Abstract

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 RelTopic, within topic ontologies for topic labeling purposes. In contrast to traditional measures, which are dependent on textual resources, RelTopic considers semantic properties of entities in ontologies. Thus, correlations of nodes and weights of nodes and edges are assessed. The pertinence of RelTopic 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.

中文翻译：

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RelTopic：主题本体中基于图形的语义相关性度量及其在旧新闻文章主题标签中的适用性

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。虽然语义相似性适用于层次结构或分类，但语义相关性适用于本体。在这项工作中，我们提出了一种新的语义相关性度量，命名为相对话题，在主题本体中用于主题标记目的。与依赖文本资源的传统措施相比，相对话题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。的针对性相对话题评估旧新闻文章的主题标签。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。在旧法国报纸Le Matin的上下文中介绍了一个用例。在人工注释者的帮助下，使用双重评估方法对生成的主题进行评估。我们的方法显示了与人类显示的非常接近的协议。整个方法的重用被展示为标记文章的不同上下文，最近的（现代）报纸。