Knowledge acquisition from prescriptive texts

There is a growing interest for the application of artificial intelligence in law. Research activities have investigated different areas : formulating legislation with the aid of logical models, legal reasoning, case-based reasoning, developing expert systems applied to the juridical or administrative domains. In project A.C.A.T. (Acquisition des connaissances et analyse de textes), we explore the possibility of creating knowledge bases by exploiting information contained in texts which are used in organizations. Our research focuses on a particular category of prescriptive texts : regulations from the Government of Québec. In order to verify these hypothesis we are developing a knowledge-acquisition system which will enable human specialists to transform a prescriptive text into the form of a knowledge base which can be exploited by an inference engine. We introduce a model which enables us to identify three layers in prescriptive texts : the macrostructure, the microstructure and the dominial component. We describe the general architecture of the knowledge acquisition system which enables us to create “deontic” knowledge bases. We present the main knowledge structures used by the knowledge acquisition sub-system : the text grammars of macrostructure and microstructure.