ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Foundations of Computing and Decision Sciences Pub Date : 2022-02-01 DOI:10.2478/fcds-2022-0003
Dawid Wisniewski, Jedrzej Potoniec, A. Ławrynowicz
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Abstract

Abstract Glossary of Terms extraction from textual requirements is an important step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human interpretable, rule-based method named ReqTagger, which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare ReqTagger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of ReqTagger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of ReqTagger.
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ReqTagger:一个用于从本体需求中自动提取术语表的基于规则的标签
摘要从文本需求中提取术语表是本体工程方法论中的一个重要步骤。尽管最初它是手动执行的,但最近几年已经表明,一定程度的自动化是可能的。基于这些有前景的方法,我们引入了一种新的、可人工解释的、基于规则的方法ReqTagger,该方法可以自动从文本需求中提取本体实体(类或实例)和关系(数据或对象属性)的候选者。我们将ReqTagger与现有的自动方法进行了比较,评估基准包括550多个需求,并标记了1700多个实体和预期提取的关系。我们讨论了ReqTagger的质量,并详细说明了它为什么优于其他方法。我们还发布了评估数据集和ReqTagger的实现。
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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