1. Introduction to Semantic Information Retrieval A formal definition of IR; Overview of common solutions; A semantic approach to IR; applied in Insemtives Mar 2010

2. 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 ...”

3. 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.”

4. 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

5. 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

7. 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

8. 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!

9. Question answering Semantic solution: Introduction to Semantic Technologies #10 Mar 2010

10. Relying on ontologies: cheating? Mar 2010 #11 Introduction to Semantic Technologies Ontologies exist! Linked Data Information Extraction Insemtives

11. Applying semantic IR in KIM, Insemtives Introduction to Semantic Technologies #12 Mar 2010

12. Demonstration Introduction to Semantic Technologies #13 Mar 2010

13. Demonstration – behind the scenes Introduction to Semantic Technologies #14 Mar 2010

14. Demonstration – behind the scenes (cont.) Introduction to Semantic Technologies #15 Mar 2010

15. Demonstration – behind the scenes (cont.) Introduction to Semantic Technologies #16 Mar 2010

16. 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

17. Thank you! Mar 2010 #18 Introduction to Semantic Technologies