Combing semantic networks with multi-attribute utility models: An evaluative database indexing method

Author

Mitri, Michel

Author_Institution

Eastern Michigan Univ., Ybsilanti, MI, USA

fYear

1993

fDate

1-5 Mar 1993

Firstpage

462

Abstract

Summary form only given. The author presents the multiattribute utility (MAU) semantic network model, a method for representing evaluative knowledge by combining techniques from two separate but complementary disciplines. A semantic network knowledge representation is merged with a decision-theoretic MAU model to provide an indexing technique for databases that store information in the form of judgments or evaluations. Such a merger provides inferencing capabilities that allow the database to estimate an evaluation even if no direct evidence is available for user queries. The model includes concept types, concepts, relationship links, judgment records, and inference strategies. The MAU SemNet model has been applied to an international-marketing database that contains judgments pertaining to potential countries and markets for export or foreign investment ventures

Keywords

deductive databases; indexing; inference mechanisms; marketing data processing; semantic networks; MAU SemNet model; concept types; decision theory; evaluative database indexing method; evaluative knowledge representation; export; foreign investment ventures; inferencing capabilities; international-marketing database; judgments; multi-attribute utility models; potential markets; relationship links; semantic network model; user queries; Attenuation; Databases; Indexing; Investments; Personal communication networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence for Applications, 1993. Proceedings., Ninth Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-8186-3840-0

Type

conf

DOI

10.1109/CAIA.1993.366621

Filename

366621