{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:27:12Z","timestamp":1768094832759,"version":"3.49.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872446"],"award-info":[{"award-number":["61872446"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902417"],"award-info":[{"award-number":["61902417"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2019JJ20024"],"award-info":[{"award-number":["2019JJ20024"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Knowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.<\/jats:p>","DOI":"10.1007\/s13042-020-01259-4","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T19:03:12Z","timestamp":1609786992000},"page":"1627-1637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Caps-OWKG: a capsule network model for open-world knowledge graph"],"prefix":"10.1007","volume":"12","author":[{"given":"Yuhan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-0219","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"1259_CR1","unstructured":"Bordes A, Usunier N, Garc\u00eda-Dur\u00e1n A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Burges CJC, Bottou L, Ghahramani Z, Weinberger KQ (eds) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5\u20138, 2013. Lake Tahoe, Nevada, United States, pp 2787\u20132795"},{"issue":"2","key":"1259_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10994-013-5363-6","volume":"94","author":"A Bordes","year":"2014","unstructured":"Bordes A, Glorot X, Weston J, Bengio Y (2014) A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation. Mach Learn 94(2):233\u2013259. https:\/\/doi.org\/10.1007\/s10994-013-5363-6","journal-title":"Mach Learn"},{"key":"1259_CR3","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.18653\/v1\/n19-1423","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019","author":"J Devlin","year":"2019","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 Conference of the North American Chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019. Association for Computational Linguistics, Stroudsburg, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/n19-1423"},{"key":"1259_CR4","first-page":"2181","volume-title":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence","author":"Y Lin","year":"2015","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Bonet B, Koenig S (eds) Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, Austin, pp 2181\u20132187"},{"key":"1259_CR5","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: Bengio Y, LeCun Y (eds) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2\u20134, 2013, Workshop Track Proceedings"},{"key":"1259_CR6","doi-asserted-by":"publisher","first-page":"327","DOI":"10.18653\/v1\/n18-2053","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT","author":"DQ Nguyen","year":"2018","unstructured":"Nguyen DQ, Nguyen TD, Nguyen DQ, Phung DQ (2018) A novel embedding model for knowledge base completion based on convolutional neural network. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT. Association for Computational Linguistics, Stroudsburg, pp 327\u2013333. https:\/\/doi.org\/10.18653\/v1\/n18-2053"},{"key":"1259_CR7","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.18653\/v1\/n19-1226","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the association for computational linguistics: human language Technologies, NAACL-HLT 2019","author":"DQ Nguyen","year":"2019","unstructured":"Nguyen DQ, Vu T, Nguyen TD, Nguyen DQ, Phung DQ (2019) A capsule network-based embedding model for knowledge graph completion and search personalization. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 Conference of the North American Chapter of the association for computational linguistics: human language Technologies, NAACL-HLT 2019. Association for Computational Linguistics, Stroudsburg, pp 2180\u20132189. https:\/\/doi.org\/10.18653\/v1\/n19-1226"},{"key":"1259_CR8","first-page":"809","volume-title":"Proceedings of the 28th International Conference on Machine Learning, ICML 2011","author":"M Nickel","year":"2011","unstructured":"Nickel M, Tresp V, Kriegel H (2011) A three-way model for collective learning on multi-relational data. In: Getoor L, Scheffer T (eds) Proceedings of the 28th International Conference on Machine Learning, ICML 2011. Omnipress, Bellevue, pp 809\u2013816"},{"key":"1259_CR9","first-page":"1955","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","author":"M Nickel","year":"2016","unstructured":"Nickel M, Rosasco L, Poggio TA (2016) Holographic embeddings of knowledge graphs. In: Schuurmans D, Wellman MP (eds) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, Phoenix, pp 1955\u20131961"},{"key":"1259_CR10","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.3115\/v1\/d14-1162","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL","author":"J Pennington","year":"2014","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Moschitti A, Pang B, Daelemans W (eds) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, Doha, pp 1532\u20131543. https:\/\/doi.org\/10.3115\/v1\/d14-1162"},{"key":"1259_CR11","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.18653\/v1\/n18-1202","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2018","author":"ME Peters","year":"2018","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 Conference of the North American Chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2018. Association for Computational Linguistics, Stroudsburg, pp 2227\u20132237. https:\/\/doi.org\/10.18653\/v1\/n18-1202"},{"key":"1259_CR12","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017. Long Beach, CA, USA, pp 3856\u20133866"},{"key":"1259_CR13","doi-asserted-by":"publisher","first-page":"3044","DOI":"10.1609\/aaai.v33i01.33","volume-title":"The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019","author":"H Shah","year":"2019","unstructured":"Shah H, Villmow J, Ulges A, Schwanecke U, Shafait F (2019) An open-world extension to knowledge graph completion models. The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI Press, London, pp 3044\u20133051. https:\/\/doi.org\/10.1609\/aaai.v33i01.33"},{"issue":"4","key":"1259_CR14","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1259_CR15","doi-asserted-by":"crossref","unstructured":"Shi B, Weininger T (2018) Open-world knowledge graph completion. In: The thirty-second AAAI conference on artificial intelligence (AAAI-18), pp 1957\u20131964","DOI":"10.1609\/aaai.v32i1.11535"},{"key":"1259_CR16","unstructured":"Socher R, Chen D, Manning CD, Ng AY (2013) Reasoning with neural tensor networks for knowledge base completion. In: Burges CJC, Bottou L, Ghahramani Z, Weinberger KQ (eds) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5\u20138, 2013. Lake Tahoe, Nevada, United States, pp 926\u2013934"},{"key":"1259_CR17","doi-asserted-by":"crossref","unstructured":"Sundermeyer M, Schl\u00fcter R, Ney H (2012) LSTM neural networks for language modeling. In: INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association, Portland, Oregon, USA, September 9-13, 2012, ISCA, pp 194\u2013197","DOI":"10.21437\/Interspeech.2012-65"},{"key":"1259_CR18","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G (2016) Complex embeddings for simple link prediction. In: Balcan M, Weinberger KQ (eds) Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, JMLR.org, JMLR Workshop and Conference Proceedings, vol 48, pp 2071\u20132080"},{"key":"1259_CR19","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017. Long Beach, CA, USA, pp 5998\u20136008"},{"key":"1259_CR20","first-page":"1112","volume-title":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence","author":"Z Wang","year":"2014","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Brodley CE, Stone P (eds) Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI Press, Qu\u00e9bec City, pp 1112\u20131119"},{"key":"1259_CR21","first-page":"2659","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","author":"R Xie","year":"2016","unstructured":"Xie R, Liu Z, Jia J, Luan H, Sun M (2016) Representation learning of knowledge graphs with entity descriptions. In: Schuurmans D, Wellman MP (eds) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, Phoenix, pp 2659\u20132665"},{"key":"1259_CR22","doi-asserted-by":"publisher","first-page":"250","DOI":"10.18653\/v1\/k16-1025","volume-title":"Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016","author":"I Yamada","year":"2016","unstructured":"Yamada I, Shindo H, Takeda H, Takefuji Y (2016) Joint learning of the embedding of words and entities for named entity disambiguation. In: Goldberg Y, Riezler S (eds) Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016. ACL, Berlin, pp 250\u2013259. https:\/\/doi.org\/10.18653\/v1\/k16-1025"},{"key":"1259_CR23","unstructured":"Yang B, Yih W, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01259-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01259-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01259-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T13:35:07Z","timestamp":1670679307000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01259-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1259"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01259-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"20 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}