RelTopic:主题本体中基于图的语义关联度量及其在旧版新闻文章主题标注中的适用性

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Semantic Web Pub Date : 2022-09-01 DOI:10.3233/sw-222919
M. E. Ghosh, Nicolas Delestre, Jean-Philippe Kotowicz, C. Zanni-Merk, H. Abdulrab
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引用次数: 1

摘要

基于图的语义度量已被用于解决多个领域的问题。他们倾向于比较语义实体,以估计它们的相似性或相关性。语义相似性适用于层次结构或分类法,而语义相关性适用于本体。在这项工作中,我们提出了一个新的语义相关性度量,命名为Rel Topic,在主题本体中用于主题标记目的。与依赖文本资源的传统度量不同,Rel Topic考虑本体中实体的语义属性。因此,评估节点的相关性以及节点和边的权重。对旧版报刊文章的主题标注进行了相关性评价。为此,通过应用基于sparql的自动方法,从开放知识图派生出一个表示文章的主题本体,名为topic - opa。在旧的法国报纸《晨报》的上下文中给出了一个用例。生成的主题在人工注释器的帮助下使用双重评估方法进行评估。我们的方法显示了与人类所显示的非常接近的一致性。整个方法的重用演示了标记文章的不同上下文,最近(现代)报纸。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RelTopic: A graph-based semantic relatedness measure in topic ontologies and its applicability for topic labeling of old press articles
Graph-based semantic measures have been used to solve problems in several domains. They tend to compare semantic entities in order to estimate their similarity or relatedness. While semantic similarity is applicable to hierarchies or taxonomies, semantic relatedness is adapted to ontologies. In this work, we propose a novel semantic relatedness measure, named Rel Topic , within topic ontologies for topic labeling purposes. In contrast to traditional measures, which are dependent on textual resources, Rel Topic considers semantic properties of entities in ontologies. Thus, correlations of nodes and weights of nodes and edges are assessed. The pertinence of Rel Topic is evaluated for topic labeling of old press articles. For this purpose, a topic ontology representing the articles, named Topic-OPA, is derived from open knowledge graphs by applying a SPARQL-based automatic approach. A use-case is presented in the context of the old French newspaper Le Matin. The generated topics are evaluated using a dual evaluation approach with the help of human annotators. Our approach shows an agreement quite close to that shown by humans. The entire approach’s reuse is demonstrated for labeling a different context of articles, recent (modern) newspapers.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
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