@inproceedings{ma-etal-2022-mmekg,
title = "{MMEKG}: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities",
author = "Ma, Yubo and
Wang, Zehao and
Li, Mukai and
Cao, Yixin and
Chen, Meiqi and
Li, Xinze and
Sun, Wenqi and
Deng, Kunquan and
Wang, Kun and
Sun, Aixin and
Shao, Jing",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.23",
doi = "10.18653/v1/2022.acl-demo.23",
pages = "231--239",
abstract = "Events are fundamental building blocks of real-world happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop an induction strategy to create million-scale concept events and a schema organizing all events and relations in MMEKG. To this end, we also provide a pipeline enabling our system to seamlessly parse texts/images to event graphs and to retrieve multi-modal knowledge at both concept- and instance-levels.",
}
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<abstract>Events are fundamental building blocks of real-world happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop an induction strategy to create million-scale concept events and a schema organizing all events and relations in MMEKG. To this end, we also provide a pipeline enabling our system to seamlessly parse texts/images to event graphs and to retrieve multi-modal knowledge at both concept- and instance-levels.</abstract>
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%0 Conference Proceedings
%T MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
%A Ma, Yubo
%A Wang, Zehao
%A Li, Mukai
%A Cao, Yixin
%A Chen, Meiqi
%A Li, Xinze
%A Sun, Wenqi
%A Deng, Kunquan
%A Wang, Kun
%A Sun, Aixin
%A Shao, Jing
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ma-etal-2022-mmekg
%X Events are fundamental building blocks of real-world happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop an induction strategy to create million-scale concept events and a schema organizing all events and relations in MMEKG. To this end, we also provide a pipeline enabling our system to seamlessly parse texts/images to event graphs and to retrieve multi-modal knowledge at both concept- and instance-levels.
%R 10.18653/v1/2022.acl-demo.23
%U https://aclanthology.org/2022.acl-demo.23
%U https://doi.org/10.18653/v1/2022.acl-demo.23
%P 231-239
Markdown (Informal)
[MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities](https://aclanthology.org/2022.acl-demo.23) (Ma et al., ACL 2022)
ACL
- Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, and Jing Shao. 2022. MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 231–239, Dublin, Ireland. Association for Computational Linguistics.