Semantic networks: a stochastic model of their performance in information retrieval
Recent advances in computer technology have made possible the implementation of information retrieval strategies adopting semantic network approaches from artificial intelligence. This research investigates the claim that document-based information retrieval strategies which automatically extend the user's query by inferring, through the use of a semantic network, the concepts that the user wishes to retrieve, can out perform traditional Boolean retrieval strategies. The claim reflects a growing concern among researchers and commercial information retrieval services that many relevant documents are not being retrieved in systems using Boolean retrieval strategies. This research compares two classes of document-based information retrieval strategies-Boolean and concept extended. It analyzes Boolean and concept extended retrieval strategy performance via a stochastic model of user querying and information retrieval. Stochastic processes for querying, retrieving and constructing semantic networks are proposed, and resulting probability distributions are constructed. Results show improved performance measured by most performance statistics for the concept extended retrieval strategy over the traditional Boolean strategy.
- Research Organization:
- Michigan Univ., Ann Arbor (USA)
- OSTI ID:
- 7135263
- Country of Publication:
- United States
- Language:
- English
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