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Data on Information Technology Described by Researchers at Tsinghua University (A semantic similarity measure based on information distance for...

August 5, 2014



Data on Information Technology Described by Researchers at Tsinghua University (A semantic similarity measure based on information distance for ontology alignment)

By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Current study results on Information Technology have been published. According to news originating from Shenzhen, People's Republic of China, by VerticalNews correspondents, research stated, "Ontology alignment is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the alignment between them before processing across them."

Our news journalists obtained a quote from the research from Tsinghua University, "Many efforts have been conducted to automate the alignment by discovering the correspondence between entities of ontologies. However, some problems are still obvious, and the most crucial one is that it is almost impossible to extract semantic meaning of a lexical label that denotes the entity by traditional methods. In this paper, ontology alignment is formalized as a problem of information distance metric. In this way, discovery of optimal alignment is cast as finding out the correspondences with minimal information distance. We demonstrate a novel measure named link weight that uses semantic characteristics of two entities and Google page count to calculate an information distance similarity between them. The experimental results show that our method is able to create alignments between different lexical entities that denotes the same ones."

According to the news editors, the research concluded: "These results outperform the typical ontology alignment methods like PROMPT (Noy and Musen, 2000) [38], QOM (Ehrig and Staab, 2004) [12], and APFEL (Ehrig et al., 2005) [13] in terms of semantic precision and recall."

For more information on this research see: A semantic similarity measure based on information distance for ontology alignment. Information Sciences, 2014;278():76-87. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier - www.elsevier.com; Information Sciences - www.elsevier.com/wps/product/cws_home/505730)

The news correspondents report that additional information may be obtained from Y. Jiang, Tsinghua Univ, Grad Sch Shenzhen, Tsinghua Southampton Web Sci Lab Shenzhen, Shenzhen 518055, People's Republic of China. Additional authors for this research include X.M. Wang and H.T. Zheng.

Keywords for this news article include: Asia, Shenzhen, Information Technology, People's Republic of China

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Information Technology Newsweekly


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