1. Query Relevance Feedback and Ontologies How to Make Queries Better

2. Overview Ranked Retrieval Relevance Feedback The Semantic Web and Ontologies

3. Typical Web Retrieval Process Link Following NeedKeyword Query More Like this

4. Ranked Retrieval How can we present the “best” item to the user first

5. What are we trying to do in IR Find the Document which is most similar to the query Ranking Interpretation –show the best most similar document first –then the next best most similar document –and so on

6. Bag of Words Model of Text Ignore the order of words in the document Just record whether a word appears in a document

7. Similarity Measures Cosine Formula Measures how like a document is to a query/document See Kowalski Chapter 7

8. Similarity as Ranking Use the Similarity Measure to rank the documents

9. Relevance Feedback More Like this done properly

10. Observation The user is probably in the best position to judge the relevance of a document Likewise the user is probably in the best position to judge which returned (highly ranked) documents are irrelevant

11. Retrieval Process NeedAnalytic Query More Like this No More Like This

12. Relevance Feedback in Nutshell Perform an initial retrieval Ask the user to indicate which documents are relevant/irrelevant –Add all terms from relevant documents –Remove all terms from irrelevant documents –requery

13. Variants Using Ranking and Weighting Pseudo relevance feedback –use terms from all (highly ranked) retrieved documents –Assumes highly ranked documents are a homogenous mass of relevant documents (Croft) very helpful if very few documents retrieved  perpetuates errors/misunderstandings from original query

14. Exercise What are advantages of positive feedback ? What are advantages of negative feedback ? Which is best ?

15. Relevance Feedback Conclusion Consistently proven an effective way to improve retrieval Biggest problem is getting users to engage in the interaction, especially if no highly relevant documents are in the initially retrieved set

16. Ontologies

17. The Semantic Web Introduced by Tim Berners Lee and others in 2001 –http://www.sciam.com/article.cfm?articleID=00 048144-10D2-1C70-84A9809EC588EF21http://www.sciam.com/article.cfm?articleID=00 048144-10D2-1C70-84A9809EC588EF21 Essentially about allowing computers and people to share the same world Central to the communication is the notion of an Ontology

18. Ontology Definition To standardize semantic terms, many areas use specific ontologies, which are hierarchical taxonomies of terms describing certain knowledge topics (Baeza-Yates & Ribeiro-Neto, 1999, p143). Thesauri: Ontologies for Information Retrieval. Entities, Relations.

19. O example Car Drop head coupe Automobile Hot Hatch Engine Wheels Seat Parts Sort of Also Known as

20. Improving Recall and/or Precision If you get too few documents  Use more general terms in the query Use “automobile” instead of “drop head coupe”  Use an alternative term which is more common  Use “car” rather than “automobile”  If you get too many (overall) –Use a more specific term Use “hot hatch” rather than “car”

21. Issues How are thesauri different from Ontologies –Are we representing the world or words –Is Wordnet an ontology ? Are Ontologies meant to be –General –Universal –For a specific purpose ?

22. Thesauri Provide a map of a given field of knowledge: concepts, relations. Provide a standard vocabulary for consistent indexing. Assist users with locating terms for proper query formulation. Ensure only one term from a synonym set is used for indexing and searching: otherwise a searcher who uses one synonym and retrieves some useful documents may think the correct term has been used and the search has been exhaustive, without knowing that there are other useful documents under other synonyms. Provide classified hierarchies for broadening or narrowing a search if too many or too few documents are retrieved. Retrieval based on concepts rather than words (Baeza-Yates & Ribeiro-Neto, 1999).

23. WordNet Relations Examples are: Synonyms e.g. couch / sofa / lounge Antonyms e.g. love / hate Hypernyms (broader) e.g. cat / tabby Hyponyms (narrower) e.g. cat / animal Meronym (part-of) e.g. finger / hand Meronym (made-of) e.g. snowflake / snow

24. WordNet Demos See vancouver-webpages.com/wordnet See marimba.d.umn.edu/cgi- bin/similarity.cgi

25. Conclusions Ranked Retrieval –similarity matching Relevance Feedback –positive and negative feedback The Semantic Web and Ontologies