Extracting Causal Knowledge from a Medical Database Using Graphical Patterns

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.

• Type: Conference
• Year: 2000
• Where: ACL
• Authors: Christopher S. G. Khoo, Syin Chan, Yun Niu
• Keywords: ACL 2000, ACL 2007, Causal Knowledge, Parse Tree, Syntactic Parse Trees