Ontology learning from text

Abstract

Ontology Learning is the semi-automatic and automatic extraction of ontology from a natural language text, using text-mining and information extraction. Each ontology primarily represents some concepts and their corresponding relationships, both of which can be extracted from a natural language text. Current state-of-the-art ontology learning systems are not able to extract union and intersection relationships between the concepts while extracting an ontology from the text. These relationships can prove to be critical in some domains like the medical domain, where such expressive relationships can capture the missing knowledge of some concepts in the domain. By learning such relationships and then reasoning over the learned ontology, we might be able to answer additional queries regarding the properties of those medical concepts. In this project, I have proposed an architecture to extract these types of relations from a text in the medical domain, using Semantic and Syntactic Similarity and Graph Search. In the evaluation, I enrich a Disease Ontology with union and intersection axioms that are learned from the text. I have evaluated the architecture using metrics such as Precision, Recall, and F1-scores, by comparing the original and the learned axioms.