A Re-evaluation of Knowledge Graph Completion Methods

Abstract

Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report the performance of several existing methods using our protocol. The reproducible code has been made publicly available

arXiv commentAccepted at ACL 2020

References in corpus (5)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
• Analogical Inference for Multi-Relational Embeddings
• Modeling Relational Data with Graph Convolutional Networks
• Knowledge Base Completion: Baseline strikes back (Again)

Cited by in corpus (2)

• On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
• PPKE: Knowledge Representation Learning by Path-based Pre-training