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Learning non-taxonomical semantic relations from domain texts

机译：从领域文本中学习非分类语义关系

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摘要

Ontology of a domain mainly consists of concepts, taxonomical (hierarchical) relations and non-taxonomical relations. Automatic ontology construction requires methods for extracting both taxonomical and non-taxonomical relations. Compared to extensive works on concept extraction and taxonomical relation learning, little attention has been given on identification and labeling of non-taxonomical relations in text mining. In this paper, we propose an unsupervised technique for extracting non-taxonomical relations from domain texts. We propose the VF*ICF metric for measuring the importance of a verb as a representative relation label, in much the same spirit as the TF*IDF measure in information retrieval. Domain-relevant concepts (nouns) are extracted using techniques developed earlier. Candidate non-taxonomical relations are generated as (SVO) triples of the form (subject, verb, object) from domain texts. A statistical method with log-likelihood ratios is used to estimate the significance of relationships between concepts and to select suitable relation labels. Texts from two domains, the Electronic Voting (EV) domain texts and the Tenders and Mergers (TNM) domain texts are used to compare our method with one of the existing approaches. Experiments showed that our method achieved better performance in both domains.
机译：领域的本体主要包括概念，分类学（层次）关系和非分类学关系。自动本体构造需要用于提取分类学和非分类学关系的方法。与有关概念提取和分类关系学习的大量工作相比，文本挖掘中对非分类关系的识别和标记的关注很少。在本文中，我们提出了一种从域文本中提取非分类关系的无监督技术。我们提出了VF * ICF度量标准，用于测量动词作为代表关系标签的重要性，其精神与信息检索中的TF * IDF度量标准大致相同。与域相关的概念（名词）是使用较早开发的技术提取的。候选非分类关系从域文本生成为（SVO）三重形式的形式（主题，动词，宾语）。具有对数似然比的统计方法用于估计概念之间关系的重要性并选择合适的关系标签。来自两个域的文本，即电子投票（EV）域文本和招标与合并（TNM）域文本，用于将我们的方法与现有方法之一进行比较。实验表明，我们的方法在两个领域都取得了较好的性能。

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