Semantic Network Representation In Rule Based Inference System

Abstract

Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions. Several advantages can be cited for the semantic network representation. The most important of these concern the ability to represent subset and element taxonomic information, the ability to include the potential for smooth interface with natural language subsystems. This representation is being used in a system currently under development at SRI to aid a geologist in the evaluation of the mineral potential of exploration sites. The principles behind this system and its current implementation are described in the paper.