{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:21:51Z","timestamp":1743060111157,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030414061"},{"type":"electronic","value":"9783030414078"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41407-8_17","type":"book-chapter","created":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T15:03:02Z","timestamp":1581606182000},"page":"255-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Retrofitting Soft Rules for Knowledge Representation Learning"],"prefix":"10.1007","author":[{"given":"Bo","family":"An","sequence":"first","affiliation":[]},{"given":"Xianpei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Le","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,14]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase:a collaboratively created graph database for structuring human knowledge. In: ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, Bc, Canada, June, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601\u2013610 (2014)","DOI":"10.1145\/2623330.2623623"},{"key":"17_CR4","unstructured":"Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: International Conference on Intelligent Control and Information Processing, pp. 464\u2013469 (2013)"},{"key":"17_CR5","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787\u20132795 (2013)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"17_CR9","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071\u20132080 (2016)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Rockt\u00e4schel, T., Singh, S., Riedel, S.: Injecting logical background knowledge into embeddings for relation extraction. In: HLT-NAACL, pp. 1119\u20131129 (2015)","DOI":"10.3115\/v1\/N15-1118"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Jointly embedding knowledge graphs and logical rules. In: EMNLP, pp. 192\u2013202 (2016)","DOI":"10.18653\/v1\/D16-1019"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, Q., Wang, L., Wang, B., Guo, L.: Knowledge graph embedding with iterative guidance from soft rules. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11918"},{"issue":"6","key":"17_CR13","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","volume":"24","author":"L Gal\u00e1rraga","year":"2015","unstructured":"Gal\u00e1rraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707\u2013730 (2015)","journal-title":"VLDB J."},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Zhong, H., Zhang, J., Wang, Z., Wan, H., Chen, Z.: Aligning knowledge and text embeddings by entity descriptions. In: EMNLP, pp. 267\u2013272 (2015)","DOI":"10.18653\/v1\/D15-1031"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: AAAI, pp. 2659\u20132665 (2016)","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Xu, J., Chen, K., Qiu, X., Huang, X.: Knowledge graph representation with jointly structural and textual encoding. arXiv preprint arXiv:1611.08661 (2016)","DOI":"10.24963\/ijcai.2017\/183"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"An, B., Chen, B., Han, X., Sun, L.: Accurate text-enhanced knowledge graph representation learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 745\u2013755 (2018)","DOI":"10.18653\/v1\/N18-1068"},{"key":"17_CR18","unstructured":"Riedel, S., Yao, L., McCallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: HLT-NAACL. pp. 74\u201384 (2013)"},{"key":"17_CR19","first-page":"1499","volume":"15","author":"K Toutanova","year":"2015","unstructured":"Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. EMNLP 15, 1499\u20131509 (2015)","journal-title":"EMNLP"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Xiao, H., Huang, M., Zhu, X.: Transg: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2316\u20132325 (2016)","DOI":"10.18653\/v1\/P16-1219"},{"key":"17_CR21","unstructured":"Wang, Z., Li, J., Liu, Z., Tang, J.: Text-enhanced representation learning for knowledge graph. In: To appear in IJCAI 2016, pp. 04\u201317 (2016)"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379 (2015)","DOI":"10.18653\/v1\/D15-1082"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Toutanova, K., Lin, X.V., Yih, W.T., Poon, H., Quirk, C.: Compositional learning of embeddings for relation paths in knowledge bases and text. In: ACL2016, vol. 1, pp. 1434\u20131444 (2016)","DOI":"10.18653\/v1\/P16-1136"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Xiong, W., Hoang, T., Wang, W.Y.: Deeppath: a reinforcement learning method for knowledge graph reasoning. arXiv preprint arXiv:1707.0669 (2017)","DOI":"10.18653\/v1\/D17-1060"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"17_CR26","unstructured":"Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: International Conference on Artificial Intelligence, pp. 1859\u20131865 (2015)"},{"key":"17_CR27","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-981-10-3168-7_22","volume-title":"Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data","author":"S Guo","year":"2016","unstructured":"Guo, S., Ding, B., Wang, Q., Wang, L., Wang, B.: Knowledge base completion via rule-enhanced relational learning. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds.) CCKS 2016. CCIS, vol. 650, pp. 219\u2013227. Springer, Singapore (2016). https:\/\/doi.org\/10.1007\/978-981-10-3168-7_22"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Minervini, P.: Adversarial sets for regularising neural link predictors. In: Conference on Uncertainty in Artificial Intelligence (2017)","DOI":"10.18653\/v1\/K18-1007"},{"key":"17_CR29","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules. arXiv preprint arXiv:1603.06318 (2016)","DOI":"10.18653\/v1\/P16-1228"},{"key":"17_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-5300-3","volume-title":"Metamathematics of Fuzzy Logic","author":"P H\u00e1jek","year":"1998","unstructured":"H\u00e1jek, P.: Metamathematics of Fuzzy Logic, vol. 4. Springer Science & Business Media, Dordrecht (1998)"},{"key":"17_CR32","first-page":"2001","volume":"11","author":"K Ganchev","year":"2010","unstructured":"Ganchev, K., Gillenwater, J., Taskar, B., et al.: Posterior regularization for structured latent variable models. J.f Mach. Learn. Res. 11, 2001\u20132049 (2010)","journal-title":"J.f Mach. Learn. Res."},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August (2011)","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"17_CR34","unstructured":"Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. Eprint Arxiv (2014)"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., Poggio, T.A., et al.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955\u20131961 (2016)","DOI":"10.1609\/aaai.v30i1.10314"}],"container-title":["Lecture Notes in Computer Science","Semantic Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41407-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T13:16:18Z","timestamp":1665839778000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41407-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414061","9783030414078"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41407-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint International Semantic Technology Conference","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aswc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/jist2019.openkg.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","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":"9","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":"13","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":"13% - 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","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":"4","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)"}},{"value":"proceedings are published in both LNCS and CCIS volumes","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}