Exploring the Efficiency of Topic-Based Models in Computing Semantic Relatedness of Geographic Terms

Sadr, Hossein; Nazari, Mozhdeh; Pedram, Mir Mohsen; Teshnehlab, Mohammad

doi:10.22133/ijwr.2020.225866.1056

Exploring the Efficiency of Topic-Based Models in Computing Semantic Relatedness of Geographic Terms

Document Type : Original Article

Authors

Hossein Sadr ¹

Mozhdeh Nazari ²

Mir mohsen Pedram ³

Mohammad Teshnehlab ⁴

¹ Department of Computer Engineering Rasht Branch, Islamic Azad University Rasht, Iran

² Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran

³ Department of Electrical and Computer Engineering Faculty of Engineering, Kharazmi University Tehran, Iran

⁴ Industrial Control Center of Excellence, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

10.22133/ijwr.2020.225866.1056

Abstract

Large number of semantic relatedness measures have been presented since the last decades. In spite of an extensive number of studies that have been conducted in this field, the understanding of their foundation is still limited in real world applications. In this paper, the state-of-the-art semantic relatedness measures are surveyed and in the following a unified topic-based models is proposed to highlight their equivalences and propose bridges between their theoretical bases. Presentation of a comprehensive unified approach of topic based models induces readers to have common understanding of them in spite of the complexities and differences between their architecture and configuration details. Moreover, it may underlie fundamental development of these models. Comprehensive experiments in application of semantic relatedness of geographic phrases have been conducted to evaluate topic based models in comparison to ontology-based models. Based on the obtained results, not only topic-based models in comparison to ontology-based models confront with fewer restrictions in real world, but also their performance in computing semantic relatedness of geographic phrases is significantly superior to ontology-based models.

Keywords

Semantic Relatedness

Topic-based Models Latent Semantic Analysis

Latent Dirichlet Allocation

Explicit Semantic Analysis

Geographical Information Science Introduction