SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

Abstract

In recent years, more and more exciting sources of data have been modeled as Knowledge Graphs (KGs). This modeling represents both structural relationships and the entity specific multi-modal data in KGs. In various data analytic pipelines and Machine Learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile content like categorical relations between classes or their attribute literals like strings, timestamp or numeric data. In this paper we propose SimE4KG framework as a resource providing generic open source modules that compute semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF-KG, including the experiment results, hyper-parameter setup, and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed semantic analytics stack SANSA.