Paper Info
Title
Belief Function Model For Information-Retrieval
Abstract
The Belief Function Model for automatic indexing and ranking of documents with respect to a given user query is proposed. The model is based on a controlled vocabulary, like a thesaurus, and on term frequencies in each document. Descriptors in this vocabulary are terms chosen from among their synonyms to be used as index terms. A descriptor can have a subset of broader descriptors, a subset of narrower descriptors, and a subset of related descriptors. Thus, descriptors are not mutually exclusive and naive probabilistic models are inadequate for handling them. However, a belief function can still be defined over a thesaurus of descriptors. Belief functions over the descriptors can represent a document or a user query. We can compute the agreement between a document belief function and a query belief function. Therefore, we propose that the set of documents be ranked according to their agreement with the given user query. We show that the Belief Function Model is wider in scope than the Standard Vector Space Model.
Year
DOI
Venue
1993
10.1002/(SICI)1097-4571(199301)44:1<10::AID-ASI2>3.0.CO;2-V
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
Keywords
Field
DocType
information retrieval,documentation,relevance information retrieval,mathematical formulas,probability,semantics,comparative analysis
Data mining,Computer science,Controlled vocabulary,Artificial intelligence,Natural language processing,Probabilistic logic,Vector space model,Ranking,Information retrieval,Relevance (information retrieval),Automatic indexing,Vocabulary,Semantics
Journal
Volume
Issue
ISSN
44
1
0002-8231
Citations 
PageRank 
References 
14
1.41
0
Authors
2
Name
Order
Citations
PageRank
Wagner Teixeira da Silva1182.33
Ruy Luiz Milidiú219220.18