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We present the causal relation prediction benchmarks based on wiki data. If you use our benchmark for your work, please cite our workshop paper that presents the preliminary results. A. Khatiwada, S. Shirai, K. Srinivas and O. Hassanzadeh, "Knowledge Graph Embeddings for Causal Relation Prediction", in Deep Learning for Knowledge Graphs Workshop (DL4KG@ISWC), 2022. Bibtex: @inproceedings{khatiwada2022knowledge, title={Knowledge graph embeddings for causal relation prediction}, author={Khatiwada, Aamod and Shirai, Sola and Srinivas, Kavitha and Hassanzadeh, Oktie}, booktitle={Workshop on Deep Learning for Knowledge Graphs (DL4KG)}, year={2022} }
Causal Relation Prediction, Knowledge Graphs, Link Prediction
Causal Relation Prediction, Knowledge Graphs, Link Prediction
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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