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

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