OI-LLM: A Scalable Framework to Integrate Large Scale Ontologies Using Large Language Model

Abstract

Ontology integration plays a vital role in forming a unified knowledge base through existing knowledge. The integration process is triggered through ontology matching and ontology merging processes. The scale of ontologies dominates the ontology matching process. Earlier researchers have used the Ontology (Meta) Matching (OMM) technique to generate an efficient set of ontology alignments. The set of ontology alignments is used to match two ontologies. This process involves limitations such as the help of domain experts in choosing applicable similarity measures, opting for appropriate and applicable resources (such as WordNet, UMLS, etc.) to generate efficient semantic similarity, and requiring more computations to generate alignments. This raises the issue of increased computational complexity. To resolve this problem, researchers have employed a divide-and-conquer algorithmic strategy for executing multiple matching tasks concurrently with parallel processing. There is still a need to improve computational complexity while handling the integration of large-scale ontologies. With such needs, this article proposes a general and scalable framework to integrate large-scale ontologies. The proposed algorithm uses nature-inspired computing algorithms to apply the natural behavior of the ant-colony optimization algorithm to balance and optimize the working behavior of the machines during parallel processing. The LLM is used to generate highly efficient similarity results during the matching process, which results in a cohesive integrated ontology. The OAEI Anatomy track is used to test the performance of the framework to assess the computational time. The experimental results show that there is an improvement in computational time. It is possible to achieve the principles of ontology coherence and entity coverage.