A Re-evaluation of Knowledge Graph Completion Methods

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

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction FB15k-237 KBAT (Corrected) MRR .157 # 57
Hits@10 .331 # 61
MR 270 # 24
Link Prediction FB15k-237 ConvKB (Corrected) MRR .309 # 46
Hits@10 .421 # 56
MR 309 # 25
Link Prediction FB15k-237 CapsE (Corrected) MRR .032 # 58
Hits@10 .057 # 62
MR 446 # 28

Methods


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