本体对齐的基于Agent的prot插件

G. Rajakaruna, A. Karunananda, S. Jayalal
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引用次数: 1

摘要

本体被广泛用于构建领域知识。目前,由于已有大量的本体,对已有本体中的知识进行重用的需求很大。由于本体是复杂的结构,共享来自不同本体的知识已成为一项繁琐的任务。本体对齐是一种流行的知识共享技术,用于异构信息系统之间的信息共享。有许多用于本体对齐的技术,并且该领域仍然面临许多挑战。由于本体之间的多重关系的固有性质,它假设了多代理系统技术是一种更好的技术,可以在很少的人为干预下实现本体对齐的自动化。本文提出了基于多智能体的本体对齐方法,该方法作为流行的本体建模环境proprot的插件而开发。它模拟了不同的过程如何在人类大脑中相互作用,以智能地执行某些活动。实际上,提出的解决方案使用代理通信、协商和协调作为探索本体之间语义关系的主要方法。生成的本体可以用作运行在输入本体上的信息系统之间的共享理解。利用农业领域的两个本体对该方法的有效性进行了评价。结果表明,该系统发现语义关系的准确率超过70%,可以在一定程度上解决本体对齐的复杂性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agent based Protégé plugin for ontology alignment
Ontologies are extensively used to strcuture the domain knowledge. Nowadays, with the large amount of ontologies already available, there is a high demand for reusing the knowledge in existing ontologies. Since ontologies are complex structures, sharing of knowledge coming from various ontologies has become a tedious task. Ontology alignment is a popular knowledge sharing technique that is used to share information between heterogeneous information systems. There are numerous techniques for the alignment of ontologies, and the field still faces many challenges. Due to the inherent nature of multiple relationships among the ontologies, it postulates that the Multi-Agent System technology is a better technology to automate the ontology alignment with little human intervention. This paper presents multi-agent based approach for ontology alignment developed as a plug in for the popular ontological modelling environment known as Protégé. It simulates how different processes interactively operate inside the human mind to perform certain activities intelligently. Indeed, the proposed solution uses agent communication, negotiation, and coordination as the primary method of exploring the semantic relationships between the ontologies. The generated ontology could be used as a shared understanding between information systems that are running on input ontologies. The success of the proposed approach was evaluated by using two ontologies of agricultural domain. It was evident that the system could discover over 70% accurate semantic relationships, and thus, the authors claim that the proposed approach could resolve the complexity in ontology alignment for certain extend.
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