{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:15:49Z","timestamp":1737436549681,"version":"3.33.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05966-1","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T08:00:49Z","timestamp":1733990449000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An original model for multi-target learning of logical rules for knowledge graph reasoning"],"prefix":"10.1007","volume":"55","author":[{"given":"Haotian","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"5966_CR1","unstructured":"Agrawal R, Srikant R, et\u00a0al (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases. VLDB, Citeseer, pp 487\u2013499"},{"key":"5966_CR2","doi-asserted-by":"publisher","unstructured":"Bala\u017eevi\u0107 I, Allen C, Hospedales TM (2019) Tucker: Tensor factorization for knowledge graph completion. arXiv:1901.09590, https:\/\/doi.org\/10.48550\/arXiv.1901.09590","DOI":"10.48550\/arXiv.1901.09590"},{"key":"5966_CR3","doi-asserted-by":"publisher","unstructured":"Bollacker K, Evans C, Paritosh P, et\u00a0al (2008) 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. https:\/\/doi.org\/10.1145\/1376616.1376746","DOI":"10.1145\/1376616.1376746"},{"key":"5966_CR4","unstructured":"Bordes A, Usunier N, Garcia-Dur\u00e1n A, et\u00a0al (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th international conference on neural information processing systems - Volume 2. Curran Associates Inc., Red Hook, NY, USA, NIPS\u201913, pp 2787\u20132795"},{"key":"5966_CR5","doi-asserted-by":"publisher","unstructured":"Cohen WW (2016) Tensorlog: a differentiable deductive database. arXiv:1605.06523, https:\/\/doi.org\/10.48550\/arXiv.1605.06523","DOI":"10.48550\/arXiv.1605.06523"},{"key":"5966_CR6","doi-asserted-by":"publisher","unstructured":"Dettmers T, Minervini P, Stenetorp P et\u00a0al (2018) Convolutional 2d knowledge graph embeddings. In: Thirty-second AAAI confe-rence on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v32i1.11573","DOI":"10.1609\/aaai.v32i1.11573"},{"issue":"6","key":"5966_CR7","doi-asserted-by":"publisher","first-page":"062314","DOI":"10.1103\/PhysRevA.62.062314","volume":"62","author":"W D\u00fcr","year":"2000","unstructured":"D\u00fcr W, Vidal G, Cirac JI (2000) Three qubits can be entangled in two inequivalent ways. Phys Rev A 62(6):062314. https:\/\/doi.org\/10.1103\/PhysRevA.62.062314","journal-title":"Phys Rev A"},{"key":"5966_CR8","doi-asserted-by":"crossref","unstructured":"Faudree JR, Faudree RJ, Schmitt JR (2011) A survey of minimum saturated graphs. The Electron J Combinatorics 1000:DS19. \nhttps:\/\/doi.org\/10.37236\/41","DOI":"10.37236\/41"},{"key":"5966_CR9","doi-asserted-by":"publisher","unstructured":"Gao H, Yang K, Yang Y et\u00a0al (2021) Quatde: Dynamic quaternion embedding for knowledge graph completion. arXiv:2105.09002, https:\/\/doi.org\/10.48550\/arXiv.2105.09002","DOI":"10.48550\/arXiv.2105.09002"},{"key":"5966_CR10","doi-asserted-by":"crossref","unstructured":"Getoor L, Taskar B (2007) Introduction to statistical relational learning. MIT press","DOI":"10.7551\/mitpress\/7432.001.0001"},{"issue":"1","key":"5966_CR11","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1038\/s41467-022-29292-7","volume":"13","author":"A Gogleva","year":"2022","unstructured":"Gogleva A, Polychronopoulos D, Pfeifer M et al (2022) Knowledge graph-based recommendation framework identifies drivers of resistance in egfr mutant non-small cell lung cancer. Nat Commun 13(1):1667. https:\/\/doi.org\/10.1038\/s41467-022-29292-7","journal-title":"Nat Commun"},{"key":"5966_CR12","doi-asserted-by":"publisher","unstructured":"Guu K, Miller J, Liang P (2015) Traversing knowledge graphs in vector space. arXiv:1506.01094, https:\/\/doi.org\/10.48550\/arXiv.1506.01094","DOI":"10.48550\/arXiv.1506.01094"},{"key":"5966_CR13","doi-asserted-by":"publisher","first-page":"720","DOI":"10.4153\/CJM-1965-072-1","volume":"17","author":"A Hajnal","year":"1965","unstructured":"Hajnal A (1965) A theorem on k-saturated graphs. Can J Math 17:720\u2013724. https:\/\/doi.org\/10.4153\/CJM-1965-072-1","journal-title":"Can J Math"},{"key":"5966_CR14","doi-asserted-by":"publisher","unstructured":"Han C, He Q, Yu C et\u00a0al (2023) Logical entity representation in knowledge-graphs for differentiable rule learning. arXiv:2305.12738, https:\/\/doi.org\/10.48550\/arXiv.2305.12738","DOI":"10.48550\/arXiv.2305.12738"},{"issue":"1","key":"5966_CR15","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","volume":"8","author":"J Han","year":"2004","unstructured":"Han J, Pei J, Yin Y et al (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Disc 8(1):53\u201387. https:\/\/doi.org\/10.1023\/B:DAMI.0000005258.31418.83","journal-title":"Data Min Knowl Disc"},{"issue":"8","key":"5966_CR16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1007\/978-3-642-24797-2_4","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4","journal-title":"Neural Comput"},{"key":"5966_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070843","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. https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5966_CR18","first-page":"1","volume":"1","author":"WWCFY Kathryn","year":"2018","unstructured":"Kathryn WWCFY, Mazaitis R (2018) Tensorlog: deep learning meets probabilistic databases. J Artif Intell Res 1:1\u201315","journal-title":"J Artif Intell Res"},{"key":"5966_CR19","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980, https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"5966_CR20","doi-asserted-by":"publisher","unstructured":"Kok S, Domingos P (2007) Statistical predicate invention. In: Proceedings of the 24th international conference on Machine learning. pp 433\u2013440. https:\/\/doi.org\/10.1145\/1273496.1273551","DOI":"10.1145\/1273496.1273551"},{"key":"5966_CR21","doi-asserted-by":"publisher","unstructured":"Lin Q, Liu J, Xu F et\u00a0al (2022) Incorporating context graph with logical reasoning for inductive relation prediction. In: Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR \u201922, pp 893\u2013903. https:\/\/doi.org\/10.1145\/3477495.3531996","DOI":"10.1145\/3477495.3531996"},{"key":"5966_CR22","doi-asserted-by":"publisher","unstructured":"Lin Y, Liu Z, Sun M et\u00a0al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v29i1.9491","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"5966_CR23","doi-asserted-by":"publisher","unstructured":"Liu L, Du B, Xu J et\u00a0al (2022a) Joint knowledge graph completion and question answering. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, KDD \u201922, pp 1098\u20131108. https:\/\/doi.org\/10.1145\/3534678.3539289","DOI":"10.1145\/3534678.3539289"},{"key":"5966_CR24","doi-asserted-by":"publisher","unstructured":"Liu Y, Sun Z, Li G et\u00a0al (2022b) I know what you do not know: knowledge graph embedding via co-distillation learning. In: Proceedings of the 31st ACM international conference on information & knowledge management. Association for Computing Machinery, New York, NY, USA, CIKM \u201922, pp 1329\u20131338. https:\/\/doi.org\/10.1145\/3511808.3557355","DOI":"10.1145\/3511808.3557355"},{"issue":"11","key":"5966_CR25","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39\u201341. https:\/\/doi.org\/10.1145\/219717.219748","journal-title":"Commun ACM"},{"key":"5966_CR26","unstructured":"Miller GA (1998) WordNet: An electronic lexical database. MIT press"},{"key":"5966_CR27","doi-asserted-by":"publisher","unstructured":"Onoe Y, Boratko M, McCallum A et\u00a0al (2021) Modeling fine-grained entity types with box embeddings. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.160","DOI":"10.18653\/v1\/2021.acl-long.160"},{"key":"5966_CR28","volume-title":"PyTorch: an imperative style, high-performance deep learning library","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style, high-performance deep learning library. Curran Associates Inc., Red Hook, NY, USA"},{"key":"5966_CR29","doi-asserted-by":"publisher","unstructured":"Pryor C, Dickens C, Augustine E et\u00a0al (2022) Neupsl: Neural probabilistic soft logic. arXiv:2205.14268, https:\/\/doi.org\/10.48550\/arXiv.2205.14268","DOI":"10.48550\/arXiv.2205.14268"},{"key":"5966_CR30","doi-asserted-by":"publisher","unstructured":"Qu M, Tang J (2019) Probabilistic logic neural networks for reasoning. arXiv:1906.08495, https:\/\/doi.org\/10.48550\/arXiv.1906.08495","DOI":"10.48550\/arXiv.1906.08495"},{"key":"5966_CR31","doi-asserted-by":"publisher","unstructured":"Qu M, Chen J, Xhonneux LP et\u00a0al (2020) Rnnlogic: Learning logic rules for reasoning on knowledge graphs. arXiv:2010.04029, https:\/\/doi.org\/10.48550\/arXiv.2010.04029","DOI":"10.48550\/arXiv.2010.04029"},{"key":"5966_CR32","doi-asserted-by":"publisher","unstructured":"Sadeghian A, Armandpour M, Ding P et\u00a0al (2019) Drum: end-to-end differentiable rule mining on knowledge graphs. arXiv:1911.00055, https:\/\/doi.org\/10.48550\/arXiv.1911.00055","DOI":"10.48550\/arXiv.1911.00055"},{"key":"5966_CR33","doi-asserted-by":"publisher","unstructured":"Saxena A, Kochsiek A, Gemulla R (2022) Sequence-to-sequence knowledge graph completion and question answering. arXiv:2203.10321, https:\/\/doi.org\/10.48550\/arXiv.2203.10321","DOI":"10.48550\/arXiv.2203.10321"},{"key":"5966_CR34","doi-asserted-by":"publisher","unstructured":"Stokman FN, de\u00a0Vries PH (1988) Structuring knowledge in a graph. In: van\u00a0der Veer GC, Mulder G (eds) Human-computer interaction. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 186\u2013206. https:\/\/doi.org\/10.1007\/978-3-642-73402-1_12","DOI":"10.1007\/978-3-642-73402-1_12"},{"key":"5966_CR35","doi-asserted-by":"publisher","unstructured":"Sun Z, Deng ZH, Nie JY et\u00a0al (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv:1902.10197, https:\/\/doi.org\/10.48550\/arXiv.1902.10197","DOI":"10.48550\/arXiv.1902.10197"},{"key":"5966_CR36","doi-asserted-by":"crossref","unstructured":"Toutanova K, Chen D (2015) Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd workshop on continuous vector space models and their compositionality. pp 57\u201366","DOI":"10.18653\/v1\/W15-4007"},{"key":"5966_CR37","unstructured":"Trouillon T, Welbl J, Riedel S et\u00a0al (2016) Complex embeddings for simple link prediction. In: International conference on machine learning. PMLR, pp 2071\u20132080"},{"key":"5966_CR38","doi-asserted-by":"publisher","unstructured":"Wang P, Dou D, Wu F et\u00a0al (2019a) Logic rules powered knowledge graph embedding. arXiv:1903.03772, https:\/\/doi.org\/10.48550\/arXiv.1903.03772","DOI":"10.48550\/arXiv.1903.03772"},{"key":"5966_CR39","doi-asserted-by":"publisher","unstructured":"Wang P, Xie X, Wang X et\u00a0al (2023) Reasoning through memorization: nearest neighbor knowledge graph embeddings. In: CCF international conference on natural language processing and chinese computing. Springer, pp 111\u2013122. https:\/\/doi.org\/10.1007\/978-3-031-44693-1_9","DOI":"10.1007\/978-3-031-44693-1_9"},{"key":"5966_CR40","unstructured":"Wang PW, Stepanova D, Domokos C et\u00a0al (2019b) Differentiable learning of numerical rules in knowledge graphs. In: International conference on learning representations"},{"key":"5966_CR41","doi-asserted-by":"crossref","unstructured":"Wang WY, Mazaitis K, Cohen WW (2013) Programming with personalized pagerank: a locally groundable first-order probabilistic logic. In: Proceedings of the 22nd ACM international conference on information & knowledge management. pp 2129\u20132138","DOI":"10.1145\/2505515.2505573"},{"key":"5966_CR42","doi-asserted-by":"publisher","unstructured":"Yang B, Yih Wt, He X et\u00a0al (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv:1412.6575, https:\/\/doi.org\/10.48550\/arXiv.1412.6575","DOI":"10.48550\/arXiv.1412.6575"},{"key":"5966_CR43","doi-asserted-by":"publisher","unstructured":"Yang F, Yang Z, Cohen WW (2017) Differentiable learning of logical rules for knowledge base reasoning. arXiv:1702.08367, https:\/\/doi.org\/10.48550\/arXiv.1702.08367","DOI":"10.48550\/arXiv.1702.08367"},{"key":"5966_CR44","doi-asserted-by":"publisher","unstructured":"Yang Y, Song L (2019) Learn to explain efficiently via neural logic inductive learning. arXiv:1910.02481, https:\/\/doi.org\/10.48550\/arXiv.1910.02481","DOI":"10.48550\/arXiv.1910.02481"},{"key":"5966_CR45","doi-asserted-by":"crossref","unstructured":"Yang Y, Huang C, Xia L et\u00a0al (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. pp 1434\u20131443","DOI":"10.1145\/3477495.3532009"},{"key":"5966_CR46","doi-asserted-by":"crossref","unstructured":"Zhang W, Paudel B, Wang L et\u00a0al (2019) Iteratively learning embeddings and rules for knowledge graph reasoning. In: The world wide web conference. pp 2366\u20132377","DOI":"10.1145\/3308558.3313612"},{"key":"5966_CR47","doi-asserted-by":"publisher","unstructured":"Zhang Y, Chen X, Yang Y et\u00a0al (2020) Efficient probabilistic logic reasoning with graph neural networks. arXiv:2001.11850, https:\/\/doi.org\/10.48550\/arXiv.2001.11850","DOI":"10.48550\/arXiv.2001.11850"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05966-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05966-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05966-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:06:51Z","timestamp":1737385611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05966-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5966"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05966-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,12,12]]},"assertion":[{"value":"5 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"We declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"138"}}