KnowledgeNet: A Benchmark Dataset for Knowledge Base Population

Filipe Mesquita, Matteo Cannaviccio, Jordan Schmidek, Paramita Mirza, Denilson Barbosa

Abstract
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% (0.28). However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging. The KnowledgeNet dataset and baselines are available at https://github.com/diffbot/knowledge-net
Anthology ID:
D19-1069
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
749–758
Language:
URL:
https://aclanthology.org/D19-1069
DOI:
10.18653/v1/D19-1069
Bibkey:
Cite (ACL):
Filipe Mesquita, Matteo Cannaviccio, Jordan Schmidek, Paramita Mirza, and Denilson Barbosa. 2019. KnowledgeNet: A Benchmark Dataset for Knowledge Base Population. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 749–758, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population (Mesquita et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/D19-1069.pdf
Code
 diffbot/knowledge-net
Data
KnowledgeNet
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