{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:38Z","timestamp":1777890038623,"version":"3.51.4"},"reference-count":81,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SW"],"published-print":{"date-parts":[[2023,12,13]]},"abstract":"<jats:p>Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w.r.t. domain and range constraints. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@K. We also perform an extensive study to qualify the abilities of KGEMs as measured by our metric. Our experiments show that Sem@K provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offers different conclusions on the predictive power of models. Regarding Sem@K, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.<\/jats:p>","DOI":"10.3233\/sw-233508","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T11:42:57Z","timestamp":1702381377000},"page":"1273-1309","source":"Crossref","is-referenced-by-count":11,"title":["Sem@K: Is my knowledge graph embedding model semantic-aware?"],"prefix":"10.1177","volume":"14","author":[{"given":"Nicolas","family":"Hubert","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Lorraine, ERPI, France"},{"name":"Universit\u00e9 de Lorraine, CNRS, LORIA, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Monnin","sequence":"additional","affiliation":[{"name":"Universit\u00e9 C\u00f4te d\u2019Azur, Inria, CNRS, I3S, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armelle","family":"Brun","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, LORIA, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davy","family":"Monticolo","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, ERPI, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/SW-233508_ref1","doi-asserted-by":"publisher","first-page":"8825","DOI":"10.1109\/TPAMI.2021.3124805","article-title":"Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework","volume":"44","author":"Ali","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.3233\/SW-233508_ref2","doi-asserted-by":"crossref","unstructured":"S.\u00a0Auer, C.\u00a0Bizer, G.\u00a0Kobilarov, J.\u00a0Lehmann, R.\u00a0Cyganiak and Z.G.\u00a0Ives, DBpedia: A nucleus for a web of open data, in: The Semantic Web, 6th International Semantic Web Conf., 2nd Asian Semantic Web Conf., ISWC + ASWC, Lecture Notes in Computer Science, Vol.\u00a04825, Springer, 2007, pp.\u00a0722\u2013735.","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"10.3233\/SW-233508_ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103649"},{"key":"10.3233\/SW-233508_ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1522"},{"key":"10.3233\/SW-233508_ref6","doi-asserted-by":"crossref","unstructured":"K.D.\u00a0Bollacker, C.\u00a0Evans, P.K.\u00a0Paritosh, T.\u00a0Sturge and J.\u00a0Taylor, Freebase: A collaboratively created graph database for structuring human knowledge, in: Proc. of the ACM SIGMOD International Conf. on Management of Data, ACM, 2008, pp.\u00a01247\u20131250.","DOI":"10.1145\/1376616.1376746"},{"key":"10.3233\/SW-233508_ref7","unstructured":"A.\u00a0Bordes, N.\u00a0Usunier, A.\u00a0Garc\u00eda-Dur\u00e1n, J.\u00a0Weston and O.\u00a0Yakhnenko, Translating embeddings for modeling multi-relational data, in: Conf. on Neural Information Processing Systems (NeurIPS), 2013, pp.\u00a02787\u20132795."},{"key":"10.3233\/SW-233508_ref8","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Cao, Q.\u00a0Xu, Z.\u00a0Yang, X.\u00a0Cao and Q.\u00a0Huang, Dual quaternion knowledge graph embeddings, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, the Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2\u20139, 2021, AAAI Press, 2021, pp.\u00a06894\u20136902, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16850.","DOI":"10.1609\/aaai.v35i8.16850"},{"key":"10.3233\/SW-233508_ref9","unstructured":"G.\u00a0Chowdhury, M.\u00a0Srilakshmi, M.\u00a0Chain and S.\u00a0Sarkar, Neural factorization for offer recommendation using knowledge graph embeddings, in: Proc. of the SIGIR Workshop on eCommerce, Vol.\u00a02410, 2019."},{"key":"10.3233\/SW-233508_ref10","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Cui, P.\u00a0Kapanipathi, K.\u00a0Talamadupula, T.\u00a0Gao and Q.\u00a0Ji, Type-augmented relation prediction in knowledge graphs, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, the Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2\u20139, 2021, AAAI Press, 2021, pp.\u00a07151\u20137159, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16879.","DOI":"10.1609\/aaai.v35i8.16879"},{"key":"10.3233\/SW-233508_ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77385-4_26"},{"key":"10.3233\/SW-233508_ref12","unstructured":"R.\u00a0Das, S.\u00a0Dhuliawala, M.\u00a0Zaheer, L.\u00a0Vilnis, I.\u00a0Durugkar, A.\u00a0Krishnamurthy, A.\u00a0Smola and A.\u00a0McCallum, Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning, in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings, OpenReview.net, 2018, https:\/\/openreview.net\/forum?id=Syg-YfWCW."},{"key":"10.3233\/SW-233508_ref13","unstructured":"T.\u00a0Dettmers, P.\u00a0Minervini, P.\u00a0Stenetorp and S.\u00a0Riedel, Convolutional 2D knowledge graph embeddings, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2\u20137, 2018, AAAI Press, 2018, pp.\u00a01811\u20131818, https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/17366."},{"key":"10.3233\/SW-233508_ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1011"},{"key":"10.3233\/SW-233508_ref15","doi-asserted-by":"crossref","unstructured":"I.\u00a0Ferrari, G.\u00a0Frisoni, P.\u00a0Italiani, G.\u00a0Moro and C.\u00a0Sartori, Comprehensive analysis of knowledge graph embedding techniques benchmarked on link prediction, Electronics 11(23) (2022).","DOI":"10.3390\/electronics11233866"},{"issue":"6","key":"10.3233\/SW-233508_ref16","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","article-title":"Fast rule mining in ontological knowledge bases with AMIE+","volume":"24","author":"Gal\u00e1rraga","year":"2015","journal-title":"VLDB J."},{"key":"10.3233\/SW-233508_ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488425"},{"issue":"2","key":"10.3233\/SW-233508_ref18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1006\/knac.1993.1008","article-title":"A translation approach to portable ontology specifications","volume":"5","author":"Gruber","year":"1993","journal-title":"Knowl. Acquis."},{"key":"10.3233\/SW-233508_ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17105-5_5"},{"key":"10.3233\/SW-233508_ref21","unstructured":"N.\u00a0Hubert, P.\u00a0Monnin, A.\u00a0Brun and D.\u00a0Monticolo, Knowledge graph embeddings for link prediction: Beware of semantics! in: DL4KG@ISWC 2022: Workshop on Deep Learning for Knowledge Graphs, Held as Part of ISWC 2022: The 21st International Semantic Web Conference, Virtual, China, 2022."},{"key":"10.3233\/SW-233508_ref22","doi-asserted-by":"crossref","unstructured":"N.\u00a0Jain, J.\u00a0Kalo, W.\u00a0Balke and R.\u00a0Krestel, Do embeddings actually capture knowledge graph semantics? in: The Semantic Web \u2013 18th International Conf., ESWC, LNCS, Vol.\u00a012731, Springer, 2021, pp.\u00a0143\u2013159.","DOI":"10.1007\/978-3-030-77385-4_9"},{"key":"10.3233\/SW-233508_ref23","doi-asserted-by":"crossref","unstructured":"N.\u00a0Jain, T.\u00a0Tran, M.H.\u00a0Gad-Elrab and D.\u00a0Stepanova, Improving knowledge graph embeddings with ontological reasoning, in: The Semantic Web \u2013 International Semantic Web Conf. ISWC, Vol.\u00a012922, 2021, pp.\u00a0410\u2013426.","DOI":"10.1007\/978-3-030-88361-4_24"},{"key":"10.3233\/SW-233508_ref24","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/p15-1067"},{"issue":"2","key":"10.3233\/SW-233508_ref25","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge graphs: Representation, acquisition, and applications","volume":"33","author":"Ji","year":"2022","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.3233\/SW-233508_ref26","unstructured":"Y.\u00a0Jia, Y.\u00a0Wang, H.\u00a0Lin, X.\u00a0Jin and X.\u00a0Cheng, Locally adaptive translation for knowledge graph embedding, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, February 12\u201317, 2016, AAAI Press, 2016, pp.\u00a0992\u2013998, http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12018."},{"key":"10.3233\/SW-233508_ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1103"},{"key":"10.3233\/SW-233508_ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2609"},{"key":"10.3233\/SW-233508_ref29","unstructured":"S.M.\u00a0Kazemi and D.\u00a0Poole, SimplE embedding for link prediction in knowledge graphs, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montr\u00e9al, Canada, December 3\u20138, 2018, 2018, pp.\u00a04289\u20134300."},{"key":"10.3233\/SW-233508_ref30","doi-asserted-by":"crossref","unstructured":"D.\u00a0Krompa\u00df, S.\u00a0Baier and V.\u00a0Tresp, Type-constrained representation learning in knowledge graphs, in: The Semantic Web \u2013 14th International Semantic Web Conf. (ISWC), Vol.\u00a09366, Springer, 2015, pp.\u00a0640\u2013655.","DOI":"10.1007\/978-3-319-25007-6_37"},{"key":"10.3233\/SW-233508_ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49461-2_3"},{"key":"10.3233\/SW-233508_ref32","unstructured":"N.\u00a0Lao, T.M.\u00a0Mitchell and W.W.\u00a0Cohen, Random walk inference and learning in a large scale knowledge base, in: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27\u201331 July 2011, John McIntyre Conference Centre, Edinburgh, UK, a Meeting of SIGDAT, a Special Interest Group of the ACL, ACL, 2011, pp.\u00a0529\u2013539, https:\/\/aclanthology.org\/D11-1049\/."},{"issue":"4","key":"10.3233\/SW-233508_ref34","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TKDE.2003.1209005","article-title":"An approach for measuring semantic similarity between words using multiple information sources","volume":"15","author":"Li","year":"2003","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.3233\/SW-233508_ref35","unstructured":"Y.\u00a0Lin, Z.\u00a0Liu, M.\u00a0Sun, Y.\u00a0Liu and X.\u00a0Zhu, Learning entity and relation embeddings for knowledge graph completion, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA, January 25\u201330, 2015, AAAI Press, 2015, pp.\u00a02181\u20132187, http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI15\/paper\/view\/9571."},{"key":"10.3233\/SW-233508_ref36","unstructured":"H.\u00a0Liu, Y.\u00a0Wu and Y.\u00a0Yang, Analogical inference for multi-relational embeddings, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017, Proceedings of Machine Learning Research, Vol.\u00a070, PMLR, 2017, pp.\u00a02168\u20132178, http:\/\/proceedings.mlr.press\/v70\/liu17d.html."},{"key":"10.3233\/SW-233508_ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1222"},{"key":"10.3233\/SW-233508_ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/435"},{"issue":"11","key":"10.3233\/SW-233508_ref39","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"},{"issue":"3","key":"10.3233\/SW-233508_ref40","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3233\/SW-210452","article-title":"Discovering alignment relations with graph convolutional networks: A biomedical case study","volume":"13","author":"Monnin","year":"2022","journal-title":"Semantic Web"},{"key":"10.3233\/SW-233508_ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1466"},{"key":"10.3233\/SW-233508_ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n18-2053"},{"issue":"1","key":"10.3233\/SW-233508_ref43","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","article-title":"A review of relational machine learning for knowledge graphs","volume":"104","author":"Nickel","year":"2016","journal-title":"Proc. IEEE"},{"key":"10.3233\/SW-233508_ref44","unstructured":"M.\u00a0Nickel, L.\u00a0Rosasco and T.A.\u00a0Poggio, Holographic embeddings of knowledge graphs, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, February 12\u201317, 2016, AAAI Press, 2016, pp.\u00a01955\u20131961, http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12484."},{"key":"10.3233\/SW-233508_ref45","unstructured":"M.\u00a0Nickel, V.\u00a0Tresp and H.\u00a0Kriegel, A three-way model for collective learning on multi-relational data, in: Proc. of the 28th International Conf. on Machine Learning, ICML, 2011, pp.\u00a0809\u2013816."},{"key":"10.3233\/SW-233508_ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.105"},{"key":"10.3233\/SW-233508_ref47","doi-asserted-by":"publisher","DOI":"10.24432\/C5MK57"},{"issue":"3","key":"10.3233\/SW-233508_ref48","doi-asserted-by":"publisher","first-page":"489","DOI":"10.3233\/SW-160218","article-title":"Knowledge graph refinement: A survey of approaches and evaluation methods","volume":"8","author":"Paulheim","year":"2017","journal-title":"Semantic Web"},{"key":"10.3233\/SW-233508_ref49","unstructured":"H.\u00a0Paulheim, Make embeddings semantic again! in: Proc. of the ISWC Posters & Demonstrations, Industry and Blue Sky Ideas Tracks, CEUR Workshop Proceedings, Vol.\u00a02180, 2018."},{"key":"10.3233\/SW-233508_ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.187"},{"key":"10.3233\/SW-233508_ref51","unstructured":"J.\u00a0Portisch, M.\u00a0Hladik and H.\u00a0Paulheim, RDF2Vec light \u2013 a lightweight approachfor knowledge graph embeddings, in: Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice Co-Located with 19th International Semantic Web Conference (ISWC 2020), Globally online, November 1\u20136, 2020 (UTC), CEUR Workshop Proceedings, Vol.\u00a02721, CEUR-WS.org, 2020, pp.\u00a079\u201384, http:\/\/ceur-ws.org\/Vol-2721\/paper520.pdf."},{"issue":"1","key":"10.3233\/SW-233508_ref52","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/21.24528","article-title":"Development and application of a metric on semantic nets","volume":"19","author":"Rada","year":"1989","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.3233\/SW-233508_ref53","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1613\/jair.514","article-title":"Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language","volume":"11","author":"Resnik","year":"1999","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"10.3233\/SW-233508_ref54","doi-asserted-by":"crossref","first-page":"14:1","DOI":"10.1145\/3424672","article-title":"Knowledge graph embedding for link prediction: A comparative analysis","volume":"15","author":"Rossi","year":"2021","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.3233\/SW-233508_ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517887"},{"key":"10.3233\/SW-233508_ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.669"},{"key":"10.3233\/SW-233508_ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"10.3233\/SW-233508_ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"10.3233\/SW-233508_ref59","unstructured":"R.\u00a0Socher, D.\u00a0Chen, C.D.\u00a0Manning and A.Y.\u00a0Ng, Reasoning with neural tensor networks for knowledge base completion, in: 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, 2013, pp.\u00a0926\u2013934, https:\/\/proceedings.neurips.cc\/paper\/2013\/hash\/b337e84de8752b27eda3a12363109e80-Abstract.html."},{"key":"10.3233\/SW-233508_ref60","doi-asserted-by":"crossref","unstructured":"F.M.\u00a0Suchanek, G.\u00a0Kasneci and G.\u00a0Weikum, Yago: A core of semantic knowledge, in: Proc. of the 16th International Conf. on World Wide Web, WWW, ACM, 2007, pp.\u00a0697\u2013706.","DOI":"10.1145\/1242572.1242667"},{"key":"10.3233\/SW-233508_ref61","unstructured":"Z.\u00a0Sun, Z.\u00a0Deng, J.\u00a0Nie and J.\u00a0Tang, RotatE: Knowledge graph embedding by relational rotation in complex space, in: 7th International Conf. on Learning Representations, ICLR, 2019."},{"key":"10.3233\/SW-233508_ref62","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.489"},{"key":"10.3233\/SW-233508_ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49461-2_34"},{"key":"10.3233\/SW-233508_ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449856"},{"key":"10.3233\/SW-233508_ref65","doi-asserted-by":"crossref","unstructured":"K.\u00a0Toutanova and D.\u00a0Chen, Observed versus latent features for knowledge base and text inference, in: Proc. of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality, Association for Computational Linguistics, 2015, pp.\u00a057\u201366.","DOI":"10.18653\/v1\/W15-4007"},{"key":"10.3233\/SW-233508_ref66","unstructured":"T.\u00a0Trouillon, J.\u00a0Welbl, S.\u00a0Riedel, \u00c9.\u00a0Gaussier and G.\u00a0Bouchard, Complex embeddings for simple link prediction, in: Proc. of the 33rd International Conf. on Machine Learning, ICML, Vol.\u00a048, 2016, pp.\u00a02071\u20132080."},{"key":"10.3233\/SW-233508_ref67","doi-asserted-by":"crossref","unstructured":"S.\u00a0Vashishth, S.\u00a0Sanyal, V.\u00a0Nitin, N.\u00a0Agrawal and P.P.\u00a0Talukdar, InteractE: Improving convolution-based knowledge graph embeddings by increasing feature interactions, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, the Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7\u201312, 2020, AAAI Press, 2020, pp.\u00a03009\u20133016, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5694.","DOI":"10.1609\/aaai.v34i03.5694"},{"key":"10.3233\/SW-233508_ref68","unstructured":"S.\u00a0Vashishth, S.\u00a0Sanyal, V.\u00a0Nitin and P.P.\u00a0Talukdar, Composition-based multi-relational graph convolutional networks, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020, OpenReview.net, 2020, https:\/\/openreview.net\/forum?id=BylA_C4tPr."},{"key":"10.3233\/SW-233508_ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482224"},{"key":"10.3233\/SW-233508_ref70","doi-asserted-by":"publisher","DOI":"10.3390\/sym13030485"},{"key":"10.3233\/SW-233508_ref71","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10121407"},{"issue":"12","key":"10.3233\/SW-233508_ref72","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and applications","volume":"29","author":"Wang","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.3233\/SW-233508_ref73","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Wang, D.\u00a0Ruffinelli, R.\u00a0Gemulla, S.\u00a0Broscheit and C.\u00a0Meilicke, On evaluating embedding models for knowledge base completion, in: Proc. of the 4th Workshop on Representation Learning for NLP, RepL4NLP@ACL, 2019, pp.\u00a0104\u2013112.","DOI":"10.18653\/v1\/W19-4313"},{"key":"10.3233\/SW-233508_ref74","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Wang, J.\u00a0Zhang, J.\u00a0Feng and Z.\u00a0Chen, Knowledge graph embedding by translating on hyperplanes, in: Proc. of the Twenty-Eighth AAAI Conf. on Artificial Intelligence, 2014, pp.\u00a01112\u20131119.","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"10.3233\/SW-233508_ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482424"},{"key":"10.3233\/SW-233508_ref76","doi-asserted-by":"publisher","DOI":"10.3115\/981732.981751"},{"key":"10.3233\/SW-233508_ref77","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p16-1219"},{"key":"10.3233\/SW-233508_ref78","unstructured":"R.\u00a0Xie, Z.\u00a0Liu and M.\u00a0Sun, Representation learning of knowledge graphs with hierarchical types, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9\u201315 July 2016, IJCAI\/AAAI Press, 2016, pp.\u00a02965\u20132971, http:\/\/www.ijcai.org\/Abstract\/16\/421."},{"key":"10.3233\/SW-233508_ref79","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1060"},{"key":"10.3233\/SW-233508_ref80","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1026"},{"key":"10.3233\/SW-233508_ref81","unstructured":"B.\u00a0Yang, W.\u00a0Yih, X.\u00a0He, J.\u00a0Gao and L.\u00a0Deng, Embedding entities and relations for learning and inference in knowledge bases, in: 3rd International Conf. on Learning Representations, ICLR, 2015."},{"key":"10.3233\/SW-233508_ref82","unstructured":"S.\u00a0Zhang, Y.\u00a0Tay, L.\u00a0Yao and Q.\u00a0Liu, Quaternion knowledge graph embeddings, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada, December 8\u201314, 2019, 2019, pp.\u00a02731\u20132741, https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/d961e9f236177d65d21100592edb0769-Abstract.html."},{"key":"10.3233\/SW-233508_ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-3412-6_8"},{"key":"10.3233\/SW-233508_ref84","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Zhang, J.\u00a0Cai, Y.\u00a0Zhang and J.\u00a0Wang, Learning hierarchy-aware knowledge graph embeddings for link prediction, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, the Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7\u201312, 2020, AAAI Press, 2020, pp.\u00a03065\u20133072, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5701.","DOI":"10.1609\/aaai.v34i03.5701"}],"container-title":["Semantic Web"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/SW-233508","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:12Z","timestamp":1777613232000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/SW-233508"}},"subtitle":[],"editor":[{"given":"Claudia","family":"d\u2019Amato","sequence":"additional","affiliation":[{"name":"University of Bari, Italy"}],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,13]]},"references-count":81,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/sw-233508","relation":{},"ISSN":["2210-4968","1570-0844"],"issn-type":[{"value":"2210-4968","type":"electronic"},{"value":"1570-0844","type":"print"}],"subject":[],"published":{"date-parts":[[2023,12,13]]}}}