大规模本体匹配系统

Q3 Computer Science
Abhisek Sharma, Sarika Jain, Archana Patel
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引用次数: 0

摘要

本体匹配通过发现本体实体之间的语义关系来解决语义异构问题。在过去的二十年里,本体匹配范式得到了长足的发展和改进。已经开发了50多个本体匹配系统,其中一些系统的性能非常好。然而,最初的改善率相当高,现在正在放缓。然而,仍有改进的空间,我们作为一个社区可以努力实现这一点。有鉴于此,我们开发了一个大规模本体匹配系统(LSMatch),该系统使用不同的匹配器来查找两个本体概念之间的相似性。LSMatch主要使用两个模块进行匹配。这些模块对本体的概念进行字符串相似度和同义词匹配。对于LSMatch的评估,我们在2021年本体对齐评估倡议(OAEI)中对其进行了测试。性能结果表明,LSMatch可以对大型本体进行匹配操作。LSMatch在解剖学、疾病和表型、会议、知识图和公共知识图(KG)轨迹上进行了评估。在所有这些曲目中,LSMatch的表现与其他系统不相上下。作为LSMatch的第一次参与,该系统显示出了潜力,还有改进的空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large Scale Ontology Matching System (LSMatch)
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. 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. 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. Being LSMatch’s first participation, the system showed potential and has room for improvement.
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来源期刊
Recent Advances in Computer Science and Communications
Recent Advances in Computer Science and Communications Computer Science-Computer Science (all)
CiteScore
2.50
自引率
0.00%
发文量
142
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