ARISTA causal knowledge discovery from texts

Abstract

A method is proposed in the present paper for supporting the discovery of causal knowledge by finding causal sentences from a text and chaining them by the operation of our system. The operation of our system called ACkdT relies on the search for sentences containing appropriate natural language phrases. The system consists of two main subsystems. The first subsystem achieves the extraction of knowledge from individual sentences that is similar to traditional information extraction from texts while the second subsystem is based on a causal reasoning process that generates new knowledge by combining knowledge extracted by the first subsystem. In order to speed up the whole knowledge acquisition process a search algorithm is applied on a table of combinations of keywords characterizing the sentences of the text. Our knowledge discovery method is based on the use of our knowledge representation independent method ARISTA that accomplishes causal reasoning “on the fly” directly from text. The application of the method is demonstrated by the use of two examples. The first example concerns pneumonology and is found in a textbook and the second concerns cell apoptosis and is compiled from a collection of MEDLINE paper abstracts related to the recent proposal of a mathematical model of apoptosis.