{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:13:38Z","timestamp":1743142418495,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930486"},{"type":"electronic","value":"9783030930493"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93049-3_32","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:01Z","timestamp":1641015001000},"page":"383-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Conditional Knowledge Graph Representation and\u00a0Construction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2246-9454","authenticated-orcid":false,"given":"Tingyue","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6297-5442","authenticated-orcid":false,"given":"Ziqiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0468-6495","authenticated-orcid":false,"given":"Yufan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5681-4351","authenticated-orcid":false,"given":"Yuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-1023","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-4771","authenticated-orcid":false,"given":"Xiaochun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"issue":"2","key":"32_CR1","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s00778-008-0125-y","volume":"18","author":"DJ Abadi","year":"2009","unstructured":"Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: SW-store: a vertically partitioned DBMS for semantic web data management. VLDB J. 18(2), 385\u2013406 (2009)","journal-title":"VLDB J."},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Angeli, G., Premkumar, M.J.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), pp. 344\u2013354. Association for Computational Linguistics, Beijing, China (2015)","DOI":"10.3115\/v1\/P15-1034"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Cui, L., Wei, F., Zhou, M.: Neural open information extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, (vol. 2: Short Papers), pp. 407\u2013413. Association for Computational Linguistics, Melbourne, Australia (2018)","DOI":"10.18653\/v1\/P18-2065"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Guo, Z., Nan, G., LU, W., Cohen, S.B.: Learning latent forests for medical relation extraction. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, International Joint Conferences on Artificial Intelligence Organization, Virtual, Japan, pp. 3651\u20133657 (2020)","DOI":"10.24963\/ijcai.2020\/505"},{"key":"32_CR5","unstructured":"Harris, S., Gibbins, N.: 3store: efficient bulk RDF storage. In: Proceedings of the 1st International Workshop on Practical and Scalable Semantic Systems, pp. 81\u201395, Sanibel Island, Florida, USA (2004)"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Hohenecker, P., Mtumbuka, F., Kocijan, V., Lukasiewicz, T.: Systematic comparison of neural architectures and training approaches for open information extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8554\u20138565. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.690"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Jiang, T., Zhao, T., Qin, B., Liu, T., Chawla, N., Jiang, M.: Multi-input multi-output sequence labeling for joint extraction of fact and condition tuples from scientific text. 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), pp. 302\u2013312. Association for Computational Linguistics, Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1029"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, T., Zhao, T., Qin, B., Liu, T., Chawla, N.V., Jiang, M.: The role of \u201ccondition\": a novel scientific knowledge graph representation and construction model. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1634\u20131642. Association for Computing Machinery (2019)","DOI":"10.1145\/3292500.3330942"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Kolluru, K., Adlakha, V., Aggarwal, S., Mausam, Chakrabarti, S.: OpenIE6: iterative grid labeling and coordination analysis for open information extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3748\u20133761. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.306"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Kolluru, K., Aggarwal, S., Rathore, V., Mausam, Chakrabarti, S.: IMoJIE: iterative memory-based joint open information extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5871\u20135886. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.acl-main.521"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Niklaus, C., Cetto, M., Freitas, A., Handschuh, S.: Transforming complex sentences into a semantic hierarchy. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3415\u20133427. Association for Computational Linguistics, Florence, Italy (2019)","DOI":"10.18653\/v1\/P19-1333"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Pan, Z., Heflin, J.: DLDB: extending relational databases to support semantic web queries. In: Proceedings of the the 1st International Workshop on Practical and Scalable Semantic Systems, pp. 109\u2013113, Sanibel Island, Florida, USA (2004)","DOI":"10.21236\/ADA451847"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Song, L., Zhang, Y., Gildea, D., Yu, M., Wang, Z., Su, J.: Leveraging dependency forest for neural medical relation extraction. 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), pp. 208\u2013218. Association for Computational Linguistics, Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1020"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Stanovsky, G., Michael, J., Zettlemoyer, L., Dagan, I.: Supervised open information extraction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 885\u2013895. Association for Computational Linguistics, New Orleans, Louisiana (2018)","DOI":"10.18653\/v1\/N18-1081"},{"issue":"15","key":"32_CR15","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1093\/bioinformatics\/btaa491","volume":"36","author":"C Sun","year":"2020","unstructured":"Sun, C., et al.: Chemical-protein interaction extraction via gaussian probability distribution and external biomedical knowledge. Bioinformatics 36(15), 4323\u20134330 (2020)","journal-title":"Bioinformatics"},{"issue":"4","key":"32_CR16","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s41019-019-00109-w","volume":"4","author":"P Tong","year":"2019","unstructured":"Tong, P., Zhang, Q., Yao, J.: Leveraging domain context for question answering over knowledge graph. Data Sci. Eng. 4(4), 323\u2013335 (2019). https:\/\/doi.org\/10.1007\/s41019-019-00109-w","journal-title":"Data Sci. Eng."},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s41019-020-00140-2","volume":"5","author":"J Wawrzinek","year":"2020","unstructured":"Wawrzinek, J., Pinto, J.M.G., Wiehr, O., Balke, W.T.: Exploiting latent semantic subspaces to derive associations for specific pharmaceutical semantics. Data Sci. Eng. 5, 333\u2013345 (2020)","journal-title":"Data Sci. Eng."},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Webber, J.: A programmatic introduction to Neo4j. In: Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity, pp. 217\u2013218. Association for Computing Machinery, New York, NY, USA (2012)","DOI":"10.1145\/2384716.2384777"},{"issue":"1","key":"32_CR19","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.14778\/1453856.1453965","volume":"1","author":"C Weiss","year":"2008","unstructured":"Weiss, C., Karras, P., Bernstein, A.: Hexastore: sextuple indexing for semantic web data management. Proc. VLDB Endowment 1(1), 1008\u20131019 (2008)","journal-title":"Proc. VLDB Endowment"},{"key":"32_CR20","unstructured":"Wilkinson, K.: Jena property table implementation. In: Proceedings of the 2nd International Workshop on Scalable Semantic Web Knowledge Base Systems, pp. 35\u201346, Athens, Georgia, USA (2006)"},{"issue":"1","key":"32_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1855-x","volume":"18","author":"W Zheng","year":"2017","unstructured":"Zheng, W., et al.: An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinform. 18(1), 1\u201311 (2017)","journal-title":"BMC Bioinform."},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, Z., Ning, S., Lang, C., Lin, Y., Du, L.: Knowledge-aware attention network for protein-protein interaction extraction. J. Biomed. Inform. 96, 103234 (2019)","DOI":"10.1016\/j.jbi.2019.103234"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Leveraging prior knowledge for protein-protein interaction extraction with memory network. Database 18 (2018)","DOI":"10.1093\/database\/bay071"},{"key":"32_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/s41019-016-0029-6","volume":"2","author":"L Zou","year":"2017","unstructured":"Zou, L., \u00d6zsu, M.T.: Graph-based RDF data management. Data Sci. Eng. 2, 56\u201370 (2017)","journal-title":"Data Sci. Eng."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93049-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:18Z","timestamp":1641015018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93049-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930486","9783030930493"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93049-3_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}