Multi-view Knowledge Graph Embedding for Link Prediction by PLSA Method

Abstract

Reasoning refers to the extraction of information from a graph in an embedded space through link prediction. Link prediction involves identifying the missing edge between a pair of entities. While graph features are utilized to locate these missing edges, similarity-based methods often fail to consider all of the graph features. Multi-view methods leverage multiple perspectives, each offering a different aspect of data analysis. In this paper, we present a novel approach called the Multi-View Probabilistic Latent Semantic Analysis (PLSA) based PLSA algorithm. This algorithm calculates three distinct views, enabling a comprehensive analysis of the underlying data. The first one refers to the viewing probability of a node in the head, tail, or relation separately. The second view highlights the significance of a suggested tail in information representation, while the third view evaluates the quality of information flow within a set comprising the head, relation, and tail. These three views are combined to derive the optimal score function by PLSA method. Test results indicate that the proposed method ensures that the correct solution lies within an acceptable range, with a hit rate exceeding 40% in the Freebase dataset. Experimental results further demonstrate the effectiveness of the proposed algorithms compared to state-of-the-art methods.