语义Web数据中类型类和类型关系的动态发现
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
摘要：
近年来，语义Web技术的不断发展和越来越多的用户采用加速了将显式语义与Web上的数据相结合的进程。随着语义Web上RDF（Resource Description Framework）数据的快速增长，处理大规模语义图数据变得越来越具有挑战性。从原始RDF构造摘要图结构可以帮助获得语义类型关系并降低用于图处理目的的计算复杂性。本文针对RDF图中的图摘要问题，提出了一种从RDF图数据自动构建摘要图结构的方法。此外，我们还引入了一种帮助发现最佳类相异性阈值的措施和一种自动发现类型类的有效方法。在未来的工作中，我们计划研究所提出的方法的可扩展性的进一步改进方案。
英文摘要：
The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
