A random walk model for entity relatedness

Semantic relatedness is a critical measure for a wide variety of applications nowadays. Numerous models, including path-based, have been proposed for this task with great success in many applications during the last few years. Among these applications, many of them require computing semantic relatedness between hundreds of pairs of items as part of their regular input. This scenario demands a computationally efficient model to process hundreds of queries in short time spans. Unfortunately, Path-based models are computationally challenging, creating large bottlenecks when facing these circumstances. Current approaches for reducing this computation have focused on limiting the number of paths to consider between entities. Contrariwise, we claim that a semantic relatedness model based on random walks is a better alternative for handling the computational cost. To this end, we developed a model based on the well-studied Katz score. Our model addresses the scalability issues of Path-based models by pre-computing relatedness for all pair of vertices in the knowledge graph beforehand and later providing them when needed in querying time. Our current findings demonstrate that our model has a competitive performance in comparison to Path-based models while being computationally efficient for high-demanding applications.