{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:17:06Z","timestamp":1761808626514,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031472398"},{"type":"electronic","value":"9783031472404"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47240-4_16","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:02:40Z","timestamp":1698825760000},"page":"290-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Comprehensive Study on\u00a0Knowledge Graph Embedding over\u00a0Relational Patterns Based on\u00a0Rule Learning"],"prefix":"10.1007","author":[{"given":"Long","family":"Jin","sequence":"first","affiliation":[]},{"given":"Zhen","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Mingyang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"16_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: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"16_CR2","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, vol. 26 (2013)"},{"key":"16_CR3","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"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Cao, Z., Xu, Q., Yang, Z., Cao, X., Huang, Q.: Dual quaternion knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6894\u20136902 (2021)","DOI":"10.1609\/aaai.v35i8.16850"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Goldberg, S., Wang, D.Z., Johri, S.S.: Ontological pathfinding. In: Proceedings of the 2016 International Conference on Management of Data, pp. 835\u2013846 (2016)","DOI":"10.1145\/2882903.2882954"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, K., Yang, Z., Zhang, M., Sun, Y.: Uniker: a unified framework for combining embedding and definite horn rule reasoning for knowledge graph inference. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9753\u20139771 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.769"},{"key":"16_CR7","unstructured":"Cui, W., Chen, X.: Instance-based learning for knowledge base completion. arXiv preprint arXiv:2211.06807 (2022)"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1009863704807","volume":"3","author":"L Dehaspe","year":"1999","unstructured":"Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Min. Knowl. Disc. 3, 7\u201336 (1999)","journal-title":"Data Min. Knowl. Disc."},{"issue":"6","key":"16_CR9","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":"16_CR10","doi-asserted-by":"crossref","unstructured":"Gal\u00e1rraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413\u2013422 (2013)","DOI":"10.1145\/2488388.2488425"},{"key":"16_CR11","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: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11918"},{"key":"16_CR12","unstructured":"Hajimoradlou, A., Kazemi, M.: Stay positive: knowledge graph embedding without negative sampling. arXiv preprint arXiv:2201.02661 (2022)"},{"key":"16_CR13","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":"16_CR14","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: 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 (Volume 1: Long Papers), pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"issue":"2","key":"16_CR15","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., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494\u2013514 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"16_CR16","unstructured":"Kamigaito, H., Hayashi, K.: Comprehensive analysis of negative sampling in knowledge graph representation learning. In: International Conference on Machine Learning, pp. 10661\u201310675. PMLR (2022)"},{"key":"16_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-49461-2_3","volume-title":"The Semantic Web","author":"J Lajus","year":"2020","unstructured":"Lajus, J., Gal\u00e1rraga, L., Suchanek, F.: Fast and exact rule mining with AMIE 3. In: Harth, A., Kirrane, S., Ngonga Ngomo, A.-C., Paulheim, H., Rula, A., Gentile, A.L., Haase, P., Cochez, M. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 36\u201352. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49461-2_3"},{"issue":"2","key":"16_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann, J., et al.: DBPedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic web 6(2), 167\u2013195 (2015)","journal-title":"Semantic web"},{"key":"16_CR19","unstructured":"Li, R., Cao, Y., Zhu, Q., Li, X., Fang, F.: Is there more pattern in knowledge graph? exploring proximity pattern for knowledge graph embedding. arXiv preprint arXiv:2110.00720 (2021)"},{"key":"16_CR20","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: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"issue":"11","key":"16_CR21","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1007\/978-3-319-71249-9_40","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Minervini","year":"2017","unstructured":"Minervini, P., Costabello, L., Mu\u00f1oz, E., Nov\u00e1\u010dek, V., Vandenbussche, P.-Y.: Regularizing knowledge graph embeddings via equivalence and inversion axioms. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 668\u2013683. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_40"},{"key":"16_CR23","unstructured":"Mohamed, S.K., Nov\u00e1cek, V., Vandenbussche, P.Y., Mu\u00f1oz, E.: Loss functions in knowledge graph embedding models. DL4KG@ ESWC. 2377, 1\u201310 (2019)"},{"key":"16_CR24","unstructured":"Nickel, M., Tresp, V., Kriegel, H.P., et al.: A three-way model for collective learning on multi-relational data. In: ICML, vol. 11, pp. 3104482\u20133104584 (2011)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Niu, G., et al.: Rule-guided compositional representation learning on knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2950\u20132958 (2020)","DOI":"10.1609\/aaai.v34i03.5687"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Pirr\u00f2, G.: Relatedness and TBOX-driven rule learning in large knowledge bases. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2975\u20132982 (2020)","DOI":"10.1609\/aaai.v34i03.5690"},{"key":"16_CR27","unstructured":"Qu, M., Tang, J.: Probabilistic logic neural networks for reasoning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Sharma, A., Talukdar, P., et al.: Towards understanding the geometry of knowledge graph embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 122\u2013131 (2018)","DOI":"10.18653\/v1\/P18-1012"},{"key":"16_CR29","unstructured":"Srinivasan, A.: The aleph manual (2001)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"16_CR31","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Suresh, S., Neville, J.: A hybrid model for learning embeddings and logical rules simultaneously from knowledge graphs. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1280\u20131285. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00164"},{"key":"16_CR33","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. PMLR (2016)"},{"issue":"12","key":"16_CR34","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.S.: Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5329\u20135336 (2019)","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"16_CR37","unstructured":"Xie, R., Liu, Z., Sun, M., et al.: Representation learning of knowledge graphs with hierarchical types. In: IJCAI, vol. 2016, pp. 2965\u20132971 (2016)"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Xiong, C., Power, R., Callan, J.: Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1271\u20131279 (2017)","DOI":"10.1145\/3038912.3052558"},{"key":"16_CR39","unstructured":"Xu, Z., Ye, P., Chen, H., Zhao, M., Chen, H., Zhang, W.: Ruleformer: context-aware rule mining over knowledge graph. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 2551\u20132560 (2022)"},{"key":"16_CR40","unstructured":"Yang, B., Mitchell, T.: Leveraging knowledge bases in LSTMS for improving machine reading. arXiv preprint arXiv:1902.09091 (2019)"},{"key":"16_CR41","unstructured":"Yang, B., Yih, W.t., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)"},{"key":"16_CR42","unstructured":"Zhang, W., Chen, J., Li, J., Xu, Z., Pan, J.Z., Chen, H.: Knowledge graph reasoning with logics and embeddings: survey and perspective. arXiv preprint arXiv:2202.07412 (2022)"},{"key":"16_CR43","unstructured":"Zhang, W., Chen, M., Xu, Z., Zhu, Y., Chen, H.: Explaining knowledge graph embedding via latent rule learning (2021)"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: The World Wide Web Conference, pp. 2366\u20132377 (2019)","DOI":"10.1145\/3308558.3313612"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cai, J., Zhang, Y., Wang, J.: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3065\u20133072 (2020)","DOI":"10.1609\/aaai.v34i03.5701"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47240-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T05:23:15Z","timestamp":1730438595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47240-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031472398","9783031472404"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47240-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2023.semanticweb.org\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248","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":"58","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":"0","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":"1","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)"}}]}}