Article An architecture for probabilistic concept-based information retrieval Share on Authors: R. M. Fung Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , S. L. Crawford Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , L. A. Appelbaum Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , R. M. Tong Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile Authors Info & Claims SIGIR '90: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrievalDecember 1989 Pages 455–467https://doi.org/10.1145/96749.98252Online:01 December 1989Publication History 19citation520DownloadsMetricsTotal Citations19Total Downloads520Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This article contains a detailed description of the knowledge representation used in RUBRIC, our concept-based, full-text information retrieval system. Since we view information retrieval as a process of evidential reasoning, the RUBRIC knowledge representation is designed to help maintain a clear distinction between descriptions of domain concepts, the documentary evidence for those concepts, and the beliefs about concepts that can be sustained on the basis of the evidence. This article is a consolidation of our earlier work in this area, containing, as it does, a revised language for defining concepts, an extended view of the role of evidence, and a formally grounded model of belief.

Article Free Access Share on Conceptual information retrieval using RUBRIC Authors: R. Tong Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CA Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CAView Profile , L. Appelbaum Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CA Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CAView Profile , V. Askman Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CA Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CAView Profile , J. Cunningham Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CA Advanced Decision Systems, 201 San Antonio Circle, Suite 286, Mountain View, CAView Profile Authors Info & Claims SIGIR '87: Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrievalNovember 1987 Pages 247–253https://doi.org/10.1145/42005.42033Online:01 November 1987Publication History 24citation577DownloadsMetricsTotal Citations24Total Downloads577Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A research prototype software system for conceptual information retrieval has been developed. The goal of the system, called RUBRIC, is to provide more automated and relevant access to unformatted textual databases. The approach is to use production rules from artificial intelligence to define a hierarchy of retrieval subtopics, with fuzzy context expressions and specific word phrases at the bottom. RUBRIC allows the definition of detailed queries starting at a conceptual level, partial matching of a query and a document, selection of only the highest ranked documents for presentation to the user, and detailed explanation of how and why a particular document was selected. Initial experiments indicate that a RUBRIC rule set better matches human retrieval judgment than a standard Boolean keyword expression, given equal amounts of effort in defining each. The techniques presented may be useful in stand-alone retrieval systems, front-ends to existing information retrieval systems, or real-time document filtering and routing.

A technique for making linguistic decsions is presented. Fuzzy sets are assued to be an appropriate way of dealng with uncertainty, and it b therefore cncluded that decisions taken on the basis of such infomation must themselves be fuzzy. It b inappriate then to present the decision in numencal form; a statement in natural angug is much better. For brevity only a single-stage multlabute decsion problem is considered. Solutions to such problems are shown using ideas in linguisc approximation and truth qualiction. An extensive example illuminates the basic ideas and techniques.