Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Zhiqing Sun
- sl:arxiv_num : 1911.03903
- sl:arxiv_published : 2019-11-10T11:19:08Z
- sl:arxiv_summary : 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@en
- sl:arxiv_title : A Re-evaluation of Knowledge Graph Completion Methods@en
- sl:arxiv_updated : 2020-07-08T19:32:34Z
- sl:bookmarkOf : https://arxiv.org/abs/1911.03903
- sl:creationDate : 2020-07-28
- sl:creationTime : 2020-07-28T11:27:26Z
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