SQE – Semantic Query Expansion Digital Enterprise Research Institute National University of Ireland, Galway [email_address] jakub.demczuk @deri.org [email_address] [email_address]

Outline Motivations Algorithm overview Ideas

Motivations

Algorithm overview

Ideas

SQE motivations: Why? Simple search returns too many results Keyword search must be precise Ambiguity of words

Why?

Simple search returns too many results

Keyword search must be precise

Ambiguity of words

SQE motivations: How Improve search results by applying semantic based reasoning Expand queries that have few results, or Narrow number of results when there are to many of them reflect user’s interests in results

How

Improve search results by applying semantic based reasoning

Expand queries that have few results, or

Narrow number of results when there are to many of them

reflect user’s interests in results

Algorithm overview: User writes a query Query is processed by the fulltext search engine – Lucene Results are sorted using two metrics: TF(Term Frequency) and IDF(Inverse Document Frequency) Semantic Query Expansion is called Search history is saved for future use

User writes a query

Query is processed by the fulltext search engine – Lucene

Results are sorted using two metrics: TF(Term Frequency) and

IDF(Inverse Document Frequency)

Semantic Query Expansion is called

Search history is saved for future use

SQE picture: Search: Java web Lucene Query Expansion (WordNet,Taxonomies,FOAF) Results (TF/IDF) Java(PL) Java (Island) 60 10 SSCF Bookmarks FOAFRealm Web(www) Spider’s web 50 20 Results History Friends

Semantic Query Expansion in details: Giving words meanings Associating weights to results: Long-term context – user’s interests Mid-term context – recent searches, recently browsed resources etc. Short-term context – last queries and query itself Final weight is calculated for each result If results are not sufficient or are to broad expansion is being made, else results are returned

Giving words meanings

Associating weights to results:

Long-term context – user’s interests

Mid-term context – recent searches, recently browsed resources etc.

Short-term context – last queries and query itself

Final weight is calculated for each result

If results are not sufficient or are to broad expansion is being made, else results are returned

Ideas : Word similarity Word meaning in text Allow users to view algorithm steps, and if neccessary, modify them by hand The „Tell me why” button

Word similarity

Word meaning in text

Allow users to view algorithm steps, and if neccessary, modify them by hand

The „Tell me why” button