Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
This work addresses scalability issues in ontology alignment for semantic web and data integration applications, presenting an incremental improvement.
The paper tackled the challenge of aligning large ontologies by dividing the task into smaller subtasks using a lexical index and neural embeddings, achieving encouraging results on Ontology Alignment Evaluation Initiative datasets.
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state-of-the-art systems.