Abstract
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA 1, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation2. The system is publicly available at GitHub3 and DockerHub4, with complete documentation5
Cite
CITATION STYLE
Li, M., Zareian, A., Lin, Y., Pan, X., Whitehead, S., Chen, B., … Freedman, M. (2020). GAIA: A fine-grained multimedia knowledge extraction system. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 77–86). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-demos.11
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