基于本体驱动的概念建模和自然语言处理的本体开发方法——以旅游业为例

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
S. Haridy, R. Ismail, N. Badr, M. Hashem
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引用次数: 2

摘要

本体为表示、重用和共享领域知识提供了一种强大的方法。它们被广泛应用于各种学科,包括人工智能、知识工程、生物医学信息学等等。由于几个原因,开发领域本体是一项具有挑战性的任务。其中一个原因是,这是一个复杂而耗时的过程。多种本体开发方法已经被提出。但是,在发展过程中包括更多的活动(如浓缩)和加强其他活动(如概念化)方面还有改进的余地。本文提出了一种增强的本体开发方法(ON-ODM)。本体驱动的概念建模(ODCM)和自然语言处理(NLP)是提出的方法的基础。ODCM被定义为利用来自不同领域的本体论思想来构建改进概念建模的工程工件。自然语言处理是指使用计算机技术来分析人类语言的科学学科。将提出的ON-ODM应用于构建旅游本体,该本体将有利于包括电子旅游在内的各种应用。生成的本体基于能力问题(CQs)和质量指标进行评估。验证了本体回答涵盖领域专家指定的所有CQ组的SPARQL查询。使用质量度量将生成的本体与四个现有的旅游本体进行比较。例如,根据与简洁性相关的度量,生成的本体在与其他本体相比获得第一名的排名,而在可理解性方面获得第二名的排名。这些结果表明,利用ODCM和NLP分别可以促进和改进开发过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study
Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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