Boolean Set Processing / Venn Diagram

Boolean Set Processing / Inverted File Results

Documents are made up of words, or other types of terms (numbers, phrases, or other types of features). So, we could build a matrix that has a row for each document, and a column for each term, with matrix entries holding a count of the number of occurrences of the term in the document. We can compress this into an inverted file.

• Inverted file is called that since it looks at the matrix of documents x terms in the inverse way --- by term instead of by document (the normal case for readers).
• Extension to handle proximity takes extra space.
  • More precise searching allows users to constrain word combinations further, such as:
    • adjacent
    • within n words
    • in same sentence
    • in same paragraph
  • Entries for a term might include (doc,wt) pairs plus:
  • List of locations inside document as:
  • Byte offset (or offsets for start/end); or
  • Paragraph number, sentence number, word number; or
  • Pointer into a structure tree (or pointers for a span):
    • e.g., chapter no. / section no. / subsection no. / par. no.;
    • e.g., reference no. / title part / subtitle field;
    • e.g., dictionary headword / part of speech / sense / definition.

Online Searching in the Boolean Model

• Phases
  • Clarify info. need / problem
  • Identify access points: a, t, s, ...
  • Identify concepts, terms
  • Develop, try, adapt search strategies
  • Examine results, use feedback
• Query organization
  • String of pearls (DNF: [A*B*C]+[H*I*J+K]+[X*Y*Z])
  • List of required concepts (CNF: [A+B]*[H+I+J+K+L])
• Concept organization building on:
  • Elements: descriptors, phrases, words, stems/roots
  • Relationships: Synonym, xref, bt