Subgraph-based feature fusion models for semantic similarity computation in heterogeneous knowledge graphs

Semantic similarity is a fundamental task in natural language processing that determines the similarity between two concepts within a taxonomy. For example, a pair of words (e.g., car and bike) appear similar because they share the same category (e.g., vehicle). Numerous computation methods, such as distance-based and feature-based approaches, are proposed to precisely depict this similarity. As knowledge graphs become heterogeneous (e.g., DBpedia), existing methods have limitations on utilizing multi-view features (e.g., abstract, structure, and categories). On the one hand, some features are incomplete for various reasons, reducing the effectiveness of embedding methods. On the other hand, the hidden connections among multi-view features are omitted by existing approaches. To address the problems mentioned above, we first extract three subgraphs from a heterogeneous knowledge graph and then combine various embedding approaches to capture the global semantics of each concept. Next, we offer subgraph-based feature fusion models that improve concept representation by fusing multi-view features. Finally, we devise mixed computation methods to calculate the semantic similarity between the two concepts. Experiment results show that multi-view features, particularly the abstract feature, can effectively improve the performance of the proposed methods. Compared to existing approaches, our methods significantly improve the Pearson correlation coefficient by about 7%. The source code of this paper is available at: https://github.com/fiego/SubgraphSS.