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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