Number of found records: 9
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DAVIS, Randall; SHROBE, Howard; SZOLOVITS, Peter |
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What is a Knowledge Representation? |
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AI Magazine, 1993, vol.14, n.1, pp. 17-33 |
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On line ( 15/06/2004) |
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Although knowledge representation is one of the central and in some ways most familiar concepts in AI, the most fundamental question about it--What is it?--has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, while still others have focused on properties that are important to the notion of representation in general. In this paper we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and at times conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field. (AU) |
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Knowledge representation; Artificial intelligence |
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LANCASTER, F.W.; SMITH, Linda C. (Eds.) |
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Artificial Intelligence and Expert Systems: Will They Change the Library? |
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Papers Presented at the Annual Clinic on Library Applications of Data Processing (27th, Urbana, Illinois, March 25-27, 1990). Illinois, March 25-27, 1990). 1992. |
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Some of the 12 conference papers presented in this proceedings focus on the present and potential capabilities of artificial intelligence and expert systems as they relate to a wide range of library applications, including descriptive cataloging, technical services, collection development, subject indexing, reference services, database searching, and document delivery. Other papers deal with the underlying design issues of knowledge representation and natural language processing. The papers are; (1) "Artificial Intelligence: What Will They Think of Next?" (Douglas P. Metzler); (2) "Technical Services Processes as Models for Assessing Expert System Suitability and Benefits" (Charles Fenly); (3) "Automated Cataloging: Implications for Libraries and Patrons" (Stuart Weibel); (4) "Interactive Knowledge-Based Systems for Improved Subject Analysis and Retrieval" (Susanne M. Humphrey); (5) "Reference Expert Systems: Foundations in Reference Theory" (James R. Parrott); (6) "Expert Systems at the National Agricultural Library: Past, Present, and Future" (Samuel T. Waters); (7) "User Models for Information Systems: Prospects and Problems" (Christine L. Borgman and Yolanda I. Plute); (8) "Natural Language Processing: Current Status for Libraries" (Amy Warner); (9) "Knowledge Representation in Artificial Intelligence" (Irene L. Travis); (10) "Intelligent Interfaces to Online Databases" (Brian C. Vickery); (11) "Expert Systems in Document Delivery: The Feasibility of Learning Capabilities" (Jaime Pontigo, Ezequiel Tovar-Reyes, Guillermo Rodriquez, and Sergio Ortiz-Gama); and (12) "Walking Your Talk: Why Information Managers Are Not High Tech" (W. David Penniman). An index and brief author biographies conclude the volume; chapters include references. (AU) |
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artificial intelligence; Indexing-; Natural language processing |
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NEWMAN, David R. |
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Knowledge Representation |
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Queen’s University Belfast |
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On line ( 15/06/2004) |
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Concepts and key elements of knowledge representation |
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knowledge representation; semantic networks; systemic networks |
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RAU, Lisa F.; JACOBS, Paul S.; ZERNIK, Uri |
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Information Extraction and Text Summarization Using Linguistic Knowledge Acquisition |
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Information Processing and Management, vol. 25, nº 4, 1989, pp. 419-428. |
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On line (10/05/2005) |
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Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system called SCISOR (System for Conceptual Information Summarization, Organization and Retrieval). Describes the text processing, language aquisition, and summarisation components of SCISOR. (AU) |
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Information-storage-and-retrieval; text summarization; Subject indexing; |
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