Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs

The problem of answering complex First-order Logic queries over incomplete knowledge graphs is receiving growing attention in the literature. A promising recent approach to this problem has been to exploit neural link predictors, which can be effective in identifying individual missing triples in the incomplete graph, in order to efficiently answer complex queries. A crucial advantage of this approach over other methods is that it does not require example answers to complex queries for training, as it relies only on the availability of a trained link predictor for the knowledge graph at hand. This approach, however, can be computationally expensive during inference, and cannot deal with queries involving negation. In this paper, we propose a novel approach that addresses all of these limitations. Experiments on established benchmark datasets demonstrate that our approach offers superior performance while significantly reducing inference times.