Large-scale Knowledge Base Completion: Inferring via Grounding Network Sampling over Selected Instances

Constructing large-scale knowledge bases has attracted much attention in recent years, for which Knowledge Base Completion (KBC) is a key technique. In general, inferring new facts in a large-scale knowledge base is not a trivial task. The large number of inferred candidate facts has resulted in the failure of the majority of previous approaches. Inference approaches can achieve high precision for formulas that are accurate, but they are required to infer candidate instances one by one, and extremely large candidate sets bog them down in expensive calculations. In contrast, the existing embedding-based methods can easily calculate similarity-based scores for each candidate instance as opposed to using inference, so they can handle large-scale data. However, this type of method does not consider explicit logical semantics and usually has unsatisfactory precision. To resolve the limitations of the above two types of methods, we propose an approach through Inferring via Grounding Network Sampling over Selected Instances. We first employ an embedding-based model to make the instance selection and generate much smaller candidate sets for subsequent fact inference, which not only narrows the candidate sets but also filters out part of the noise instances. Then, we only make inferences within these candidate sets by running a data-driven inference algorithm on the Markov Logic Network (MLN), which is called Inferring via Grounding Network Sampling (INS). In this process, we especially incorporate the similarity priori generated by embedding-based models into INS to promote the inference precision. The experimental results show that our approach improved Hits@1 from 32.911% to 71.692% on the FB15K dataset and achieved much better AP@n evaluations than state-of-the-art methods.