All event relational (common-sense) knowledge extracted by previous papers (also in Ph.D. dissertation). This knowledge includes subevent, temporal, and causal relations between events. The entire knowledge graph contains 126K event nodes, 174K happens-before edges, 239K parent-subevent edges and 49K cause-effect edges. You can find the entire knowledge graph in entire_event_knowledge_graph.txt, where -> indicates parent-child relation, => indicates temporal happen-before relation, and ==> indicates cause-effect relation. If you find it is useful, please cite my previous papers. Thanks!
- regular_event_pairs.txt: temporal before/after relational knowledge between events. Please refer to "A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously" for more details.
@inproceedings{yao-etal-2017-weakly, title = "A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously", author = "Yao, Wenlin and Nettyam, Saipravallika and Huang, Ruihong", booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017", month = sep, year = "2017", address = "Varna, Bulgaria", publisher = "INCOMA Ltd.", url = "https://doi.org/10.26615/978-954-452-049-6_103", doi = "10.26615/978-954-452-049-6_103", pages = "803--812", }
- event_knowledge_2.0.zip: Narratives and temporal before/after relational knowledge between events. Please refer to "Temporal Event Knowledge Acquisition via Identifying Narratives" for more details.
@inproceedings{yao-huang-2018-temporal, title = "Temporal Event Knowledge Acquisition via Identifying Narratives", author = "Yao, Wenlin and Huang, Ruihong", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P18-1050", doi = "10.18653/v1/P18-1050", pages = "537--547",
}
- subevent_pairs/ and temporal_causal_pairs/: subevent (parent-child), temporal (before/after), and causal relational knowledge between events. They are extracted using the weakly-supervised approach introduced by "Weakly Supervised Subevent Knowledge Acquisition".
@inproceedings{yao-etal-2020-weakly, title = "Weakly {S}upervised {S}ubevent {K}nowledge {A}cquisition", author = "Yao, Wenlin and Dai, Zeyu and Ramaswamy, Maitreyi and Min, Bonan and Huang, Ruihong", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.430", doi = "10.18653/v1/2020.emnlp-main.430", pages = "5345--5356", }