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The increase in the number of users on the internet and the advancement in information technology have spiked the generation of information to an unprecedented level making information retrieval and web mining a difficult task. Semantic technologies can help improve the results of web mining by providing constructs that can help represent the web documents in a machine-understandable manner. To keep providing semantically rich services while keeping this surge in the amount of information in mind, we have to work towards ways to make the process of information management efficient while retaining its effectiveness. One of the ways to accomplish the above task is to improvise the knowledge organization in a manner that every piece of information is in its designated place. This paper discusses and addresses the problems with current knowledge organization methodologies and presents an algorithm to alter the available OWL ontologies. The authors were able to get a noticeable improvement in the amount of storage used by the ontology with fewer axioms without losing any information.

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Background: Ontology matching provides a solution to the semantic heterogeneity problem by finding semantic relationships between entities of ontologies. Over the last two decades, there has been considerable development and improvement in the ontology matching paradigm. More than 50 ontology matching systems have been developed, and some of them are performing really well. However, the initial rate of improvement was measurably high, which now is slowing down. However, there still is room for improvement, which we as a community can work towards to achieve. Method: In this light, we have developed a Large Scale Ontology Matching System (LSMatch), which uses different matchers to find similarities between concepts of two ontologies. LSMatch mainly uses two modules for matching. These modules perform string similarity and synonyms matching on the concepts of the ontologies. Results: For the evaluation of LSMatch, we have tested it in Ontology Alignment Evaluation Initiative (OAEI) 2021. The performance results show that LSMatch can perform matching operations on large ontologies. LSMatch was evaluated on anatomy, disease and phenotype, conference, Knowledge graph, and Common Knowledge Graphs (KG) track. In all of these tracks, LSMatch’s performance was at par with other systems. Conclusion: Being LSMatch’s first participation, the system showed potential and has room for improvement.