Authors:
Omar El Idrissi Esserhrouchni
1
;
Bouchra Frikh
2
and
Brahim Ouhbi
1
Affiliations:
1
Moulay Ismaïl University, Morocco
;
2
Sidi Mohamed Ben Abdellah University, Morocco
Keyword(s):
Ontology Learning, Financial Ontology, Non-taxonomic Relationships Extraction, Knowledge Acquisition, Open Information Extraction.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Collaboration and e-Services
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Enterprise Ontology
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
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.