OWL中的领域本体和词网:建模选项

LDV Forum Pub Date : 2007-07-01 DOI:10.21248/jlcl.22.2007.92
H. Lüngen, Angelika Storrer
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引用次数: 6

摘要

词网是遵循普林斯顿WordNet项目(Fellbaum 1998,从此称为PWN1)设计原则的词汇参考系统。领域本体(或特定于领域的本体,例如GOLD2或GENE本体3)以支持对该领域中的对象及其之间关系的自动推理的格式表示关于特定领域的知识(参见Erdmann 2001,78)。词网在语篇分析、词汇和主题链接、衔接分析、自动分词和连接、回指消解和信息提取等方面有着广泛的应用。当这些应用程序处理处理特定领域的文档时,需要将有关领域本体中表示的特定于领域的词汇表的知识与表示通用词汇表的词汇库(如PWN)结合起来。在这种情况下,使用公共表示语言表示两种类型资源中的实体和关系并使其相互关联是很有用的。在我们的研究小组“文本技术信息建模”中,我们选择OWL作为实现此目的的通用格式。由于我们的项目主要与德语文档有关,因此我们开发了一个OWL模型,该模型将德语wordnet GermaNet(以下简称GN)5与特定领域的本体联系起来,其方法受到Magnini/Speranza(2002)中提出的插件模型的启发。Kunze等人描述了我们的方法(即将出现);使用GN和领域本体TermNet6(以下简称TN)的代表性子集作为数据和protg进行评估
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Domain ontologies and wordnets in OWL: Modelling options
Word nets are lexical reference systems that follow the design principles of the Princeton WordNet project (Fellbaum 1998, henceforth referred to as PWN1). Domain ontologies (or domain-specific ontologies, e.g. GOLD2 or the GENE Ontology3) represent knowledge about a specific domain in a format that supports automated reasoning about the objects in that domain and the relations between them (cf. Erdmann 2001, 78). Word nets have been used in various applications of text processing, e.g. discourse parsing, lexical and thematic chaining, cohesion analyses, automatic segmentation and linking, anaphora resolution, and information extraction. When these applications process documents dealing with a specific domain, one needs to combine knowlegde about the domain-specific vocabulary represented in domain ontologies with lexical repositories representing general vocabulary (like PWN). In this context, it is useful to represent and interrelate the entities and relations in both types of resources using a common representation language. In our research group “Text-technological Information Modelling4” we chose OWL as a common format for this purpose. Since our projects are mainly concerned with German documents, we developed an OWL model that relates the German wordnet GermaNet (henceforth referred to as GN)5 with domain-specific ontologies in an approach that was inspired by the Plug-In model proposed in Magnini/Speranza (2002). Our approach is decribed in Kunze et al. (to appear); it was evaluated using representative subsets of GN and of the domain ontology TermNet6 (henceforth referred to as TN) as data and Protégé
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