Abstract
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.
Highlights
Edge computing technology gain increasingly more attention because of its special advantages, and it has been applied in various types of domains [1]–[3]
For measuring the semantic similarity among entities, the knowledge graph embedding and transformer networks were introduced in the model
The results show that directly using the output of BERT leads to rather poor performances, which achieve an aver
Summary
Edge computing technology gain increasingly more attention because of its special advantages, and it has been applied in various types of domains [1]–[3] It provides a new architecture for personalized recommendation systems. As a branch of word semantics similarity computing, entity semantic similarity computing or similarity measuring is the essential work of entity-related personal searching in the edge environment. We use context words in a pretraining corpus instead of properties, and the state-of-the-art methods are to compute the similarity between two distributional word vectors [13]; this approach has been shown to perform well on semantic similarity and relatedness tasks [14], [15].
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