Arabic ontology learning using deep learning

Saeed Al-Bukhitan, T. Helmy, A. Al-Nazer
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引用次数: 23

Abstract

Ontology, the backbone of Semantic Web, is defined as the formal specification of conceptual hierarchy with relationships between concepts. Ontology Learning (OL) is a process to create an ontology from text automatically or semi-automatically. OL is an important topic in the Semantic Web field in the last two decades but it is still not mature in Arabic not like Latin languages. Currently, there is a limited support for using knowledge from Arabic literature automatically in semantically-enabled systems. Deep Learning (DL), an artificial neural networks learning based application, has proved a good improvement in multiple areas including text mining. By using DL, it is possible to have word embedding as distributed word representations from textual data. The application of DL to aid Arabic ontology development remains largely unexplored. This paper investigates the performance of implementing DL with Arabic ontology learning tasks using major models such as Continuous Bag of Words (CBOW) and Skip-gram. Initial performance results are promising as an effective application of Arabic ontology learning.
使用深度学习的阿拉伯语本体学习
本体是语义网的支柱,是概念间关系的概念层次的形式化规范。本体学习(Ontology Learning, OL)是自动或半自动地从文本中创建本体的过程。语义网是近二十年来语义网领域的一个重要课题,但与拉丁语言不同,它在阿拉伯语中还不成熟。目前,在语义支持系统中自动使用阿拉伯文学知识的支持有限。深度学习(Deep Learning, DL)是一种基于人工神经网络学习的应用,在包括文本挖掘在内的多个领域都有很好的改进。通过使用深度学习,可以将词嵌入作为文本数据中的分布式词表示。DL在阿拉伯语本体开发中的应用在很大程度上仍未被探索。本文研究了使用连续词袋(CBOW)和Skip-gram等主要模型实现阿拉伯语本体学习任务的深度学习的性能。作为阿拉伯文本体学习的有效应用,初步的性能结果是有希望的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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