Basic IR: Queries

1
Basic IR Queries

• Query is statement of users information need.
• Index is designed to map queries to likely to be
  relevant documents. Query type, content,
  representation dictates what the index must do.
• Varies from single keywords through specialized
  query languages to exemplar documents.

2
Documents as Queries

• Find other documents like this one.
• Query is itself a document it can go through
  same sort of pre-processing (e.g., stop word
  removal, stemming).
• Characteristics of queries mimic those of
  documents.

3
Query Term Distribution in SavvySearch
4
Keyword Queries

• Query is composed of a set of keywords.
• Retrieve document that best matches keywords.
• Advantage easy to use, supports fast indexing
• Disadvantage coarse, easy to lead astray (e.g.,
  words with multiple meanings), difficult to
  express complex information need

5
Boolean Queries

• combine queries (keywords) with Boolean
  operators
• OR children OR kids
• AND windows AND software
• BUT unix BUT solaris
• no NOT!
• Advantage more precise queries
• Disadvantage does not support ranking, less
  intuitive

6
Phrase Search

• Supplement single terms with phrases exact
  sequence of terms
• Requires index that tracks proximity of terms or
  stores both singletons and phrases
• Extension is context where proximity between
  terms is stated (e.g., adele w/2 howe to
  CiteSeer)

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Query Language Web Search Engines I

• Altavista Advanced Search
• Form
• all of these words
• this exact phrase
• any of these words
• none of these words
• Boolean Expression
• AND
• OR
• AND NOT
• NEAR
• Date, File Type, Location

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Query Language Web Search Engines II

• Google Advanced Search
• Find Results
• with all of the words
• with the exact phrase
• with at least one of the words
• without the words
• Language, File Format, Date, Occurrences, Domain,
  SafeSearch
• Page-Specific Search
• Find pages similar to the page
• Find pages that link to the page
• Topic Specific Searches

9
Typical Query Behavior on WWW

• Query term distribution obeys Zipfs Law (quite
  skewed, although skew does drift).
• Length is ? terms.
• Few users exploit full power of query languages
  most enter terms without operations and do not
  use advanced search interfaces.
• Change in behavior?

10
Natural Language

• Augmented Boolean approach
• Treat query as document.
• Rank documents by how well they match the
  constraints of the query and return those above a
  certain threshold.
• NLP approach
• Interpret semantics in a limited way to constrain
  query (e.g., who indicates a person)

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NL Example AskJeeves
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Advanced Querying

• Pattern Matching
• combinations of syntactic features, e.g., regular
  expressions, wild-card queries
• Structural Queries
• forms, hypertext and hierarchies
• typically supports iterative querying as in
  guided browsing (e.g., WebGlimpse or Letitzia)

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Advanced Querying Letizia

• Recommends new pages based on users browsing
  preferences
• Infers interests by observing user behavior save
  bookmark, follow link, spend time on page
• Models documents as list of keywords

Figure from http//lieber.www.media.mit.edu/people
/lieber/Liebrary/Letizia/Letizia.html
14
Caching

• An astonishing number of people submit the same
  queries (e.g., Harry Potter).
• Just as single word usage is skewed (Zipfs Law)
  so is query submission on WWW.
• Can exploit this by caching results for oft
  repeated queries.

15
Single vs. On-Going Queries Filtering

• Find new documents from information stream that
  satisfy a static information need
• User profile represents interests threshold
  represents how closely documents must match.
• User may provide the query or it may be learned
  through relevance feedback.

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Filtering Process
from MIR text