Learning Non-taxonomic Relationships of Financial Ontology

Abstract

Finance ontology is, in most cases, manually addressed. This results in a tedious development process and error prone that delay their applicability. This is why there is a need of domain ontology learning methods that built the ontology automatically and without human intervention. However, in this learning process, the discovery of non-taxonomic relationships has been recognized as one of the most difficult problems. In this paper, we propose a new methodology for learning non-taxonomic relationships and building financial ontology from scratch. Our new technique is based on using and adapting Open Information Extraction algorithms to extract and label domain relations between concepts. To evaluate our new method effectiveness, we compare the extracted non-taxonomic relations of our algorithm with related works in the same finance corpus. The results showed that our system is more accurate and more effective.