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Automatic Generation of Semantic Features and Lexical Relations Using OWL Ontologies

机译:使用猫头鹰本体自动生成语义特征和词汇关系

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Semantic features are theoretical units of meaning-holding components which are used for representing word meaning. These features play a vital role in determining the kind of lexical relation which exists between words in a language. Although such model of meaning representation has numerous applications in various fields, the manual derivation of semantic features is a cumbersome and time consuming task. We aim to elevate this process by developing an automated semantic feature extraction system based on ontological models. Such an approach will provide explicit word meaning representation, and enable the computation of lexical relations such as synonym and antonymy. This paper describes the design and implementation of a prototype system used for automatically deriving componential formulae, and computing lexical relations between words from a given OWL ontology. The system has been tested on a number of ontologies, both English and Arabic. Results of the evaluation indicate that the system was able to provide necessary componential formulae for highly-axiomed ontologies. With regards to computing lexical relations, the system performs better when predicting antonyms, with an average precision of 40%, and an average recall of 75%. We have also found a strong relation between ontology expressivity and system performance.
机译:语义特征是含义控件组件的理论单位,用于表示词含义。这些特征在确定语言中的单词之间存在的词性关系中起着至关重要的作用。虽然这种含义表示的模型在各种领域具有许多应用,但语义特征的手动推导是一个繁琐且耗时的任务。我们的目标是通过开发基于本体模型的自动语义特征提取系统提升这一过程。这样的方法将提供显式的单词含义表示表示,并能够计算词汇关系,例如同义词和反义。本文介绍了用于自动导出成分公式的原型系统的设计和实现,以及计算给定猫头鹰本体的单词之间的词汇关系。该系统已经在许多本体中进行了测试,英语和阿拉伯语。评估结果表明该系统能够为高度公理的本体提供必要的成分公式。关于计算词汇关系,系统在预测反义词时执行更好,平均精度为40%,平均召回为75%。我们还发现了本体表现与系统性能之间的强有力。

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