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Fmi semtech-semantic ir-beta
 

Fmi semtech-semantic ir-beta

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    Fmi semtech-semantic ir-beta Fmi semtech-semantic ir-beta Presentation Transcript

    • Introduction to Semantic Information Retrieval
      A formal definition of IR; Overview of common solutions; A semantic approach to IR; applied in Insemtives
      Mar 2010
    • AtanasKiryakov, CEO of Ontotext, introduces the what, why and how of semantic technologies.
      Prof. KirilSimov defined knowledge, reasoning, knowledge storeage and reasoning systems.
      Mariana Damova, PhD taught you how to store knowledge in ontologies. RDF was introduced.
      Engineers work with knowledge by describing it RDF, storing in an RDF database and reason on it using OWL.
      Mar 2010
      #2
      Introduction to Semantic Technologies
      Previously on “SemanticTech. Course ...”
    • Putting knowledge to use in:
      Information Retrieval: an informal definition by example -search engines
      We are trying to do it better in …
      Ontotext KIM – semantic information extraction and retrieval platform
      Insemtives (http://insemtives.eu/)– R & D for the next generation of semantic technologies, which objective is to …
      Introduction to Semantic Technologies
      #3
      Mar 2010
      “to bridge the gap between human and
      computational intelligence.”
    • Outline
      Information Retrieval: formal definition
      Measure of success
      Common approaches
      Vector space model
      Using knowledge for better IR
      Understanding queries
      Enabling users to put rich queries
      Applying semantic IR in KIM, Insemtives
      Introduction to Semantic Technologies
      #4
      Mar 2010
    • Information Retrieval: the scientist’s approach
      Introduction to Semantic Technologies
      #5
      Mar 2010
      Define it formally
      Measure the success
      http://en.wikipedia.org/wiki/Information_retrieval#Performance_measures
      Collect examples
      Test corpus
      Development corpus
      Training corpus
      Don’t overfit!
      Learn how others do it …
      0 ≤ F ≤ 1
    • Mar 2010
      Vector space model
      Documents and queries and vectors
      Simplest way: a dimension for each term
      Simplest value: count the time the term is present
      Compare documents by distance, compare a query to a document using the angle
      #6
      Introduction to Semantic Technologies
      • Doing it smarter – weights instead of 0 and 1; remove dimensions all together
    • Doing it (slightly) smarter: TF-IDF
      Some words are more important than others …
      Bestprice for iPhone
      TF.IDF ranks matcher on rarewords higher
      Improve your vector space with previously gathered knowledge – frequency of each word in general language
      Mar 2010
      #7
      Introduction to Semantic Technologies
    • Doing it smarter: reduce the dimensions
      Some words mean the same
      Bestprice for Apple iPhone
      Math. Formulation: the dimension vectors are not orthogonal, thus the vector space is non-uniform
      Reduce equivalent words to a single concept  Merge the (linearly) dependent dimension vectors into one.
      Mar 2010
      #8
      Introduction to Semantic Technologies
    • Using knowledge for better IR
      How do we know that two sets of terms mean the same?
      Account for broader / narrower relations
      Best price for smartphones
      Query analysis Account for structure – NLP
      Rich user interfaces
      Introduction to Semantic Technologies
      #9
      Mar 2010
      Ontologies!
    • Question answering
      Semantic solution:
      Introduction to Semantic Technologies
      #10
      Mar 2010
    • Relying on ontologies: cheating?
      Mar 2010
      #11
      Introduction to Semantic Technologies
      Ontologies exist!
      Linked Data
      Information Extraction
      Insemtives
    • Applying semantic IR in KIM, Insemtives
      Introduction to Semantic Technologies
      #12
      Mar 2010
    • Demonstration
      Introduction to Semantic Technologies
      #13
      Mar 2010
    • Demonstration – behind the scenes
      Introduction to Semantic Technologies
      #14
      Mar 2010
    • Demonstration – behind the scenes (cont.)
      Introduction to Semantic Technologies
      #15
      Mar 2010
    • Demonstration – behind the scenes (cont.)
      Introduction to Semantic Technologies
      #16
      Mar 2010
    • Coming up next …
      Anton – KIM: The complete picture
      George and Kate2– HOWTO: Information Extraction
      Yasen – Sentiment analysis: Put user’s voice in the vector space
      AtanasKiryakov– Behing the scenes in the RDF database
      Introduction to Semantic Technologies
      #17
      Mar 2010
    • Thank you!
      Mar 2010
      #18
      Introduction to Semantic Technologies