Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms

Over the last decades, a multitude of semantic relatedness measures have been proposed. Despite an extensive amount of work dedicated to this area of research, the understanding of their foundation is still limited in real-world applications. In this paper, a unifying approach representing topic-based models is proposed and from which the state-of-the-art semantic relatedness measures are divided into two distinct types of topic-based and ontology-based models. Regardless of extensive researches in the field of ontology-based models, topic-based models have not been taken into account considerably. Consequently, the unified approach is able to highlight equivalences among these models and propose bridges between their theoretical bases. On the other hand, presenting a comprehensive unifying approach of topic-based models induces readers to have a common understanding of them despite the differences and complexities between their architecture and configuration details. In order to evaluate topic-based models in comparison to ontology-based models, comprehensive experiments in the application of semantic relatedness of geographic phrases have been applied. Empirical results have demonstrated that not only topic-based models in comparison to ontology-based models confront with fewer restrictions in the real world, but also their performance in computing semantic relatedness of geographic phrases is significantly superior to ontology-based models.