{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T19:15:59Z","timestamp":1758395759745,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2019YFF0302601"],"award-info":[{"award-number":["2019YFF0302601"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2020B0101130013"],"award-info":[{"award-number":["2020B0101130013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04528-1","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T09:04:23Z","timestamp":1679389463000},"page":"19940-19961","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Geometry-based anisotropy representation learning of concepts for knowledge graph embedding"],"prefix":"10.1007","volume":"53","author":[{"given":"Jibin","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8874-5466","authenticated-orcid":false,"given":"Zheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongjun","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"4528_CR1","doi-asserted-by":"publisher","unstructured":"Zhang N, Deng S, Sun Z, et al (2019) Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In: proceedings of the 2019 conference of the north american chapter of the association for computational linguistics : human language technologies, vol 1 (Long and short papers). Association for computational linguistics. Minnesota, Minneapolis, pp 3016\u20133025, https:\/\/doi.org\/10.18653\/v1\/N19-1306","DOI":"10.18653\/v1\/N19-1306"},{"key":"4528_CR2","doi-asserted-by":"publisher","unstructured":"Chen Y, Wu L, Zaki MJ (2019) Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics : human language technologies, vol 1 (Long and short papers). Association for computational linguistics. Minnesota, Minneapolis, pp 2913\u20132923, https:\/\/doi.org\/10.18653\/v1\/N19-1299","DOI":"10.18653\/v1\/N19-1299"},{"key":"4528_CR3","doi-asserted-by":"publisher","unstructured":"He H, Balakrishnan A, Eric M, et al (2017) Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 1 : long papers). Association for computational linguistics, Vancouver, Canada, pp 1766-1776, https:\/\/doi.org\/10.18653\/v1\/P17-1162","DOI":"10.18653\/v1\/P17-1162"},{"key":"4528_CR4","unstructured":"Bordes A, Usunier N, Garcia-Duran A, et al (2013) Translating embeddings for modeling multi-relational data. In: Burges C J, Bottou L, Welling M (eds) Advances in neural information processing systems, vol 26. Curran Associates, Inc"},{"key":"4528_CR5","doi-asserted-by":"publisher","unstructured":"Lv X, Hou L, Li J, et al (2018) Differentiating concepts and instances for knowledge graph embedding. In: Proceedings of the 2018 conference on empirical methods in natural language processing. Association for computational linguistics, Brussels, Belgium, pp 1971-1979. https:\/\/doi.org\/10.18653\/v1\/D18-1222","DOI":"10.18653\/v1\/D18-1222"},{"issue":"2","key":"4528_CR6","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2021","unstructured":"Ji S, Pan S, Cambria E, et al (2021) A survey on knowledge graphs : representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494\u2013514. https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4528_CR7","doi-asserted-by":"crossref","unstructured":"Liu H, Wang Y, Wu F et al (2019) REKER: relation extraction with knowledge of entity and relation. In: Tang J, Kan MY, Zhao D (eds) Natural language processing and chinese computing, Springer International Publishing, Cham, pp 90-102","DOI":"10.1007\/978-3-030-32236-6_8"},{"key":"4528_CR8","doi-asserted-by":"publisher","unstructured":"Zhang H, Liu Z, Xiong C, et al (2020) Grounded conversation generation as guided traverses in commonsense knowledge graphs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, pp 2031-2043. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.184","DOI":"10.18653\/v1\/2020.acl-main.184"},{"issue":"4","key":"4528_CR9","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/ijl\/3.4.235","volume":"3","author":"GA Miller","year":"1990","unstructured":"Miller GA, Beckwith R, Fellbaum C, et al (1990) Introduction to WordNet : an On-line lexical database*. Int J Lexicogr 3(4):235\u2013244. https:\/\/doi.org\/10.1093\/ijl\/3.4.235","journal-title":"Int J Lexicogr"},{"key":"4528_CR10","doi-asserted-by":"crossref","unstructured":"Suchanek FM, Kasneci G, Weikum G (2007) Yago : a core of semantic knowledge. In: 16th international world wide web conference, Banff, AB, Canada, pp 697\u2013706","DOI":"10.1145\/1242572.1242667"},{"key":"4528_CR11","doi-asserted-by":"publisher","unstructured":"Hao J, Chen M, Yu W, et al (2019) Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. association for computing machinery, New York, NY, USA, KDD \u201919, pp 1709\u20131719 event-place : Anchorage, AK, USA. https:\/\/doi.org\/10.1145\/3292500.3330838","DOI":"10.1145\/3292500.3330838"},{"key":"4528_CR12","doi-asserted-by":"publisher","unstructured":"Zhou J, Wang P, Pan Z, et al (2020) JECI : a joint knowledge graph embedding model for concepts and instances. In: Wang X, Lisi FA, Xiao G (eds) Semantic Technology, vol 12032. Springer International Publishing, Cham, pp 82-98. https:\/\/doi.org\/10.1007\/978-3-030-41407-8_6","DOI":"10.1007\/978-3-030-41407-8_6"},{"key":"4528_CR13","doi-asserted-by":"publisher","unstructured":"Yu Y, Xu Z, Lv Y, et al (2019) TransFG : a fine-grained model for knowledge graph embedding. In: Ni W, Wang X, Song W (eds) Web Information Systems and Applications, Springer International Publishing, Cham, pp 455-466. https:\/\/doi.org\/10.1007\/978-3-030-30952-7_45","DOI":"10.1007\/978-3-030-30952-7_45"},{"key":"4528_CR14","unstructured":"Fan M, Zhou Q, Chang E, et al (2014) Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of the 28th pacific asia conference on language, information and computing. department of linguistics, Chulalongkorn University, Phuket,Thailand, pp 328-337"},{"key":"4528_CR15","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, et al (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, pp 1112-1119","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"4528_CR16","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, et al (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, pp 2181-2187","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"4528_CR17","doi-asserted-by":"publisher","unstructured":"Ji G, He S, Xu L, et al (2015) Knowledge graph embedding via dynamic mapping matrix. 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). Association for computational linguistics, Beijing, China, pp 687-696. https:\/\/doi.org\/10.3115\/v1\/P15-1067","DOI":"10.3115\/v1\/P15-1067"},{"key":"4528_CR18","doi-asserted-by":"publisher","unstructured":"Yang S, Tian J, Zhang H et al (2019) TransMS : knowledge graph embedding for complex relations by multidirectional semantics. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI-19. International joint conferences on artificial intelligence organization, pp 1935\u20131942 https:\/\/doi.org\/10.24963\/ijcai.2019\/268","DOI":"10.24963\/ijcai.2019\/268"},{"key":"4528_CR19","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neucom.2021.02.100","volume":"461","author":"M Nayyeri","year":"2021","unstructured":"Nayyeri M, Cil GM, Vahdati S, et al (2021) Trans4E : link prediction on scholarly knowledge graphs. Neurocomputing 461:530\u2013542. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.100","journal-title":"Neurocomputing"},{"key":"4528_CR20","doi-asserted-by":"publisher","unstructured":"Zhang Z, Cai J, Zhang Y et al (2020) Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 3065\u20133072. https:\/\/doi.org\/10.1609\/aaai.v34i03.5701","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"4528_CR21","unstructured":"Yang B, Yih WT, He X et al (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Bengio Y, LeCun Y (eds) 3rd international conference on learning representations"},{"key":"4528_CR22","doi-asserted-by":"crossref","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, pp 1955\u20131961","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"4528_CR23","unstructured":"Trouillon T, Welbl J, Riedel S et al (2016) Complex embeddings for simple link prediction. In: Proceedings of the 33rd international conference on international conference on machine learning - vol 48. JMLR.org, ICML\u201916, p 2071\u20132080"},{"key":"4528_CR24","doi-asserted-by":"publisher","unstructured":"Zhang W, Paudel B, Zhang W et al (2019) Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 96\u2013104. https:\/\/doi.org\/10.1145\/3289600.3291014","DOI":"10.1145\/3289600.3291014"},{"key":"4528_CR25","doi-asserted-by":"crossref","unstructured":"Shi B, Weninger T (2017) ProjE: embedding projection for knowledge graph completion. In: Singh S, Markovitch S (eds) Proceedings of the thirty-first AAAI conference on artificial intelligence. AAAI Press, pp 1236-1242","DOI":"10.1609\/aaai.v31i1.10677"},{"key":"4528_CR26","doi-asserted-by":"crossref","unstructured":"Dettmers T, Minervini P, Stenetorp P, et al (2018) Convolutional 2D knowledge graph embeddings. In: McIlraith SA, Weinberger KQ (eds) Proceedings of the thirty-second AAAI conference on artificial intelligence. AAAI Press, pp 1811-1818","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"4528_CR27","doi-asserted-by":"crossref","unstructured":"Vashishth S, Sanyal S, Nitin V et al (2020) InteractE : improving convolution-based knowledge graph embeddings by increasing feature interactions. In: The thirty-fourth AAAI conference on artificial intelligence. AAAI Press, pp 3009\u20133016","DOI":"10.1609\/aaai.v34i03.5694"},{"key":"4528_CR28","doi-asserted-by":"crossref","unstructured":"Bala\u017eevi\u0107 I, Allen C, Hospedales TM (2019) Hypernetwork knowledge graph embeddings. In: Tetko IV, K\u016frkov\u00e1 V, Karpov P (eds) Artificial neural networks and machine learning \u2013 ICANN, workshop and special sessions, Springer International Publishing, Cham, pp 553-565","DOI":"10.1007\/978-3-030-30493-5_52"},{"key":"4528_CR29","unstructured":"Vashishth S, Sanyal S, Nitin V, et al (2020) Composition-based multi-relational graph convolutional networks. In: International conference on learning representations. OpenReview.net"},{"key":"4528_CR30","unstructured":"Balazevic I, Allen C, Hospedales T, et al (2019) Multi-relational poincar\u00e9 graph embeddings. In: Wallach H, Larochelle H, Beygelzimer A (eds) Advances in Neural Information Processing Systems. vol 32. Curran Associates, Inc"},{"key":"4528_CR31","doi-asserted-by":"publisher","unstructured":"Pan Z, Wang P (2021) Hyperbolic hierarchy-aware knowledge graph embedding for link prediction. In: Findings of the association for computational linguistics: EMNLP. Association for Computational Linguistics, Punta Cana, Dominican Republic, pp 2941-2948. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.251","DOI":"10.18653\/v1\/2021.findings-emnlp.251"},{"key":"4528_CR32","doi-asserted-by":"publisher","unstructured":"Wang K, Liu Y, Lin D, et al (2021) Hyperbolic geometry is not necessary : lightweight euclidean-based models for low-dimensional knowledge graph embeddings. In: Findings of the association for computational linguistics : EMNLP. Association for Computational Linguistics, Punta Cana, Dominican Republic, pp 464-474. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.42","DOI":"10.18653\/v1\/2021.findings-emnlp.42"},{"key":"4528_CR33","doi-asserted-by":"publisher","unstructured":"Chen M, Tian Y, Chen X et al (2018) On2Vec : embedding-based relation prediction for ontology population. In: Proceedings of the 2018 SIAM international conference on data mining (SDM), pp 315\u2013323. https:\/\/doi.org\/10.1137\/1.9781611975321.36","DOI":"10.1137\/1.9781611975321.36"},{"key":"4528_CR34","unstructured":"Guti\u00e9rrez-Basulto V, Schockaert S (2018) From knowledge graph embedding to ontology embedding? an analysis of the compatibility between vector space representations and rules. In: Thielscher M, Toni F, Wolter F (eds) Principles of knowledge representation and reasoning: proceedings of the sixteenth international conference. AAAI Press, pp 379-388"},{"key":"4528_CR35","doi-asserted-by":"publisher","unstructured":"Diaz GI, Fokoue A, Sadoghi M, et al (2018) EmbedS : scalable, ontology-aware graph embeddings. In: B\u00f6hlen MH, Pichler R, May N (eds) Proceedings of the 21st international conference on extending database technology. OpenProceedings.org, pp 433-436. https:\/\/doi.org\/10.5441\/002\/edbt.2018.40","DOI":"10.5441\/002\/edbt.2018.40"},{"key":"4528_CR36","doi-asserted-by":"publisher","unstructured":"Gao H, Zheng X, Li W, et al (2019) Cosine-based embedding for completing schematic knowledge. In: Tang J, Kan MY, Zhao D (eds) Natural language processing and chinese computing, Springer International Publishing, Cham, pp 249-261. https:\/\/doi.org\/10.1007\/978-3-030-32233-5_20","DOI":"10.1007\/978-3-030-32233-5_20"},{"key":"4528_CR37","doi-asserted-by":"publisher","unstructured":"Qiu J, Wang S, et al (2020) Learning the concept embeddings of ontology. In: Yang X, Wang CD, Islam MS (eds) Advanced data mining and applications, Springer International Publishing, Cham, pp 127-134. https:\/\/doi.org\/10.1007\/978-3-030-65390-3_10","DOI":"10.1007\/978-3-030-65390-3_10"},{"key":"4528_CR38","doi-asserted-by":"publisher","unstructured":"Hu Z, Huang P, Deng Y, et al (2015) Entity hierarchy embedding. 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). Association for Computational Linguistics, Beijing, China, pp 1292-1300. https:\/\/doi.org\/10.3115\/v1\/P15-1125","DOI":"10.3115\/v1\/P15-1125"},{"issue":"4","key":"4528_CR39","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1109\/TKDE.2016.2638425","volume":"29","author":"S Guo","year":"2017","unstructured":"Guo S, Wang Q, Wang B et al (2017) SSE : semantically smooth embedding for knowledge graphs. IEEE Trans Knowl Data Eng 29(4):884\u2013897. https:\/\/doi.org\/10.1109\/TKDE.2016.2638425, publisher : IEEE Computer Society","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4528_CR40","doi-asserted-by":"publisher","unstructured":"Guan N, Song D, Liao L (2019) Knowledge graph embedding with concepts. Knowl-Based Syst 164:38\u201344. https:\/\/doi.org\/10.1016\/j.knosys.2018.10.008, publisher: Elsevier BV","DOI":"10.1016\/j.knosys.2018.10.008"},{"key":"4528_CR41","doi-asserted-by":"publisher","unstructured":"Xiang Y, Zhang Z, Chen J, et al (2021) OntoEA : ontology-guided entity alignment via joint knowledge graph embedding. In: Findings of the association for computational linguistics : ACL-IJCNLP 2021. Association for Computational Linguistics, Online, pp 1117-1128. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.96","DOI":"10.18653\/v1\/2021.findings-acl.96"},{"key":"4528_CR42","doi-asserted-by":"publisher","unstructured":"Dong Y, Wang L, Xiang J et al (2022) Modeling IsA relations via box structure for knowledge graph embedding. In: Gama J, Li T, Yu Y (eds) Advances in knowledge discovery and data mining, lecture notes in computer science, vol 13281.Springer, pp 303-315. https:\/\/doi.org\/10.1007\/978-3-031-05936-0_24","DOI":"10.1007\/978-3-031-05936-0_24"},{"key":"4528_CR43","unstructured":"Socher R, Chen D, Manning CD et al (2013) Reasoning with neural tensor networks for knowledge base completion. In: Burges CJ, Bottou L, Welling M (eds) Advances in neural information processing systems, vol 26. Curran Associates, Inc"},{"key":"4528_CR44","unstructured":"Jenatton R, Roux N, Bordes A, et al (2012) A latent factor model for highly multi-relational data. In: Pereira F, Burges CJ, Bottou L (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc"},{"key":"4528_CR45","doi-asserted-by":"crossref","unstructured":"Bordes A, Weston J, Collobert R, et al (2011) Learning structured embeddings of knowledge bases. In: Burgard W, Roth D (eds) Proceedings of the twenty-fifth AAAI conference on artificial intelligence. AAAI Press","DOI":"10.1609\/aaai.v25i1.7917"},{"issue":"8","key":"4528_CR46","doi-asserted-by":"publisher","first-page":"9289","DOI":"10.1007\/s10489-021-02947-6","volume":"52","author":"W Li","year":"2022","unstructured":"Li W, Peng R, Li Z (2022) Improving knowledge graph completion via increasing embedding interactions. Appl Intell 52(8):9289\u20139307. https:\/\/doi.org\/10.1007\/s10489-021-02947-6","journal-title":"Appl Intell"},{"issue":"86","key":"4528_CR47","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten LVD, Hinton G (2008) Visualizing Data using t-SNE. J Mach Learn Res 9(86):2579\u20132605","journal-title":"J Mach Learn Res"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04528-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04528-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04528-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:14Z","timestamp":1694777534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04528-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":47,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4528"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04528-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,3,21]]},"assertion":[{"value":"10 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}