Entity Thematic Similarity Measurement for Personal Explainable Searching Services in the Edge Environment

Currently, search engines are widely used to address the information overload problem. Different from the existing client-and-sever-based frameworks, edge computing (EC) technology can provide a new architecture for personalized searching services. The issue of how to measure the similarities among entities by using the context information generated by user behavior in the edge environment is vital in the task of entity-related personal searching. To analyze and measure the similarities among entities, existing methods are mainly based on either the textual content or relationships unilaterally, and the results usually have a fixed degree of similarity. However, the similarities among entities depend on the set of properties that belong to the entities. This approach should be used in determining the similarity or dissimilarity associated with the surrounding context. To address this limitation, we propose a novel semantic augmentation method with a double attention mechanism. The method refers to a dynamic representation learning process that maps an entity to a real number vector in semantic space. In this article, different from the existing similarity measurement methods, we propose a thematic similarity measure approach to analyze the connotation and denotation similarities among entities. The experimental results show that the double attention mechanism leads to a significant improvement in the entity thematic similarity measurement tasks. The model can make a separation among the entities from different domains effectively. In addition, it can take similar entities that are closer in the same domain. It also shows excellent performance on the task of entity thematic similarity, which makes the recommendation results more explainable.