Use of Ontology Learning in Information System Integration: A Literature Survey

Abstract

Ontology-based information integration is a useful method to integrate heterogeneous data at the semantic level. However, there are some bottlenecks of the traditional method for constructing ontology, i.e., time-consuming, error-prone, and semantic loss. Ontology learning is a kind of ontology construction approach based on machine learning, it provides a new opportunity to tackle the above bottlenecks. Especially, it could be employed to construct ontologies and integrate large-scale and heterogeneous data from various information systems. This paper surveys the latest developments of ontology learning and highlights how they could be adopted and play a vital role in the integration of information systems. The recent techniques and tools of ontology learning from text and relational database are reviewed, the possibility of using ontology learning in information integration were discussed based on the mapping results of the aforementioned bottlenecks and features of ontology learning. The potential directions for using ontology learning in information systems integration were given.