Automatic Information Extraction from Texts with Inference and Linguistic Knowledge Acquisition Rules

In this paper we present a novel methodology for automatic information extraction from natural language texts, based on the integration of linguistic rules, multiple ontologies and inference resources, integrated with an abstraction layer for linguistic annotation and data representation. The methodology allows ontology population with instances of events. The main contribution presented is related to the exploration of the flexibility of linguistic rules and domain knowledge representation, through their manipulation and integration by a reasoning system. The results from the case study indicate that the proposed approach is effective for the legal domain.