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146 6 Information Retrieval


Summary



  • Citations (such as hypertext links) can be used to rank documents relevant
    to a query according to various criteria:

    1. Authoritativeness

    2. Central source

    3. Popularity



  • Citation ranking improves both selectivity and coverage.

  • However, citation ranking has a number of weaknesses:

    1. Current systems are based on matching words in the query with words
      in documents and do not consider the meaning of the words.

    2. Only the principal eigenvector is used, so smaller communities will be
      masked by larger ones.




6.5 Vector Space Retrieval of Knowledge Representations


One of the main assumptions of the vector space model is that documents
are composed of collections of terms. While some systems attempt to take
advantage of correlations between terms, such correlations are difficult to
determine accurately, and the number of correlations that must be computed
is huge. In any case, the terms are still disjoint from one another. Knowledge
representations change this situation. Terms can now be complex concept
combinations that are built from simpler terms. Thus a term like “flu vac-
cine” contains both “flu” and “vaccine” as well as the complex relationship
between these two concepts which expresses the effect of the vaccine on the
influenza virus as well as the the derivation of the vaccine from the virus and
in response to it. In the UMLS, all three of these are concepts, and they are
related to one another.
To see how natural, as well as how subtle, concept combinations can be,
try juxtaposing two commonly used terms in different orders. For example,
“test drug” and “drug test.” Although these two have completely different
meanings, most search engines give essentially the same answer for both.
Indeed, “test drug” can be interpreted in two ways depending on whether
“test” is a verb or adjective. The term “drug test” also has several meanings.
As an exercise, try some other pairs of terms to see how many meanings
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