{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T01:04:32Z","timestamp":1790039072137,"version":"4.0.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Biomed Semant"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence.<\/jats:p>\n                    <jats:p>In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13326-017-0168-3","type":"journal-article","created":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T08:55:35Z","timestamp":1517302535000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Exploiting graph kernels for high performance biomedical relation extraction"],"prefix":"10.1186","volume":"9","author":[{"given":"Nagesh C.","family":"Panyam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karin","family":"Verspoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trevor","family":"Cohn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kotagiri","family":"Ramamohanarao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"issue":"13","key":"168_CR1","doi-asserted-by":"publisher","first-page":"i41","DOI":"10.1093\/bioinformatics\/btm229","volume":"23","author":"WA Baumgartner Jr","year":"2007","unstructured":"Baumgartner Jr WA, Cohen KB, Fox LM, Acquaah-Mensah G, Hunter L. Manual curation is not sufficient for annotation of genomic databases. Bioinformatics. 2007; 23(13):i41\u20138. Vancouver.","journal-title":"Bioinformatics"},{"key":"168_CR2","doi-asserted-by":"crossref","unstructured":"Islamaj Dogan R, Murray GC, N\u00e9v\u00e9ol A, Lu Z. Understanding pubmed\u00ae; user search behavior through log analysis. Database. 2009 ;2009.","DOI":"10.1093\/database\/bap018"},{"key":"168_CR3","doi-asserted-by":"crossref","unstructured":"Wei CH, Peng Y, Leaman R, Davis AP, Mattingly CJ, Li J, Wiegers TC, Lu Z. Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database; 2016.","DOI":"10.1093\/database\/baw032"},{"key":"168_CR4","doi-asserted-by":"crossref","unstructured":"Bunescu RC, Mooney RJ. A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics;2005. p. 724\u201331.","DOI":"10.3115\/1220575.1220666"},{"key":"168_CR5","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J. Relation Classification via Convolutional Deep Neural Network. In: COLING.2014. p. 2335\u201344."},{"key":"168_CR6","doi-asserted-by":"crossref","unstructured":"Xu Y, Mou L, Li G, Chen Y, Peng H, Jin Z. Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths. In: EMNLP.2015. p. 1785\u201394.","DOI":"10.18653\/v1\/D15-1206"},{"key":"168_CR7","doi-asserted-by":"crossref","unstructured":"Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The stanford corenlp natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations.2014. p. 55\u201360.","DOI":"10.3115\/v1\/P14-5010"},{"key":"168_CR8","doi-asserted-by":"crossref","unstructured":"Collins M, Duffy N. Convolution kernels for natural language. In: Advances in Neural Information Processing Systems.2001. p. 625\u201332.","DOI":"10.7551\/mitpress\/1120.003.0085"},{"key":"168_CR9","doi-asserted-by":"crossref","unstructured":"Smola AJ, Vishwanathan SVN. Fast kernels for string and tree matching. Advances in neural information processing systems. 2003;:585\u201392.","DOI":"10.7551\/mitpress\/4057.003.0008"},{"key":"168_CR10","doi-asserted-by":"crossref","unstructured":"Moschitti A. Efficient convolution kernels for dependency and constituent syntactic trees. In: European Conference on Machine Learning. Springer;2006. p. 318\u201329.","DOI":"10.1007\/11871842_32"},{"key":"168_CR11","volume-title":"Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. CIKM \u201914","author":"P Annesi","year":"2014","unstructured":"Annesi P, Croce D, Basili R. Semantic compositionality in tree kernels. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. CIKM \u201914. New York: ACM;2014. p. 1029\u201338. doi:http:\/\/dx.doi.org\/10.1145\/2661829.2661955."},{"key":"168_CR12","doi-asserted-by":"crossref","unstructured":"De Marneffe MC, Manning CD. Stanford typed dependencies manual. Technical report. Stanford University; 2008. https:\/\/nlp.stanford.edu\/software\/dependencies_manual.pdf.","DOI":"10.3115\/1608858.1608859"},{"issue":"5","key":"168_CR13","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.jbi.2009.07.004","volume":"42","author":"M Lan","year":"2009","unstructured":"Lan M, Tan CL, Su J. Feature generation and representations for protein-protein interaction classification. J Biomed Inf. 2009; 42(5):866\u201372.","journal-title":"J Biomed Inf"},{"key":"168_CR14","doi-asserted-by":"crossref","unstructured":"Airola A, Pyysalo S, Bj\u00f6rne J, Pahikkala T, Ginter F, Salakoski T. A graph kernel for protein-protein interaction extraction. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing. Association for Computational Linguistics;2008. p. 1\u20139.","DOI":"10.3115\/1572306.1572308"},{"key":"168_CR15","unstructured":"Panyam NC, Verspoor K, Cohn T, Kotagiri R. ASM Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction. In: Proceedings of the Australasian Language Technology Association Workshop. 2016. p. 65\u201373."},{"issue":"4","key":"168_CR16","doi-asserted-by":"publisher","first-page":"60954","DOI":"10.1371\/journal.pone.0060954","volume":"8","author":"H Liu","year":"2013","unstructured":"Liu H, Hunter L, Ke\u0161elj V, Verspoor K. Approximate subgraph matching-based literature mining for biomedical events and relations. PloS ONE. 2013; 8(4):60954.","journal-title":"PloS ONE"},{"key":"168_CR17","doi-asserted-by":"crossref","unstructured":"Kim JD, Pyysalo S, Ohta T, Bossy R, Nguyen N, Tsujii J. Overview of bionlp shared task 2011. In: Proceedings of the BioNLP Shared Task 2011 Workshop. Association for Computational Linguistics;2011. p. 1\u20136.","DOI":"10.3115\/1572340.1572342"},{"key":"168_CR18","unstructured":"Panyam NC, Verspoor K, Cohn T, Ramamohanarao K. Exploiting Tree Kernels for High Performance Chemical Induced Disease Relation Extraction. In: Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine, 2016, Potsdam, Germany, August 4-5: 2016. http:\/\/ceur-ws.org\/Vol-1650\/smbm16Chandrasekarasastry.pdf."},{"issue":"Feb","key":"168_CR19","first-page":"1083","volume":"3","author":"D Zelenko","year":"2003","unstructured":"Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction. J Mach Learn Res. 2003; 3(Feb):1083\u2013106.","journal-title":"J Mach Learn Res"},{"key":"168_CR20","first-page":"124","volume":"2011","author":"FM Chowdhury","year":"2011","unstructured":"Chowdhury FM, Lavelli A, Moschitti A. A Study on Dependency Tree Kernels for Automatic Extraction of Protein-Protein Interaction. BioNLP. 2011; 2011:124\u201333.","journal-title":"BioNLP"},{"key":"168_CR21","doi-asserted-by":"publisher","first-page":"048","DOI":"10.1093\/database\/baw048","volume":"2016","author":"H Zhou","year":"2016","unstructured":"Zhou H, Deng H, Chen L, Yang Y, Jia C, Huang D. Exploiting syntactic and semantics information for chemical\u2013disease relation extraction. Database. 2016; 2016:048.","journal-title":"Database"},{"key":"168_CR22","volume-title":"Proceedings of ACL-IJCNLP 2015 System Demonstrations","author":"S Filice","year":"2015","unstructured":"Filice S, Castellucci G, Croce D, Basili R. Kelp: a kernel-based learning platform for natural language processing. In: Proceedings of ACL-IJCNLP 2015 System Demonstrations. Beijing: Association for Computational Linguistics and The Asian Federation of Natural Language Processing;2015. p. 19\u201324. http:\/\/www.aclweb.org\/anthology\/P15-4004."},{"key":"168_CR23","volume-title":"Proceedings of BioNLP 2011 Workshop. BioNLP \u201911","author":"FM Chowdhury","year":"2011","unstructured":"Chowdhury FM, Lavelli A, Moschitti A. A study on dependency tree kernels for automatic extraction of protein-protein interaction. In: Proceedings of BioNLP 2011 Workshop. BioNLP \u201911. Stroudsburg: Association for Computational Linguistics;2011. p. 124\u201333. http:\/\/dl.acm.org\/citation.cfm?id=2002902.2002921."},{"key":"168_CR24","first-page":"35","volume":"761","author":"J Bjorne","year":"2011","unstructured":"Bjorne J, Airola A, Pahikkala T, Salakoski T. Drug-drug interaction extraction from biomedical texts with SVM and RLS classifiers. CEUR Workshop Proceedings. 2011; 761:35\u201342.","journal-title":"CEUR Workshop Proceedings"},{"issue":"3","key":"168_CR25","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1471-2105-9-S3-S6","volume":"9","author":"S Pyysalo","year":"2008","unstructured":"Pyysalo S, Airola A, Heimonen J, Bj\u00f6rne J, Ginter F, Salakoski T. Comparative analysis of five protein-protein interaction corpora. BMC Bioinformatics. 2008; 9(3):6.","journal-title":"BMC Bioinformatics"},{"issue":"7","key":"168_CR26","doi-asserted-by":"publisher","first-page":"p.e1000837","DOI":"10.1371\/journal.pcbi.1000837","volume":"6","author":"D Tikk","year":"2010","unstructured":"Tikk D, Thomas P, Palaga P, Hakenberg J, Leser U. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature. PLoS Comput Biol. 2010; 6(7):p.e1000837. doi:http:\/\/dx.doi.org\/10.1371\/journal.pcbi.1000837.","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"168_CR27","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1023\/A:1009752403260","volume":"1","author":"SL Salzberg","year":"1997","unstructured":"Salzberg SL. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining Knowl Discov. 1997; 1(3):317\u201328.","journal-title":"Data Mining Knowl Discov"},{"key":"168_CR28","volume-title":"Proceedings of the NAACL HLT 2010 Student Research Workshop. HLT-SRWS \u201910","author":"PV Ogren","year":"2010","unstructured":"Ogren PV. Improving syntactic coordination resolution using language modeling. In: Proceedings of the NAACL HLT 2010 Student Research Workshop. HLT-SRWS \u201910. Stroudsburg: Association for Computational Linguistics;2010. p. 1\u20136. http:\/\/dl.acm.org\/citation.cfm?id=1858146.1858147."},{"key":"168_CR29","unstructured":"Farkas R, Vincze V, M\u00f3ra G, Csirik J, Szarvas G. The conll-2010 shared task: learning to detect hedges and their scope in natural language text. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning\u2014Shared Task. Association for Computational Linguistics;2010. p. 1\u201312."},{"key":"168_CR30","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. https:\/\/arxiv.org\/abs\/1301.3781. 2013."},{"key":"168_CR31","doi-asserted-by":"crossref","unstructured":"Saleh I, Moschitti A, Nakov P, M\u00e0rquez L, Joty SR. Semantic Kernels for Semantic Parsing. In: EMNLP.2014. p. 436\u201342.","DOI":"10.3115\/v1\/D14-1050"}],"container-title":["Journal of Biomedical Semantics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-017-0168-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13326-017-0168-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-017-0168-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:23:18Z","timestamp":1790036598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13326-017-0168-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,30]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["168"],"URL":"https:\/\/doi.org\/10.1186\/s13326-017-0168-3","relation":{},"ISSN":["2041-1480"],"issn-type":[{"value":"2041-1480","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,30]]},"assertion":[{"value":"15 February 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not Applicable.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Not Applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":3,"name":"Ethics","label":"Publisher\u2019s Note","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"7"}}