{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T13:30:40Z","timestamp":1782912640807,"version":"3.54.5"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030260712","type":"print"},{"value":"9783030260729","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-26072-9_28","type":"book-chapter","created":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T23:05:48Z","timestamp":1564009548000},"page":"382-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Leveraging Lexical Semantic Information for Learning Concept-Based Multiple Embedding Representations for Knowledge Graph Completion"],"prefix":"10.1007","author":[{"given":"Yashen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanhuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,18]]},"reference":[{"issue":"3","key":"28_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.websem.2009.07.002","volume":"7","author":"C Bizer","year":"2009","unstructured":"Bizer, C., et al.: DBpedia - a crystallization point for the Web of Data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154\u2013165 (2009)","journal-title":"Web Semant. Sci. Serv. Agents World Wide Web"},{"key":"28_CR2","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: SIGMOD Conference, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"28_CR3","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787\u20132795 (2013)"},{"key":"28_CR4","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 (2011)","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-662-44848-9_11","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Bordes","year":"2014","unstructured":"Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165\u2013180. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44848-9_11"},{"issue":"1","key":"28_CR6","doi-asserted-by":"publisher","first-page":"34","DOI":"10.2307\/1401634","volume":"35","author":"J Cornfield","year":"1967","unstructured":"Cornfield, J.: Bayes theorem. Rev. Linstitut Int. Stat. 35(1), 34\u201349 (1967)","journal-title":"Rev. Linstitut Int. Stat."},{"key":"28_CR7","unstructured":"Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic, 28\u201330 June 2007, pp. 708\u2013716 (2007)"},{"key":"28_CR8","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":"28_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-04930-9_14","volume-title":"The Semantic Web - ISWC 2009","author":"T Franz","year":"2009","unstructured":"Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: ranking semantic web data by tensor decomposition. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213\u2013228. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04930-9_14"},{"key":"28_CR10","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7432.001.0001","volume-title":"Introduction to Statistical Relational Learning","author":"L Getoor","year":"2007","unstructured":"Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)"},{"key":"28_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-3-642-04409-0_32","volume-title":"Web Information Systems Engineering - WISE 2009","author":"H Huang","year":"2009","unstructured":"Huang, H., Liu, C.: Query evaluation on probabilistic RDF databases. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 307\u2013320. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04409-0_32"},{"issue":"7","key":"28_CR12","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1109\/TKDE.2017.2787709","volume":"30","author":"H Huang","year":"2018","unstructured":"Huang, H., Wang, Y., Feng, C., Liu, Z., Zhou, Q.: Leveraging conceptualization for short-text embedding. IEEE Trans. Knowl. Data Eng. 30(7), 1282\u20131295 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"28_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/978-3-319-25007-6_37","volume-title":"The Semantic Web - ISWC 2015","author":"D Krompa\u00df","year":"2015","unstructured":"Krompa\u00df, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 640\u2013655. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25007-6_37"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Luan, H.B., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: EMNLP (2015)","DOI":"10.18653\/v1\/D15-1082"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Zhu, X., Zhu, X., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Long, T., Lowe, R., Cheung, J.C.K., Precup, D.: Leveraging lexical resources for learning entity embeddings in multi-relational data. CoRR abs\/1605.05416 (2016)","DOI":"10.18653\/v1\/P16-2019"},{"key":"28_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-319-71249-9_43","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Ma","year":"2017","unstructured":"Ma, S., Ding, J., Jia, W., Wang, K., Guo, M.: TransT: type-based multiple embedding representations for knowledge graph completion. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 717\u2013733. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_43"},{"key":"28_CR18","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111\u20133119 (2013)"},{"issue":"1\u20132","key":"28_CR19","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s10107-015-0864-7","volume":"155","author":"D Needell","year":"2016","unstructured":"Needell, D., Srebro, N., Ward, R.: Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. Math. Program. 155(1\u20132), 549\u2013573 (2016)","journal-title":"Math. Program."},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Sirts, K., Qu, L., Johnson, M.: STransE: a novel embedding model of entities and relationships in knowledge bases. In: HLT-NAACL (2016)","DOI":"10.18653\/v1\/N16-1054"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1955\u20131961 (2016)","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"28_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/978-3-319-11915-1_8","volume-title":"The Semantic Web \u2013 ISWC 2014","author":"D Krompa\u00df","year":"2014","unstructured":"Krompa\u00df, D., Nickel, M., Tresp, V.: Querying factorized probabilistic triple databases. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 114\u2013129. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11915-1_8"},{"key":"28_CR23","unstructured":"Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: International Conference on International Conference on Machine Learning, pp. 809\u2013816 (2011)"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Park, J.W., Hwang, S.W., Wang, H.: Fine-grained semantic conceptualization of FrameNet. In: AAAI, pp. 2638\u20132644 (2016)","DOI":"10.1609\/aaai.v30i1.10332"},{"issue":"2","key":"28_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00692ED1V01Y201601AIM032","volume":"10","author":"Luc De Raedt","year":"2016","unstructured":"Raedt, L.D., Kersting, K., Natarajan, S., Poole, D.: Statistical relational artificial intelligence: logic, probability, and computation, vol. 10, no. 2, pp. 1\u2013189 (2016)","journal-title":"Synthesis Lectures on Artificial Intelligence and Machine Learning"},{"issue":"1\u20132","key":"28_CR26","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10994-006-5833-1","volume":"62","author":"M Richardson","year":"2006","unstructured":"Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1\u20132), 107\u2013136 (2006)","journal-title":"Mach. Learn."},{"key":"28_CR27","unstructured":"Riedel, S., Yao, L., McCallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: HLT-NAACL (2013)"},{"key":"28_CR28","unstructured":"Schmidt, D.C.: Learning probabilistic relational models (2000)"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Shi, B., Weninger, T.: Fact checking in heterogeneous information networks. In: International Conference Companion on World Wide Web, pp. 101\u2013102 (2016)","DOI":"10.1145\/2872518.2889354"},{"key":"28_CR30","unstructured":"Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: International Conference on Neural Information Processing Systems, pp. 926\u2013934 (2013)"},{"key":"28_CR31","unstructured":"Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 2330\u20132336 (2011)"},{"key":"28_CR32","unstructured":"Song, Y., Wang, S., Wang, H.: Open domain short text conceptualization: a generative + descriptive modeling approach. In: Proceedings of the 24th International Conference on Artificial Intelligence (2015)"},{"issue":"476","key":"28_CR33","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1198\/016214506000000302","volume":"101","author":"YW Teh","year":"2006","unstructured":"Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. Am. Stat. Assoc. 101(476), 1566\u20131581 (2006)","journal-title":"Am. Stat. Assoc."},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Unger, C., Lehmann, J., Ngomo, A.C.N., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In: International Conference on World Wide Web, pp. 639\u2013648 (2012)","DOI":"10.1145\/2187836.2187923"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, H., Feng, C.: Query expansion based on a feedback concept model for microblog retrieval. In: International Conference on World Wide Web, pp. 559\u2013568 (2017)","DOI":"10.1145\/3038912.3052710"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, H., Feng, C., Zhou, Q., Gu, J., Gao, X.: CSE: conceptual sentence embeddings based on attention model. In: 54th Annual Meeting of the Association for Computational Linguistics, pp. 505\u2013515 (2016)","DOI":"10.18653\/v1\/P16-1048"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"28_CR38","unstructured":"Wang, Z., Zhao, K., Wang, H., Meng, X., Wen, J.R.: Query understanding through knowledge-based conceptualization. In: International Conference on Artificial Intelligence, pp. 3264\u20133270 (2015)"},{"key":"28_CR39","doi-asserted-by":"crossref","unstructured":"Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD Conference (2012)","DOI":"10.1145\/2213836.2213891"},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Xiao, H., Huang, M., Meng, L., Zhu, X.: SSP: semantic space projection for knowledge graph embedding with text descriptions. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10952"},{"key":"28_CR41","doi-asserted-by":"crossref","unstructured":"Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: Meeting of the Association for Computational Linguistics, pp. 2316\u20132325 (2016)","DOI":"10.18653\/v1\/P16-1219"},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Jia, J.J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"28_CR43","unstructured":"Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types. In: International Joint Conference on Artificial Intelligence, pp. 2965\u20132971 (2016)"},{"key":"28_CR44","unstructured":"Yi, T., Luu, A.T., Hui, S.C.: Non-parametric estimation of multiple embeddings for link prediction on dynamic knowledge graphs. In: Thirty First Conference on Artificial Intelligence (2017)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-26072-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:27:36Z","timestamp":1709810856000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-26072-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030260712","9783030260729"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-26072-9_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","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":"1 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cfm.uestc.edu.cn\/apwebwaim2019\/","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":"Research Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"180","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":"42","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":"17","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":"23% - 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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}