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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
 

Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks

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We propose a framework for ranking information based on quality, relevance and importance, and argue that a socio-semantic contextual approach that extends topicality can lead to increased value of ...

We propose a framework for ranking information based on quality, relevance and importance, and argue that a socio-semantic contextual approach that extends topicality can lead to increased value of information retrieval systems. We use Topic Maps to implement our framework, and discuss procedures for calculating the resource ranking. A fuzzy neural network approach is envisioned to complement the process of manual metadata creation.

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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks Presentation Transcript

  • Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions
  • Topic Maps 2008, Oslo Subject
  • Roy Lachica The author of FUZZZY Knowledge is fuzzy!
  • Sale in Fisherman’s World Norwegian economy Fishing Not all associations are relevant
  • Jigsaw puzzle idea of knowledge
  • Building Blocks of a Solution
    • Accumulate information on usefulness in a value matrix
    • Use user point of view or scope to estimate usefulness
    • Estimate usefulness by an algorithm
  • Value Matrix Criteria Ways of evaluating
  •  
  • Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions
  • Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions
  • Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions
  • Agenda
    • Goal
    • Project Scope
    • Problems
    • Limitations of Topic Maps
    • Information Retrieval
    • Proposed Solution
    • Partial Implementation on fuzzzy.com
  • The Ultimate Goal Getting the right information at the right time and place (Enhancing Information Retrieval systems)
  • Project Scope
    • A model based on: Topic Maps
    • For use in: Knowledge based systems
    • With a: Social collaborative environment
  • The Obstacles (TMRA-07)
    • Problems with large scale open collaborative Folktologies:
    • People use different terms/vocabularies
    • Language evolve over time
    • People mix different domains and different levels of discourse
    • People add errors and noise (overlapping, faulty or imprecise)
    • People have different views of what is important. (user-centric relevance)
    • People have different views of what things are relevant to each other. (topical relevance)
    • Topic Map Scopes
    • The above problems is problematic to solve with TM scopes Users don't share the same world view. Who decides the scopes?
  • Limitations of Topic Maps
    • Associations are not weighted: Flat information, no priority
    • No notion of the user and the context
    • Problems with representing a nuanced user context with Topic Maps scopes:
      • Explosion in number of TM constructs/assertions
      • High demands on computer processing
      • User context is something else than the domain and therefore would be somewhat misplaced in a Topic Map?
        • Export / fragment size explodes
  • Information Retrieval
    • The basic view in IR:
      • We have a set of resources and we want to retrieval only the most relevant ones
      • This view is to simple?
      • What we actually want is to retrieve the most valuable. The ones with the best quality which is relevant and important in our current context
  • Understanding Information
    • Relevance, Importance, Quality and Context are overlapping and ill defined
  • QRI
    • Measure for Information Retrival with Topic Maps:
    • Quality. The intrinsic value of an information resource as judged by an individual. (Unreliable or not understandable info is valueless, even if it may otherwise be highly relevant or important)
    • Relevance . Relevance is the strength of a relation between two subjects as judged by an individual in a given context. (Different persons with different backgrounds might have different opinions about the appropriateness of relations between concepts)
    • Importance. Importance reflects the strength of a relation between a user and a subject in a given context. (Context dependant because the perceived importance of a subject changes over time as the background and setting of the individual change)
  • Assigning QRI
    • Manually
      • Quality added by rating resources
      • Relevance set by rating associations
        • Topic types for context: e.g. project, event, task, location, group
      • Importance assigned by setting topics as important (both individual and collective)
    • Automatic
      • Quality added when highly ranked users author resources
      • Relevance added upon simultanous browsing of topics or when following topic associations
      • Importance (low degree) is set when ever a user browse or use a topic
  • Ontology
  • Sample socio-semantic contextual network Strengthened assoc (important for user) Strengthed assoc (topic 2 topic relevance)
  • QRI Implementation
  • Context implementation AssocId userId PSI val 234 567 http://xxx 0.8 234 567 http://xx2 0.3 567 321 http://xx3 0.7 .... subject A subject B
  • QRI in Resource Ranking
  • User-centered Resource Ranking
  • Ranking in the Context of a Specific Topic
  • Partial Implementation on fuzzzy.com
    • Fuzzzy.com now supports:
    • Association ranking (relevance without context)
    • Favorite topics (importance without context)
    • Recommend bookmarks (Quality)
    • Socio-Semantic search
  • Conclusion
    • Advantages of Topic Maps and neural semantic network approach
      • Intuitive in the user interface
      • Semi learning/adaptability
      • Less work on part of the user
    • Further work
      • Prototyping, benchmarking
      • Constrained Spreading Activation resource calculations
      • C ontext optimalization
      • Tuning QRI measures by user
  • Thank you