Constructing an initial knowledge base for medical domain expert system using induct RDR

Abstract

Abstract-This paper describes how we build an initial knowledge-base of ripple-down rules (RDR) in medical domain. In medical domain, all decisions are made by the domain experts. Increasing a complexity of disease and various symptoms, there are some attempts to introduce an expert system in medical domain these days. To construct the expert system, it needs to extract the expert's knowledge. To do that, we use ripple-down rules (RDR) which allows experts to modify their knowledge base directly because it provides a systematic approach to do that. We also use Induct RDR which builds a knowledge base from existing data to reduce experts' burden of adding their knowledge from the bottom up. The expert system should produce multiple comments from a test set, which is multiple classification problem. However, Induct RDR only deals with a single classification problem. To handle this problem, we divide a test set into 18 categories which is almost the single classification problem and apply Induct RDR to each category independently. Using this approach, we can improve the missing rate about 70% compared to an approach not dividing into several categories.

Modeling Complex Relationship Paths for Knowledge Graph Completion

“…Knowledge graph techniques are widely used in several intelligent fields including intelligent search [1], question answering systems [2], expert systems [3], named entity recognition [4] and entity disambiguation [5]. These techniques represent the semantic network with a graph structure, expressing the knowledge entities as nodes and relationships as edges in the graph.…”