{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:20:35Z","timestamp":1775197235915,"version":"3.50.1"},"reference-count":76,"publisher":"China Science Publishing & Media Ltd.","issue":"3","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":216,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,9,8]]},"abstract":"<jats:p>With the technological development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, the research on knowledge graph has been carried out in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph (FR2KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference (CCKS2020). FR2KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.<\/jats:p>","DOI":"10.1162\/dint_a_00108","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T16:31:39Z","timestamp":1628181099000},"page":"418-443","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":21,"title":["Data Set and Evaluation of Automated Construction of Financial\n                    Knowledge Graph"],"prefix":"10.3724","volume":"3","author":[{"given":"Wenguang","family":"Wang","sequence":"first","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]},{"given":"Yonglin","family":"Xu","sequence":"additional","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]},{"given":"Chunhui","family":"Du","sequence":"additional","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]},{"given":"Yunwen","family":"Chen","sequence":"additional","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]},{"given":"Yijie","family":"Wang","sequence":"additional","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]},{"given":"Hui","family":"Wen","sequence":"additional","affiliation":[{"name":"DataGrand Inc., Shanghai 201203, China"}]}],"member":"2026","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"2021102914234667000_ref1","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image\n                        database","volume-title":"IEEE Conference on Computer Vision and\n                        Pattern Recognition","author":"Jia","year":"2009"},{"key":"2021102914234667000_ref2","first-page":"1","article-title":"Overview of TAC-KBP2016 tri-lingual EDL and its impact on\n                        end-to-end KBP","volume-title":"Proceedings of Text Analysis\n                        Conference","author":"Ji","year":"2016"},{"key":"2021102914234667000_ref3","doi-asserted-by":"crossref","first-page":"967","DOI":"10.18653\/v1\/2020.coling-main.84","article-title":"A high precision pipeline for financial knowledge graph\n                        construction","volume-title":"Proceedings of the 28th\n                        International Conference on Computational Linguistics","author":"Elhammadi","year":"2020"},{"key":"2021102914234667000_ref4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.18653\/v1\/D19-5102","article-title":"Financial event extraction using Wikipedia-based weak\n                        supervision","volume-title":"Proceedings of the Second Workshop\n                        on Economics and Natural Language Processing","author":"Ein-Dor","year":"2019"},{"key":"2021102914234667000_ref5","volume-title":"TAC KBP 2016 Cold Start Track"},{"key":"2021102914234667000_ref6","first-page":"4171","article-title":"BERT: Pre-training of deep 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                      Meeting of the Association for Computational Linguistics","author":"Ringland","year":"2019"},{"key":"2021102914234667000_ref9","first-page":"809","article-title":"FEVER: A large-scale data set for fact extraction and\n                        VERification","volume-title":"Proceedings of the Annual\n                        Conference of the North American Chapter of the Association for\n                        Computational Linguistics: Human Language Technologies (NAACL-HLT)","author":"Thorne","year":"2018"},{"key":"2021102914234667000_ref10","doi-asserted-by":"crossref","first-page":"764","DOI":"10.18653\/v1\/P19-1074","article-title":"DocRED: A large-scale document-level relation extraction data\n                        set","volume-title":"Proceedings of the 57th Annual Meeting of\n                        the Association for Computational 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LSTM-CRF for named entity\n                        recognition","volume-title":"Proceedings of the 2019 Conference\n                        on Empirical Methods in Natural Language Processing and the 9th\n                        International Joint Conference on Natural Language Processing","author":"Jie","year":"2019"},{"key":"2021102914234667000_ref32","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.18653\/v1\/P19-1233","article-title":"GCDT: A global context enhanced deep transition architecture\n                        for sequence labeling","volume-title":"Proceedings of the 57th\n                        Annual Meeting of the Association for Computational Linguistics","author":"Liu","year":"2019"},{"key":"2021102914234667000_ref33","first-page":"8441","article-title":"Hierarchical contextualized representation for named entity\n                        recognition","volume-title":"Proceedings of the AAAI Conference\n                        on Artificial Intelligence","author":"Luo","year":"2019"},{"key":"2021102914234667000_ref34","first-page":"1462","article-title":"Natural language processing (Almost) from\n                        scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"Journal of Machine Learning\n                        Research"},{"key":"2021102914234667000_ref35","first-page":"2670","article-title":"Fast and accurate entity recognition with Iterated Dilated\n                        Convolutions","volume-title":"Proceedings of the 2017 Conference\n                        on Empirical Methods in Natural Language Processing","author":"Strubell","year":"2017"},{"key":"2021102914234667000_ref36","first-page":"260","article-title":"Neural architectures for named entity\n                        recognition","volume-title":"Proceedings of the Annual\n                        Conference of the North American Chapter of the Association for\n                        Computational Linguistics: Human Language 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chunking","volume-title":"Proceedings of the AAAI Conference on Artificial\n                        Intelligence","author":"Zhai","year":"2017"},{"key":"2021102914234667000_ref40","doi-asserted-by":"crossref","first-page":"252","DOI":"10.18653\/v1\/W17-2630","article-title":"Deep active learning for named entity\n                        recognition","volume-title":"Proceedings of the 2nd Workshop on\n                        Representation Learning for NLP","author":"Shen","year":"2017"},{"key":"2021102914234667000_ref41","doi-asserted-by":"crossref","first-page":"465","DOI":"10.18653\/v1\/2020.acl-main.45","article-title":"Dice loss for data-imbalanced NLP tasks","volume-title":"Proceedings of the 58th Annual Meeting of the Association for\n                        Computational Linguistics","author":"Li","year":"2020"},{"key":"2021102914234667000_ref42","first-page":"359","article-title":"Recognizing named entities in tweets","volume-title":"Proceedings of the 49th Annual Meeting of the Association for\n                        Computational Linguistics: Human Language Technologies","author":"Liu","year":"2011"},{"issue":"1","key":"2021102914234667000_ref43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.artint.2005.03.001","article-title":"Unsupervised named-entity extraction from the Web: An\n                        experimental study","volume":"165","author":"Etzioni","year":"2005","journal-title":"Artificial Intelligence"},{"issue":"6","key":"2021102914234667000_ref44","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1016\/j.jbi.2013.08.004","article-title":"Unsupervised biomedical named entity recognition: Experiments\n                        with clinical and biological texts","volume":"46","author":"Zhang","year":"2013","journal-title":"Journal of\n                        Biomedical Informatics"},{"key":"2021102914234667000_ref45","first-page":"266","article-title":"Unsupervised named-entity recognition: Generating gazetteers\n                        and resolving ambiguity","volume-title":"Conference of the\n                        Canadian Society for Computational Studies of Intelligence","author":"Nadeau","year":"2006"},{"key":"2021102914234667000_ref46","first-page":"344","article-title":"Bootstrapped text-level named entity recognition for\n                        literature","volume-title":"Proceedings of the 54th Annual\n                        Meeting of the Association for Computational Linguistics","author":"Brooke","year":"2016"},{"key":"2021102914234667000_ref47","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.18653\/v1\/P19-1236","article-title":"Cross-domain NER using cross-domain language\n                        modeling","volume-title":"Proceedings of the 57th Annual Meeting\n                        of the Association for Computational Linguistics","author":"Jia","year":"2019"},{"key":"2021102914234667000_ref48","first-page":"100","article-title":"Unsupervised models for named 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classification via Convolutional Deep Neural\n                        Network","volume-title":"Proceedings of the 25th International\n                        Conference on Computational Linguistics","author":"Zeng","year":"2014"},{"key":"2021102914234667000_ref52","first-page":"626","article-title":"Classifying relations by ranking with Convolutional Neural\n                        Networks","volume-title":"Proceedings of the 53rd Annual Meeting\n                        of the Association for Computational Linguistics and the 7th International\n                        Joint Conference on Natural Language Processing of the Asian Federation of\n                        Natural Language Processing","author":"Santos","year":"2015"},{"key":"2021102914234667000_ref53","doi-asserted-by":"crossref","first-page":"536","DOI":"10.18653\/v1\/D15-1062","article-title":"Semantic relation classification via Convolutional Neural\n                        Networks with simple negative 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Processing","author":"Zhang","year":"2018"},{"key":"2021102914234667000_ref56","first-page":"241","article-title":"Attention guided graph convolutional networks for relation\n                        extraction","volume-title":"Proceedings of the 57th Annual\n                        Meeting of the Association for Computational Linguistics","author":"Guo","year":"2020"},{"key":"2021102914234667000_ref57","first-page":"1003","article-title":"Distant supervision for relation extraction without labeled\n                        data","volume-title":"Proceedings of the 47th Annual Meeting of\n                        the ACL and the 4th IJCNLP of the AFNLP","author":"Mintz","year":"2009"},{"key":"2021102914234667000_ref58","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.18653\/v1\/D15-1203","article-title":"Distant supervision for relation extraction via piecewise\n                        convolutional neural networks","volume-title":"Proceedings of\n                        the 2015 Conference on Empirical Methods in Natural Language\n                        Processing","author":"Zeng","year":"2015"},{"key":"2021102914234667000_ref59","first-page":"1471","article-title":"Relation extraction with multi-instance multi-label\n                        convolutional neural networks","volume-title":"Proceedings of\n                        the 26th International Conference on Computational Linguistics","author":"Jiang","year":"2016"},{"key":"2021102914234667000_ref60","first-page":"3060","article-title":"Distant supervision for relation extraction with\n                        sentence-level attention and entity descriptions","volume-title":"Proceedings of the 31st AAAI Conference on Artificial\n                        Intelligence","author":"Ji","year":"2017"},{"key":"2021102914234667000_ref61","first-page":"2124","article-title":"Neural relation extraction with selective attention over\n                        instances","volume-title":"Proceedings of the 54th Annual\n                        Meeting of the Association for Computational Linguistics","author":"Lin","year":"2016"},{"key":"2021102914234667000_ref62","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational\n                        data","volume-title":"Advances in Neural Information Processing\n                        Systems","author":"Bordes","year":"2013"},{"key":"2021102914234667000_ref63","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.18653\/v1\/D18-1245","article-title":"Multi-level structured self-attentions for distantly\n                        supervised relation extraction","volume-title":"Proceedings of\n                        the 2018 Conference on Empirical Methods in Natural Language\n                        Processing","author":"Du","year":"2018"},{"key":"2021102914234667000_ref64","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.18653\/v1\/D18-1157","article-title":"RESIDE: Improving distantly-supervised neural 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  the Joint Conference of the 47th Annual Meeting of the ACL and the 4th\n                        International Joint Conference on Natural Language Processing of the\n                        AFNLP","author":"Yan","year":"2009"},{"key":"2021102914234667000_ref70","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1145\/1526709.1526797","article-title":"Measuring the similarity between implicit semantic relations\n                        from the Web","volume-title":"Proceedings of the 18th\n                        International Conference on World Wide Web","author":"Bollegala","year":"2009"},{"key":"2021102914234667000_ref71","first-page":"1105","article-title":"End-to-end relation extraction using LSTMs on sequences and\n                        tree structures","volume-title":"Proceedings of the 54th Annual\n                        Meeting of the Association for Computational Linguistics","author":"Miwa","year":"2016"},{"key":"2021102914234667000_ref72","first-page":"506","article-title":"Extracting relational facts by an end-to-end neural model\n                        with copy mechanism","volume-title":"Proceedings of the 56th\n                        Annual Meeting of the Association for Computational Linguistics","author":"Zeng","year":"2018"},{"key":"2021102914234667000_ref73","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.18653\/v1\/P19-1136","article-title":"GraphRel: Modeling text as relational graphs for joint\n                        entity and relation extraction","volume-title":"Proceedings of\n                        the 57th Annual Meeting of the Association for Computational\n                        Linguistics","author":"Fu","year":"2019"},{"key":"2021102914234667000_ref74","first-page":"2256","article-title":"Extracting entities and relations with joint minimum risk\n                        training","volume-title":"Proceedings of the Conference on\n                        Empirical Methods in Natural Language Processing","author":"Sun","year":"2018"},{"key":"2021102914234667000_ref75","first-page":"7072","article-title":"A hierarchical framework for relation extraction with\n                        reinforcement learning","volume-title":"Proceedings of the AAAI\n                        Conference on Artificial Intelligence","author":"Takanobu","year":"2019"},{"key":"2021102914234667000_ref76","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.18653\/v1\/P19-1129","article-title":"Entity-relation extraction as multi-turn question\n                        answering","volume-title":"Proceedings of the 57th Annual\n                        Meeting of the Association for Computational Linguistics","author":"Li","year":"2019"}],"container-title":["Data 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