Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

On Information Retrieval and Evidential Reasoning

Lung Albert Chen

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-88-429
June 1988

http://www.eecs.berkeley.edu/Pubs/TechRpts/1988/CSD-88-429.pdf

Many research efforts have been devoted to solving the problems of Boolean systems, which are currently used for Information Retrieval (IR). We propose a new model of IR, which treats the whole process of IR as a process of evidential reasoning. Our model is knowledge based, and theoretically sound. An input query provided by a user, triggers the process of evidential reasoning. The process consists of two parts: automatic query formulation and query evaluation. Automatic query formulation maps a concept given by the user into a set of textual terms. These terms, according to the pieces of evidence given by an expert, have been used by various authors to describe the concept specified in the input query. Query evaluation is an evidence-aggregation scheme, that combines all the pieces of evidence and assigns a Retrieval Status Value (RSV) to each document. A list of documents, ranked according to the RSV, is provided to the user as a response to his or her information request. In our model, inference strength between concept and subconcept is measured by conditional basic probability assignment; and this measure is discounted, chained, and combined based on the Dempster-Shafer (D-S) theory and its extension.


BibTeX citation:

@techreport{Chen:CSD-88-429,
    Author = {Chen, Lung Albert},
    Title = {On Information Retrieval and Evidential Reasoning},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1988},
    Month = {Jun},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1988/6045.html},
    Number = {UCB/CSD-88-429},
    Abstract = {Many research efforts have been devoted to solving the problems of Boolean systems, which are currently used for Information Retrieval (IR). We propose a new model of IR, which treats the whole process of IR as a process of evidential reasoning. Our model is knowledge based, and theoretically sound. An input query provided by a user, triggers the process of evidential reasoning. The process consists of two parts: automatic query formulation and query evaluation. Automatic query formulation maps a concept given by the user into a set of textual terms. These terms, according to the pieces of evidence given by an expert, have been used by various authors to describe the concept specified in the input query. Query evaluation is an evidence-aggregation scheme, that combines all the pieces of evidence and assigns a Retrieval Status Value (RSV) to each document. A list of documents, ranked according to the RSV, is provided to the user as a response to his or her information request. In our model, inference strength between concept and subconcept is measured by conditional basic probability assignment; and this measure is discounted, chained, and combined based on the Dempster-Shafer (D-S) theory and its extension.}
}

EndNote citation:

%0 Report
%A Chen, Lung Albert
%T On Information Retrieval and Evidential Reasoning
%I EECS Department, University of California, Berkeley
%D 1988
%@ UCB/CSD-88-429
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1988/6045.html
%F Chen:CSD-88-429