Title: Query Operations
Summary: 

• The first query formulation should be treated as an initial (naive) attempt to retrieve relevant information.  Following that, the documents initially retrieved could be examined for relevance and new improved query formulations could then be constructed.
• Two steps of query reformulation: expanding the original query with new terms and reweighting the terms in the expanded query.
• Relevance feedback is the most popular query reformulation strategy.  The expected effect is that the new query will be moved towards the relevant documents and away from the non-relevant ones.
  • Advantages
    • It shields users from the details of the query reformulation process because all the users have to provide is a relevance judgement on documents.
    • It breaks down the whole search task into a sequence of small steps which are easier to grasp.
    • It provides a controlled process designed to emphasize some terms and de-emphasize others.
• Automatic Local Analysis
  • According to this notion, known relevant documents contain terms which can be used to describe a larger cluster of relevant documents.
  • This usually involves identifying terms which are related to the query terms (i.e. synonyms, stemming variations, or terms which are close to the query terms in the text).
  • Association Clusters - based on the co-occurrence of stems (or terms) inside documents.
  • Metric Clusters
  • Scalar Clusters
• Global techniques usually involve the building of a thesaurus which identifies term relationships in the whole collection.