{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:54:21Z","timestamp":1780401261368,"version":"3.54.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"S9","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Natural Key R&D Program of China","award":["No.2017YFB1002101"],"award-info":[{"award-number":["No.2017YFB1002101"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.61922085"],"award-info":[{"award-number":["No.61922085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.61976211"],"award-info":[{"award-number":["No.61976211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.61702512"],"award-info":[{"award-number":["No.61702512"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-021-01622-7","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T07:02:32Z","timestamp":1638169352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion"],"prefix":"10.1186","volume":"21","author":[{"given":"Yinyu","family":"Lan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8850-1605","authenticated-orcid":false,"given":"Shizhu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"1622_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.inffus.2015.08.005","volume":"28","author":"G Bello-Orgaz","year":"2016","unstructured":"Bello-Orgaz G, Jung JJ, Camacho D. Social big data: recent achievements and new challenges. Inf Fusion. 2016;28:45\u201359.","journal-title":"Inf Fusion"},{"issue":"13","key":"1622_CR2","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1001\/jama.2013.393","volume":"309","author":"TB Murdoch","year":"2013","unstructured":"Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013;309(13):1351\u20132.","journal-title":"Jama"},{"key":"1622_CR3","doi-asserted-by":"crossref","unstructured":"Pujara J, Augustine E, Getoor L. Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of the 2017 conference on empirical methods in natural language processing; 2017.","DOI":"10.18653\/v1\/D17-1184"},{"key":"1622_CR4","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Neural information processing systems (NIPS); 2013. pp. 1\u20139."},{"key":"1622_CR5","unstructured":"Nickel M, Tresp V, Kriegel H-P. A three-way model for collective learning on multi-relational data. In: Icml; 2011."},{"key":"1622_CR6","unstructured":"Trouillon T, Welbl J, Riedel S, Gaussier \u00c9, Bouchard G. In: Complex embeddings for simple link prediction. In: International conference on machine learning. PMLR; 2016, pp. 2071\u201380."},{"key":"1622_CR7","unstructured":"Liu H, Wu Y, Yang Y. In: Analogical inference for multi-relational embeddings. In: International conference on machine learning. PMLR; 2017, pp. 2168\u201378."},{"key":"1622_CR8","unstructured":"Chen DY, Wang DZ. Web-scale knowledge inference using Markov logic networks. In: ICML workshop on structured learning: inferring graphs from structured and unstructured inputs. Association for Computational Linguistics; 2013. pp. 106\u201310."},{"key":"1622_CR9","doi-asserted-by":"crossref","unstructured":"Jiang S, Lowd D, Dou D. In: Learning to refine an automatically extracted knowledge base using Markov logic. In: 2012 IEEE 12th international conference on data mining. IEEE; 2012. pp. 912\u201317.","DOI":"10.1109\/ICDM.2012.156"},{"key":"1622_CR10","doi-asserted-by":"crossref","unstructured":"Pujara J, Miao H, Getoor L, Cohen W. In: Knowledge graph identification. In: International semantic web conference, Springer; 2013. pp. 542\u201357.","DOI":"10.1007\/978-3-642-41335-3_34"},{"key":"1622_CR11","unstructured":"Lao N, Mitchell T, Cohen W. Random walk inference and learning in a large scale knowledge base. In: Proceedings of the 2011 conference on empirical methods in natural language processing, 2011. pp. 529\u201339."},{"key":"1622_CR12","doi-asserted-by":"crossref","unstructured":"Neelakantan A, Roth B, McCallum A. Compositional vector space models for knowledge base completion. 2015. arXiv:1504.06662.","DOI":"10.3115\/v1\/P15-1016"},{"key":"1622_CR13","doi-asserted-by":"crossref","unstructured":"Das R, Neelakantan A, Belanger D, McCallum A. Chains of reasoning over entities, relations, and text using recurrent neural networks. 2016. arXiv:1607.01426.","DOI":"10.18653\/v1\/E17-1013"},{"key":"1622_CR14","unstructured":"Jiang X, Wang Q, Qi B, Qiu Y, Li P, Wang B. In: Attentive path combination for knowledge graph completion. In: Asian conference on machine learning, PMLR; 2017. pp. 590\u2013605."},{"key":"1622_CR15","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L. Deep contextualized word representations. 2018. arXiv:1802.05365.","DOI":"10.18653\/v1\/N18-1202"},{"key":"1622_CR16","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018. arXiv:1810.04805."},{"key":"1622_CR17","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. Roberta: A robustly optimized bert pretraining approach. 2019. arXiv:1907.11692."},{"key":"1622_CR18","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV. Xlnet: generalized autoregressive pretraining for language understanding. 2019. arXiv:1906.08237."},{"key":"1622_CR19","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al. Language models are few-shot learners. 2020. arXiv:2005.14165."},{"key":"1622_CR20","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. 2013. arXiv:1310.4546."},{"key":"1622_CR21","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP); 2014. pp. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"key":"1622_CR22","unstructured":"Wang H, Kulkarni V, Wang WY. Dolores: deep contextualized knowledge graph embeddings. 2018. arXiv:1811.00147."},{"key":"1622_CR23","doi-asserted-by":"crossref","unstructured":"Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q. Ernie: enhanced language representation with informative entities. 2019. arXiv:1905.07129.","DOI":"10.18653\/v1\/P19-1139"},{"key":"1622_CR24","unstructured":"Yao L, Mao C, Luo Y. Kg-bert: bert for knowledge graph completion. 2019. arXiv:1909.03193."},{"key":"1622_CR25","doi-asserted-by":"crossref","unstructured":"Chisholm A, Radford W, Hachey B. Learning to generate one-sentence biographies from wikidata. 2017. arXiv:1702.06235.","DOI":"10.18653\/v1\/E17-1060"},{"key":"1622_CR26","doi-asserted-by":"crossref","unstructured":"Kale M. Text-to-text pre-training for data-to-text tasks. 2020. arXiv:2005.10433.","DOI":"10.18653\/v1\/2020.inlg-1.14"},{"key":"1622_CR27","unstructured":"Tylenda T, Kondreddi SK, Weikum G. Spotting knowledge base facts in web texts. In: Proceedings of the 4th workshop on automated knowledge base construction; 2014. pp. 1\u20136."},{"key":"1622_CR28","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. 2017. arXiv preprint arXiv:1706.03762."},{"key":"1622_CR29","unstructured":"Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. 2015. arXiv:1503.02531."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01622-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-021-01622-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01622-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T05:29:39Z","timestamp":1726205379000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-021-01622-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":29,"journal-issue":{"issue":"S9","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["1622"],"URL":"https:\/\/doi.org\/10.1186\/s12911-021-01622-7","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"15 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2021","order":3,"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":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"335"}}