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Learning concept hierarchies from textual resources for ontologies construction

机译：从文本资源中学习概念层次结构以进行本体构建

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摘要

Ontologies play a very important role in knowledge management and the Semantic Web, their use has been exploited in many current applications. Ontologies are especially useful because they support the exchange and sharing of information. Ontology learning from text is the process of deriving high-level concepts and their relations. An important task in ontology learning from text is to obtain a set of representative concepts to model a domain and organize them into a hierarchical structure (taxonomy) from unstructured information. In the process of building a taxonomy, the identification of hypernym/hyp-onym relations between terms is essential. How to automatically build the appropriate structure to represent the information contained in unstructured texts is a challenging task. This paper presents a novel method to obtain, from unstructured texts, representative concepts and their taxonomic relationships in a specific knowledge domain. This approach builds a concept hierarchy from a specific-domain corpus by using a clustering algorithm, a set of linguistic patterns, and additional contextual information extracted from the Web that improves the discovery of the most representative hypernym/hyponym relationships. A set of experiments were carried out using four different corpora. We evaluated the quality of the constructed taxonomies against gold standard ontologies, the experiments show promising results.
机译：本体在知识管理和语义网中起着非常重要的作用，它们的使用已在许多当前的应用程序中得到利用。本体特别有用，因为它们支持信息的交换和共享。从文本中学习本体是推导出高级概念及其关系的过程。从文本进行本体学习的一项重要任务是获得一组代表性概念，以对领域进行建模，并根据非结构化信息将其组织为分层结构（分类法）。在建立分类法的过程中，术语之间的上位词/ hyp-onym关系的识别至关重要。如何自动构建适当的结构来表示非结构化文本中包含的信息是一项具有挑战性的任务。本文提出了一种从非结构化文本中获取代表性概念及其在特定知识领域中的分类关系的新颖方法。这种方法通过使用聚类算法，一组语言模式以及从Web提取的其他上下文信息来从特定域语料库构建概念层次结构，从而改善了对最有代表性的上位词/同义词关系的发现。使用四个不同的语料库进行了一组实验。我们根据金标准本体评估了构建分类法的质量，实验显示出令人鼓舞的结果。

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