Using Model Calibration to Evaluate Link Prediction in Knowledge Graphs
Author:
Affiliation:
1. Rochester Institute of Technology, Rochester, NY, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645506
Reference50 articles.
1. Ralph Abboud .Ismail. Ilkan Ceylan Thomas Lukasiewicz and Tommaso Salvatori. 2020. BoxE: A Box Embedding Model for Knowledge Base Completion. In NeurIPS. 9649--9661.
2. Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang, Wei Hu, and Chengkai Li. 2020. Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study. In SIGMOD. 1995--2010.
3. Mehdi Ali Max Berrendorf Mikhail Galkin Veronika Thost Tengfei Ma Volker Tresp and Jens Lehmann. 2022. Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract). In IJCAI. 5259--5263.
4. Iti Bansal, Sudhanshu Tiwari, and Carlos R. Rivero. 2020. The Impact of Negative Triple Generation Strategies and Anomalies on Knowledge Graph Completion. In CIKM. 45--54.
5. Anson Bastos Kuldeep Singh Abhishek Nadgeri Johannes Hoffart Manish Singh and Toyotaro Suzumura. 2023. Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?. In TheWebConf. 2455--2466.
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