Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding

Abstract

The knowledge base (KB) completion problem is usually formulated as a link prediction problem. Such formulation is incapable of capturing certain application scenarios when the KB contains multi-fold relations. In this paper, we present a new formulation of KB completion, called instance reconstruction. Unlike its link-prediction counterpart, which has linear complexity in the size of the KB, this problem has its complexity behave as a high-degree polynomial. This presents a significant challenge in developing scalable instance reconstruction algorithms. In this paper, we present a novel knowledge embedding model (RAE) and build on it an instance reconstruction algorithm (SIR). The SIR algorithm utilizes schema-based filtering as well as "relatedness" filtering for complexity reduction. Here relatedness refers to the likelihood that two entities co-participate in a common instance, and the relatedness metric is learned from the RAE model. We show experimentally that SIR significantly reduces computation complexity without sacrificing reconstruction performance. The complexity reduction corresponds to reducing the KB size by 100 to 1000 folds.

Recommended citation

Richong Zhang, Junpeng Li, Jiajie Mei, and Yongyi Mao. 2018. Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding. In Proceedings of the 2018 World Wide Web Conference (WWW ‘18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1185-1194. DOI: https://doi.org/10.1145/3178876.3186017