AutoKGE: Searching Scoring Functions for Knowledge Graph Embedding

Scoring functions (SFs), which measure the plausibility of links between entities based on a given relation, have become the crux of knowledge graph embedding (KGE). Lots of SFs have been designed by humans in recent years. However, the improvements are getting marginal and none of them consistently achieve the best performance over various datasets. Inspired by the recent success of automated machine learning (AutoML), we propose the automated KGE (AutoKGE) in this paper to automatically design SFs for distinct KGs. We firstly identify a unified representation over popularly used SFs, which helps to set up a search space for AutoKGE. Then, we propose a greedy algorithm, which is enhanced by a filter and a predictor, to efficiently search in such a space. Extensive experiments on benchmark datasets demonstrate the effectiveness and efficiency of the proposed AutoKGE. The SFs, searched by our method, are KG dependent, new to the literature, and outperform the state-of-the-art SFs designed by humans.