An artificial intelligence environment for information retrieval research

The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of het...

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Main Author: France, Robert Karl
Other Authors: Computer Science
Format: Others
Language:en_US
Published: Virginia Polytechnic Institute and State University 2019
Subjects:
Online Access:http://hdl.handle.net/10919/91091
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spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-910912020-09-26T05:35:10Z An artificial intelligence environment for information retrieval research France, Robert Karl Computer Science LD5655.V855 1986.F726 Expert systems (Computer science) Information storage and retrieval systems Artificial intelligence The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of heterogeneous documents, such as electronic mail digests or messages, which vary widely in style, length, topic, and structure. In order to ensure system adaptability and to allow reconfiguration for controlled experimentation, the project has been designed as a moderated expert system. This thesis covers the design problems involved in providing a unified architecture and knowledge representation scheme for such a system, and the solutions chosen for CODER. An overall object-oriented environment is constructed using a set of message-passing primitives based on a modified Prolog call paradigm. Within this environment is embedded the skeleton of a flexible expert system, where task decomposition is performed in a knowledge-oriented fashion and where subtask managers are implemented as members of a community of experts. A three-level knowledge representation formalism of elementary data types, frames, and relations is provided, and can be used to construct knowledge structures such as terms, meaning structures, and document interpretations. The use of individually tailored specialist experts coupled with standardized blackboard modules for communication and control and external knowledge bases for maintenance of factual world knowledge allows for quick prototyping, incremental development, and flexibility under change. The system as a whole is structured as a set of communicating modules, defined functionally and implemented under UNIX™ using sockets and the TCP/IP protocol for communication. Inferential modules are being coded in MU-Prolog; non-inferential modules are being prototyped in MU-Prolog and will be re-implemented as needed in C++. M.S. 2019-07-03T18:56:54Z 2019-07-03T18:56:54Z 1986 Thesis Text http://hdl.handle.net/10919/91091 en_US OCLC# 14640874 In Copyright http://rightsstatements.org/vocab/InC/1.0/ viii, 158 leaves application/pdf application/pdf Virginia Polytechnic Institute and State University
collection NDLTD
language en_US
format Others
sources NDLTD
topic LD5655.V855 1986.F726
Expert systems (Computer science)
Information storage and retrieval systems
Artificial intelligence
spellingShingle LD5655.V855 1986.F726
Expert systems (Computer science)
Information storage and retrieval systems
Artificial intelligence
France, Robert Karl
An artificial intelligence environment for information retrieval research
description The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of heterogeneous documents, such as electronic mail digests or messages, which vary widely in style, length, topic, and structure. In order to ensure system adaptability and to allow reconfiguration for controlled experimentation, the project has been designed as a moderated expert system. This thesis covers the design problems involved in providing a unified architecture and knowledge representation scheme for such a system, and the solutions chosen for CODER. An overall object-oriented environment is constructed using a set of message-passing primitives based on a modified Prolog call paradigm. Within this environment is embedded the skeleton of a flexible expert system, where task decomposition is performed in a knowledge-oriented fashion and where subtask managers are implemented as members of a community of experts. A three-level knowledge representation formalism of elementary data types, frames, and relations is provided, and can be used to construct knowledge structures such as terms, meaning structures, and document interpretations. The use of individually tailored specialist experts coupled with standardized blackboard modules for communication and control and external knowledge bases for maintenance of factual world knowledge allows for quick prototyping, incremental development, and flexibility under change. The system as a whole is structured as a set of communicating modules, defined functionally and implemented under UNIX™ using sockets and the TCP/IP protocol for communication. Inferential modules are being coded in MU-Prolog; non-inferential modules are being prototyped in MU-Prolog and will be re-implemented as needed in C++. === M.S.
author2 Computer Science
author_facet Computer Science
France, Robert Karl
author France, Robert Karl
author_sort France, Robert Karl
title An artificial intelligence environment for information retrieval research
title_short An artificial intelligence environment for information retrieval research
title_full An artificial intelligence environment for information retrieval research
title_fullStr An artificial intelligence environment for information retrieval research
title_full_unstemmed An artificial intelligence environment for information retrieval research
title_sort artificial intelligence environment for information retrieval research
publisher Virginia Polytechnic Institute and State University
publishDate 2019
url http://hdl.handle.net/10919/91091
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