Denaux Controlled Natural Languages
  15. 15. Introduction Context Controlled Natural Languages Problem Conclusion Wikis Conceptual Knowledge: Yes Factual Knowledge: Yes However: Not formal Knowledge extraction results are limited, despite semi-structured format Denaux Controlled Natural Languages
  16. 16. Introduction Context Controlled Natural Languages Problem Conclusion Semantic Wikis Conceptual Knowledge: Yes, but cumbersome Factual Knowledge: Yes Denaux Controlled Natural Languages
  17. 17. Introduction Context Controlled Natural Languages Problem Conclusion Ontology Engineering Conceptual Knowledge: Yes Factual Knowledge: Yes However: Requires knowledge elicitation phase Results are difficult to understand by domain experts Denaux Controlled Natural Languages
  18. 18. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  19. 19. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Unfortunately... Denaux Controlled Natural Languages
  20. 20. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Unfortunately... Denaux Controlled Natural Languages
  21. 21. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Unfortunately... Denaux Controlled Natural Languages
  22. 22. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Unfortunately... Denaux Controlled Natural Languages
  23. 23. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Unfortunately... Denaux Controlled Natural Languages
  24. 24. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Constraints Every CNL is a kind of Engineerd Language Denaux Controlled Natural Languages
  25. 25. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Constraints Every CNL is a kind of Engineerd Language Optional Constraints A CNL usually can be translated into a Logical Formalism Denaux Controlled Natural Languages
  26. 26. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Constraints Every CNL is a kind of Engineerd Language Optional Constraints A CNL Sentence usually has an Unambiguous Logical Translation Denaux Controlled Natural Languages
  27. 27. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo What is a Controlled Natural Language(CNL)? Constraints Every CNL is a kind of Engineerd Language Optional Constraints A CNL Sentence usually is an Easy-to-Learn Language Denaux Controlled Natural Languages
  28. 28. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Broad subdivision of CNLs Simplified NL vs Formally Underpinned CNLs Every Simplified NL is a kind of CNL. cannot be directly translated into a Logical Formalism is used to increase quality of documents easier to understand by humans (eg non-natives) easier to process by machines defined by a set of restrictions on the language: limited set of words to use disallowed constructs (eg: passive voice) eg: Simple English is a Simplified NL. Caterpillar Technical English is a Simplified NL. Denaux Controlled Natural Languages
  29. 29. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Broad subdivision of CNLs Simplified NL vs Formally Underpinned CNLs Every Formally Underpinned CNL is a kind of CNL. has a semantic mapping into a Logical Formalism is used to formalise knowledge easier (than logic formalism) to understand by humans directly processable by machines defined by a formal grammar eg: ACE, PENG, CPL(By BOEING), Common Logic Controlled English and Rabbit are all Formally Underpinned CNLs. Denaux Controlled Natural Languages
  30. 30. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  31. 31. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Balancing Expressivity and Parseability Limiting statements per sentence NL: To describe in an unambiguous manner the inland hydrology feature classes surveyed by Ordnance Survey with the intention of improving the use of the surveyed data by our customers and enabling semi-automatic processing of these data. CNL: Ontology describes the OS Inland Hydrology Feature Classes. CNL: Ontology aims to improve Data Usage Of OS Data. CNL: Ontology aims to enable Semi-automatic Processing of OS Data. etc. Denaux Controlled Natural Languages
  32. 32. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Balancing Expressivity and Parseability Anaphoric Reference 1 A pilot does not have a valid license. 2 It is expired. Denaux Controlled Natural Languages
  33. 33. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Capturing the formal semantics Subjunction An Actor is a Person. (Actor == Person?) Every Actor is a kind of Person. Denaux Controlled Natural Languages
  34. 34. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Capturing the formal semantics Relation vs Definition Every River flows into a Sea A River is anything that: is a kind of Body of Water; flows into a Sea. Denaux Controlled Natural Languages
  35. 35. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Capturing the formal semantics Property Domain The relationship “flows into” must have the subject River. Everything that “flows into” something is a River. Denaux Controlled Natural Languages
  36. 36. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Limiting Ambiguity Lists Every River flows into a Sea or a Lake. Every River flows into a Sea or flows into a Lake Denaux Controlled Natural Languages
  37. 37. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Limiting Ambiguity Lists with cardinality restrictions Every River flows into exactly 1 Sea or Lake. Denaux Controlled Natural Languages
  38. 38. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Limiting Ambiguity Concept or Relationship? Every River flows into a Sea. Every River Flow has a direction. Denaux Controlled Natural Languages
  39. 39. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  40. 40. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Discourse Representation Structure First Order Logic PENG ACE Denaux Controlled Natural Languages
  41. 41. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Lightweight NLP CLoNE Rabbit Denaux Controlled Natural Languages
  42. 42. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Outline 1 Introduction Context Problem 2 Controlled Natural Languages Definition Design Issues Implementation Demo 3 Conclusion Denaux Controlled Natural Languages
  43. 43. Definition Introduction Design Issues Controlled Natural Languages Implementation Conclusion Demo Denaux Controlled Natural Languages
  44. 44. Introduction Controlled Natural Languages Conclusion Typical Usage of Novice User See example sentences to get a feeling for the language Write sentence Get feedback and improve sentence until correctly parsed Denaux Controlled Natural Languages
  45. 45. Introduction Controlled Natural Languages Conclusion How to Evaluate a CNL Use a made up world with made up concepts Use a “Controlled Visual Language” Denaux Controlled Natural Languages
  46. 46. Introduction Controlled Natural Languages Conclusion Knowledge Capture with CNLs Conceptual Knowledge: Yes Factual Knowledge: Yes However: No guarantee that conceptual knowledge is directly usable Is OWL(or 1st Order Logics) correctly understood? Denaux Controlled Natural Languages
  47. 47. Introduction Controlled Natural Languages Conclusion ROO: Rabbit to OWL Ontology Authoring. Example of adapting to ontology contributors Domain experts: Good knowledge of the domain to be represented Limited or no Ontology Engineering experience Limited or no knowledge of OWL, Protégé, etc. ROO provides tool support for domain experts: Guidance through ontology construction methodology Controlled Natural Language interface No OWL specific terminology Adaptation at design time, not at runtime Re-use techniques from User Modelling and Personalisation Denaux Controlled Natural Languages
  48. 48. Introduction Controlled Natural Languages Conclusion ROO: Rabbit to OWL Ontology Authoring. Example of adapting to ontology contributors Denaux Controlled Natural Languages
  49. 49. Introduction Controlled Natural Languages Conclusion ROO: Rabbit to OWL Ontology Authoring. Example of adapting to ontology contributors Denaux Controlled Natural Languages
  50. 50. Introduction Controlled Natural Languages Conclusion ROO: Rabbit to OWL Ontology Authoring. Example of adapting to ontology contributors Denaux Controlled Natural Languages
  51. 51. Introduction Controlled Natural Languages Conclusion Acknowledgements 3rd Party Images used: http://shlomit.deviantart.com/art/ Keanu-Reeves-portrait-118051274 http://owen-c.deviantart.com/art/ Paillard-Bolex-H16-48443994 http://eliskan.deviantart.com/art/ World-Map-127501551 http://so-aesthetic.deviantart.com/art/ Drama-Masks-49646555 Denaux Controlled Natural Languages

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