Event extraction based on open information extraction and ontology

TL;DR

This thesis presents a method for extracting events from natural language texts using open information extraction and ontology modeling, achieving good performance but requiring significant expert input, with proposed automation improvements.

Contribution

It introduces an approach combining open information extraction and ontology for event extraction, reducing expert intervention through automatic adaptation and correspondence techniques.

Findings

Good performance in event extraction tasks

Significant expert intervention needed in classifier construction

Proposed automation reduces manual effort

Abstract

The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system for the relationship extraction, to highlight the importance of OIEs in event extraction, and we used the ontology to the event modeling. We tested the results of our approach with test metrics. As a result, the two-level event extraction approach has shown good performance results but requires a lot of expert intervention in the construction of classifiers and this will take time. In this context we have proposed an approach that reduces the expert intervention in the relation extraction, the recognition of entities and the reasoning which are automatic and based on techniques of adaptation and correspondence. Finally, to prove the relevance of the extracted results, we conducted a set of experiments using different test metrics as well as a comparative study.