@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",
    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.",
}