面向表达的本体学习:综述

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computer Science Review Pub Date : 2024-12-05 DOI:10.1016/j.cosrev.2024.100693
Pauline Armary, Cheikh Brahim El-Vaigh, Ouassila Labbani Narsis, Christophe Nicolle
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引用次数: 0

摘要

本体学习,特别是公理学习,是一项具有挑战性的任务,其重点是构建表达性和可决定的本体。文献提出了一些旨在解决公理和规则学习固有复杂性的研究工作,这些学习旨在从不同的数据源自动推断逻辑结构。本文的目的是对该领域的现有工作进行全面的回顾。它旨在批判性地分析当前方法的贡献和局限性,提供对最新技术的清晰理解,并确定需要进一步研究的领域。
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Ontology learning towards expressiveness: A survey
Ontology learning, particularly axiom learning, is a challenging task that focuses on building expressive and decidable ontologies. The literature proposes several research efforts aimed to resolve the complexities inherent in axiom and rule learning, which seeks to automatically infer logical constructs from diverse data sources. The goal of this paper is to conduct a comprehensive review of existing work in this domain. It aims to critically analyze the contributions and limitations of current approaches, providing a clear understanding of the state-of-the-art and identifying areas where further research is needed.
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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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