{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T08:08:25Z","timestamp":1768291705933,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>KGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/202.118.75.18:18895\/browser\/\">http:\/\/202.118.75.18:18895\/browser\/<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-1112-5","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T08:08:50Z","timestamp":1594282130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["KGHC: a knowledge graph for hepatocellular carcinoma"],"prefix":"10.1186","volume":"20","author":[{"given":"Nan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"issue":"9822","key":"1112_CR1","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/S0140-6736(11)61347-0","volume":"379","author":"A Forner","year":"2012","unstructured":"Forner A, Llovet JM, Bruix J. Hepatocellular carcinoma. Lancet. 2012;379(9822):1245\u201355.","journal-title":"Lancet"},{"key":"1112_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2147\/JHC.S61146","volume":"3","author":"J Balogh","year":"2016","unstructured":"Balogh J, David Victor III, et al. Hepatocellular carcinoma: a review. J Hepatocell Carcinoma. 2016;3:41\u201353.","journal-title":"J Hepatocell Carcinoma"},{"issue":"3","key":"1112_CR3","first-page":"153","volume":"10","author":"AM Crissien","year":"2014","unstructured":"Crissien AM, Frenette C. Current management of hepatocellular carcinoma. Gastroenterol Hepatol. 2014;10(3):153\u201361.","journal-title":"Gastroenterol Hepatol"},{"key":"1112_CR4","unstructured":"Amit S. Introducing the knowledge graph, vol. America: Official Blog of Google; 2012."},{"issue":"1","key":"1112_CR5","doi-asserted-by":"publisher","first-page":"5994","DOI":"10.1038\/s41598-017-05778-z","volume":"7","author":"M Rotmensch","year":"2017","unstructured":"Rotmensch M, Halpern Y, Tlimat A, et al. Learning a health knowledge graph from electronic medical records. Sci Rep. 2017;7(1):5994.","journal-title":"Sci Rep"},{"key":"1112_CR6","first-page":"1","volume":"2","author":"L Shi","year":"2017","unstructured":"Shi L, Li S, et al. Semantic health knowledge graph: semantic integration of heterogeneous medical knowledge and services. Biomed Res Int. 2017;2:1\u201312.","journal-title":"Biomed Res Int"},{"key":"1112_CR7","doi-asserted-by":"crossref","unstructured":"Yuan J, Jin Z, et al. Constructing biomedical domain-specific knowledge graph with minimum supervision. Knowledge and Information Systems.2019;62:317\u201336.","DOI":"10.1007\/s10115-019-01351-4"},{"issue":"1","key":"1112_CR8","first-page":"1","volume":"16","author":"P Ernst","year":"2015","unstructured":"Ernst P, Siu A, Weikum G. Knowlife: a versatile approach for constructing a large knowledge graph for biomedical sciences. BMC biomedical sciences. 2015;16(1):1.","journal-title":"BMC biomedical sciences"},{"issue":"D1","key":"1112_CR9","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn M, Letunic I, Jensen LJ, et al. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016;44(D1):D1075.","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"1112_CR10","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.gpb.2012.04.002","volume":"10","author":"M Danishuddin","year":"2012","unstructured":"Danishuddin M, Kaushal L, Baig MH, Khan AU. Amdd: Antimicrobial drug database. Genomics Proteom Bioinforma. 2012;10(6):360\u20133.","journal-title":"Genomics Proteom Bioinforma"},{"issue":"1","key":"1112_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1756-0381-4-1","volume":"4","author":"D Urbach","year":"2011","unstructured":"Urbach D, Moore JH. Mining the diseasome. BioData mining. 2011;4(1):1.","journal-title":"BioData mining"},{"key":"1112_CR12","doi-asserted-by":"publisher","first-page":"007","DOI":"10.1093\/database\/bar007","volume":"2011","author":"C Taccioli","year":"2011","unstructured":"Taccioli C, Maselli V, Tegn\u00e9r J, Gomez-Cabrero D, Altobelli G, Emmett W, Lescai F, Gustincich S, Stupka E. Parkdb: a parkinson\u2019s disease gene expression database. Database. 2011;2011:007.","journal-title":"Database."},{"key":"1112_CR13","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1093\/database\/bav123","volume":"2016","author":"J Kringelum","year":"2016","unstructured":"Kringelum J, Kjaerulff SK, Brunak S, Lund O, Oprea TI, Taboureau O. Chemprot-3.0: a global chemical biology diseases mapping. Database. 2016;2016:123.","journal-title":"Database."},{"key":"1112_CR14","unstructured":"National Library of Medicine (US) (2005) MedlinePlus [Internet]. http:\/\/www.nlm.nih.gov\/medlineplus\/ (23 March 2015, date last accessed)."},{"key":"1112_CR15","unstructured":"National Center for Biotechnology Information (US) (2005) PubMed Help [Internet]. http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK3827\/ (23 March 2015, date last accessed)."},{"key":"1112_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/database\/bav049","volume":"2015","author":"AMR Kamdar","year":"2015","unstructured":"Kamdar AMR, Dumontier M. Ebola virus-centered knowledge base [J]. DataBase. 2015;2015:1\u201311.","journal-title":"DataBase"},{"key":"1112_CR17","first-page":"p72","volume-title":"Disambiguation of entities in medline abstracts by combining mesh terms with knowledge","author":"A Siu","year":"2016","unstructured":"Siu A, Ernst P, Weikum G. Disambiguation of entities in medline abstracts by combining mesh terms with knowledge. Florence: ACL; 2016. p. p72."},{"key":"1112_CR18","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32:267\u201370.","journal-title":"Nucleic Acids Res"},{"key":"1112_CR19","volume-title":"An automatic approach for constructing a knowledge base of symptoms in Chinese. Biological Ontologies and Knowledge bases workshop on IEEE BIBM","author":"T Ruan","year":"2016","unstructured":"Ruan T, Wang M, Sun J et al. An automatic approach for constructing a knowledge base of symptoms in Chinese. Biological Ontologies and Knowledge bases workshop on IEEE BIBM, 2016."},{"issue":"6","key":"1112_CR20","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jbi.2003.11.003","volume":"36","author":"TC Rindflesch","year":"2003","unstructured":"Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text [J]. J Biomed Inform. 2003;36(6):462\u201377.","journal-title":"J Biomed Inform"},{"key":"1112_CR21","doi-asserted-by":"publisher","first-page":"D5","DOI":"10.1093\/nar\/gkl1031","volume":"35","author":"DL Wheeler","year":"2007","unstructured":"Wheeler DL, Barrett T, Benson DA, et al. Database resources of the National Center for biotechnology information. Nucleic Acids Res. 2007;35:D5\u2013D12.","journal-title":"Nucleic Acids Res"},{"key":"1112_CR22","doi-asserted-by":"crossref","unstructured":"Benson,D.A., Cavanaugh, M., Clark, K. et al. GenBank Nucleic Acids Res, 2013, 41:D36-D42.","DOI":"10.1093\/nar\/gks1195"},{"key":"1112_CR23","doi-asserted-by":"publisher","first-page":"D57","DOI":"10.1093\/nar\/gkr1163","volume":"40","author":"T Barrett","year":"2012","unstructured":"Barrett T, Clark K, Gevorgyan R, et al. BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res. 2012;40:D57\u201363.","journal-title":"Nucleic Acids Res"},{"issue":"23","key":"1112_CR24","doi-asserted-by":"publisher","first-page":"3158","DOI":"10.1093\/bioinformatics\/bts591","volume":"28","author":"H Kilicoglu","year":"2012","unstructured":"Kilicoglu H, Shin D, Fiszman M, et al. SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics. 2012;28(23):3158\u201360.","journal-title":"Bioinformatics"},{"issue":"1\u20132","key":"1112_CR25","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3233\/ISU-2011-0627","volume":"31","author":"TC Rindflesch","year":"2011","unstructured":"Rindflesch TC, Kilicoglu H, Fiszman M, et al. Semantic MEDLINE: an advanced information management application for biomedicine [J]. Inf Serv Use. 2011;31(1\u20132):15\u201321.","journal-title":"Inf Serv Use"},{"key":"1112_CR26","volume-title":"Semantic MEDLINE: A Web Application to Manage the Results of PubMed searches. Proceedings of the 3rd International Symposium on Semantic Mining in Biomedicine","author":"HF-M Kilicoglu","year":"2008","unstructured":"Kilicoglu HF-M. Semantic MEDLINE: A Web Application to Manage the Results of PubMed searches. Proceedings of the 3rd International Symposium on Semantic Mining in Biomedicine; 2008."},{"issue":"8","key":"1112_CR27","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1093\/bioinformatics\/btx761","volume":"34","author":"L Luo","year":"2017","unstructured":"Luo L, Yang Z, Yang P, et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics. 2017;34(8):1381\u20138.","journal-title":"Bioinformatics."},{"key":"1112_CR28","volume-title":"Bidirectional LSTM-CRF Models for Sequence Tagging. Computer Science","author":"Z Huang","year":"2015","unstructured":"Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF Models for Sequence Tagging. Computer Science; 2015."},{"issue":"23","key":"1112_CR29","doi-asserted-by":"publisher","first-page":"4087","DOI":"10.1093\/bioinformatics\/bty449","volume":"34","author":"JM Giorgi","year":"2018","unstructured":"Giorgi JM, Bader GD. Transfer learning for biomedical named entity recognition with neural network. Bioinformatics. 2018;34(23):4087\u201394.","journal-title":"Bioinformatics."},{"key":"1112_CR30","doi-asserted-by":"crossref","unstructured":"Ji B, Liu R, et al. A hybrid approach for named entity recognition in Chinese electronic medical record. BMC Med Informatics Decision Making. 2019;19:64.","DOI":"10.1186\/s12911-019-0767-2"},{"key":"1112_CR31","doi-asserted-by":"crossref","unstructured":"Leaman R, et al. tmChem: a high performance approach for chemical named entity recognition and normalization. J Cheminformatics. 2015;7:S3.","DOI":"10.1186\/1758-2946-7-S1-S3"},{"key":"1112_CR32","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1758-2946-7-S1-S4","volume":"7","author":"Y Lu","year":"2015","unstructured":"Lu Y, et al. CHEMDNER system with mixed conditional random fields and multi-scale word clustering. J Cheminf. 2015;7:S4.","journal-title":"J Cheminf"},{"key":"1112_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1145\/3079452.3079501","volume-title":"Proceedings of the 2017 International conference on digital health. ACM","author":"C Pandey","year":"2017","unstructured":"Pandey C, et al. Improving RNN with attention and embedding for adverse drug reactions. In: Proceedings of the 2017 International conference on digital health. ACM; 2017. p. 67\u201371."},{"key":"1112_CR34","unstructured":"Santisteban J, Tejada-C\u00e1rcamo J. Unilateral Jaccard similarity coefficient. In: GSB@ SIGIR, 2015, 23\u201327."},{"key":"1112_CR35","first-page":"73","volume-title":"Exploring parallel tractability of ontology materialization. European Conference on Artificial Intelligence","author":"ZQ Zhou","year":"2016","unstructured":"Zhou ZQ, Qi GL, Glimm B. Exploring parallel tractability of ontology materialization. European Conference on Artificial Intelligence; 2016. p. 73\u201381."},{"key":"1112_CR36","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1145\/2384716.2384777","volume-title":"A programmatic introduction to Neo4j[C]. Conference on Systems, Programming, and Applications: Software for Humanity","author":"J Webber","year":"2012","unstructured":"Webber J. A programmatic introduction to Neo4j[C]. Conference on Systems, Programming, and Applications: Software for Humanity; 2012. p. 217\u20138."},{"key":"1112_CR37","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/234034a0","volume":"234","author":"M Levandowsky","year":"1971","unstructured":"Levandowsky M, Winter D. Distance between sets. Nature. 1971;234:34\u20135.","journal-title":"Nature"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-1112-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-020-1112-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-1112-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T23:20:04Z","timestamp":1625786404000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-1112-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":37,"journal-issue":{"issue":"S3","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["1112"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-1112-5","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"9 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"135"}}