Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

TL;DR

This paper introduces a Bayesian personalized ranking-based model for link prediction in knowledge graphs, considering predicate-specific disjoint modeling, and demonstrates superior performance on large-scale datasets like YAGO2.

Contribution

It proposes a novel predicate-specific latent feature embedding model optimized with Bayesian personalized ranking for improved link prediction in knowledge graphs.

Findings

Predicate-specific modeling improves prediction accuracy.

Topological properties predict link prediction performance.

Our approach outperforms state-of-the-art methods on YAGO2.

Abstract

Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimization technique. Experimental results on large-scale knowledge bases such as YAGO2 show that our link prediction approach achieves substantially higher performance than several state-of-art approaches. We also show that for a given predicate the topological properties of the knowledge graph induced by the given predicate edges are key indicators of the link prediction performance of that predicate in the knowledge graph.