{"title":"Ontology key concepts interpretation","authors":"J. Vrana, M. Mach","doi":"10.1109/SAMI.2010.5423736","DOIUrl":null,"url":null,"abstract":"Semantic Web has reached the point where basic research is supposed to be replaced (at least partially) by research aiming at practical aspects of using the Semantic Web. Ontologies, as the essential technology in this area, have been under the spot light which produced some results, e.g. the Semantic Web Search engines like Watson, Swoogle etc. These help in finding and locating semantic information on the Web. However, they do not support users to quickly understand what an ontology is about, what information it contains. We argue in this paper that vector description, as a snapshot of data comprised by ontologies, may help while understanding of particular ontology. In other words, instead of depicting a whole ontology all together, we prefer reduction of information given and therefore avoid users being overwhelmed and thus help with orienting in a wide offering of ontologies on the web.","PeriodicalId":306051,"journal":{"name":"2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAMI.2010.5423736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Semantic Web has reached the point where basic research is supposed to be replaced (at least partially) by research aiming at practical aspects of using the Semantic Web. Ontologies, as the essential technology in this area, have been under the spot light which produced some results, e.g. the Semantic Web Search engines like Watson, Swoogle etc. These help in finding and locating semantic information on the Web. However, they do not support users to quickly understand what an ontology is about, what information it contains. We argue in this paper that vector description, as a snapshot of data comprised by ontologies, may help while understanding of particular ontology. In other words, instead of depicting a whole ontology all together, we prefer reduction of information given and therefore avoid users being overwhelmed and thus help with orienting in a wide offering of ontologies on the web.
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本体关键概念解释
语义网已经到了这样一个阶段,基础研究应该被针对使用语义网的实际方面的研究所取代(至少部分地)。本体论作为这一领域的核心技术,已经成为人们关注的焦点,并产生了一些成果,如语义网搜索引擎如Watson、Swoogle等。这有助于查找和定位Web上的语义信息。然而,它们不支持用户快速理解本体是关于什么的,它包含什么信息。我们在本文中认为,向量描述作为由本体组成的数据快照,可能有助于理解特定的本体。换句话说,我们更倾向于减少给出的信息,从而避免用户不知所措,从而有助于在web上广泛提供的本体中进行定向,而不是一起描述整个本体。
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