{"id":"https://openalex.org/W2794309877","doi":"https://doi.org/10.1186/s13326-017-0168-3","title":"Exploiting graph kernels for high performance biomedical relation extraction","display_name":"Exploiting graph kernels for high performance biomedical relation extraction","publication_year":2018,"publication_date":"2018-01-30","ids":{"openalex":"https://openalex.org/W2794309877","doi":"https://doi.org/10.1186/s13326-017-0168-3","mag":"2794309877","pmid":"https://pubmed.ncbi.nlm.nih.gov/29382397"},"language":"en","primary_location":{"id":"doi:10.1186/s13326-017-0168-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13326-017-0168-3","pdf_url":"https://jbiomedsem.biomedcentral.com/counter/pdf/10.1186/s13326-017-0168-3","source":{"id":"https://openalex.org/S172276550","display_name":"Journal of Biomedical Semantics","issn_l":"2041-1480","issn":["2041-1480"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Biomedical Semantics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://jbiomedsem.biomedcentral.com/counter/pdf/10.1186/s13326-017-0168-3","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028681704","display_name":"Nagesh C. Panyam","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Nagesh C. Panyam","raw_affiliation_strings":["School of Computing and Information Systems, University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067214173","display_name":"Karin Verspoor","orcid":"https://orcid.org/0000-0002-8661-1544"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Karin Verspoor","raw_affiliation_strings":["School of Computing and Information Systems, University of Melbourne, Melbourne, Australia. karin.verspoor@unimelb.edu.au","School of Computing and Information Systems, University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia. karin.verspoor@unimelb.edu.au","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078530959","display_name":"Trevor Cohn","orcid":"https://orcid.org/0000-0003-4363-1673"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Trevor Cohn","raw_affiliation_strings":["School of Computing and Information Systems, University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069895484","display_name":"Kotagiri Ramamohanarao","orcid":"https://orcid.org/0000-0003-3304-9268"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kotagiri Ramamohanarao","raw_affiliation_strings":["School of Computing and Information Systems, University of Melbourne, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067214173"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":{"value":1340,"currency":"USD","value_usd":1340},"apc_paid":{"value":1340,"currency":"USD","value_usd":1340},"fwci":2.8013,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.91752924,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"9","issue":"1","first_page":"7","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.010400000028312206,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.00989999994635582,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.8957396745681763},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.892265260219574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8493573665618896},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.6616607308387756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6327370405197144},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.5747924447059631},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5559028387069702},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5395419597625732},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5338882803916931},{"id":"https://openalex.org/keywords/parse-tree","display_name":"Parse tree","score":0.5004701614379883},{"id":"https://openalex.org/keywords/dependency-grammar","display_name":"Dependency grammar","score":0.4685051739215851},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.44535520672798157},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4391346871852875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4366248548030853},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.43282246589660645},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.43039727210998535},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4303666353225708},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.3825508952140808},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.37633994221687317},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3628058433532715},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31997138261795044},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27607545256614685},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.17472785711288452},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11716905236244202}],"concepts":[{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.8957396745681763},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.892265260219574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8493573665618896},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6616607308387756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6327370405197144},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.5747924447059631},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5559028387069702},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5395419597625732},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5338882803916931},{"id":"https://openalex.org/C2781466058","wikidata":"https://www.wikidata.org/wiki/Q627921","display_name":"Parse tree","level":3,"score":0.5004701614379883},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.4685051739215851},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.44535520672798157},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4391346871852875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4366248548030853},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.43282246589660645},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.43039727210998535},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4303666353225708},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.3825508952140808},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.37633994221687317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3628058433532715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31997138261795044},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27607545256614685},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.17472785711288452},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11716905236244202},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D025941","descriptor_name":"Protein Interaction Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D025941","descriptor_name":"Protein Interaction Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D025941","descriptor_name":"Protein Interaction Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.1186/s13326-017-0168-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13326-017-0168-3","pdf_url":"https://jbiomedsem.biomedcentral.com/counter/pdf/10.1186/s13326-017-0168-3","source":{"id":"https://openalex.org/S172276550","display_name":"Journal of Biomedical Semantics","issn_l":"2041-1480","issn":["2041-1480"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Biomedical Semantics","raw_type":"journal-article"},{"id":"pmid:29382397","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29382397","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of biomedical semantics","raw_type":null},{"id":"pmh:oai:jupiter.its.unimelb.edu.au:11343/211679","is_oa":true,"landing_page_url":"http://hdl.handle.net/11343/211679","pdf_url":"http://hdl.handle.net/11343/211679","source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"The University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:371acf6df19f4fafbba8d05ad6dec043","is_oa":true,"landing_page_url":"https://doaj.org/article/371acf6df19f4fafbba8d05ad6dec043","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Biomedical Semantics, Vol 9, Iss 1, Pp 1-11 (2018)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:4715927","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5791373","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:figshare.com:article/27555288","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Exploiting_graph_kernels_for_high_performance_biomedical_relation_extraction/27555288","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s13326-017-0168-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13326-017-0168-3","pdf_url":"https://jbiomedsem.biomedcentral.com/counter/pdf/10.1186/s13326-017-0168-3","source":{"id":"https://openalex.org/S172276550","display_name":"Journal of Biomedical Semantics","issn_l":"2041-1480","issn":["2041-1480"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Biomedical Semantics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320315885","display_name":"Australian Government","ror":"https://ror.org/0314h5y94"},{"id":"https://openalex.org/F4320320974","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2794309877.pdf","grobid_xml":"https://content.openalex.org/works/W2794309877.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W76749362","https://openalex.org/W1587871245","https://openalex.org/W1614298861","https://openalex.org/W1965324211","https://openalex.org/W1987170279","https://openalex.org/W2001668098","https://openalex.org/W2032566933","https://openalex.org/W2048059249","https://openalex.org/W2064299012","https://openalex.org/W2094061585","https://openalex.org/W2103448252","https://openalex.org/W2110376608","https://openalex.org/W2110871096","https://openalex.org/W2112865076","https://openalex.org/W2121844933","https://openalex.org/W2123442489","https://openalex.org/W2124225821","https://openalex.org/W2127080518","https://openalex.org/W2127713198","https://openalex.org/W2138627627","https://openalex.org/W2146328539","https://openalex.org/W2148130205","https://openalex.org/W2163303745","https://openalex.org/W2184729325","https://openalex.org/W2250521169","https://openalex.org/W2251444615","https://openalex.org/W2335791510","https://openalex.org/W2341742436","https://openalex.org/W2522338500","https://openalex.org/W2578456568","https://openalex.org/W2781571882","https://openalex.org/W2790740913","https://openalex.org/W2913379451","https://openalex.org/W2964217331","https://openalex.org/W4239145592","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3013206934","https://openalex.org/W2574115973","https://openalex.org/W2130792056","https://openalex.org/W1590832708","https://openalex.org/W2096302783","https://openalex.org/W2153211825","https://openalex.org/W32136235","https://openalex.org/W3100948281","https://openalex.org/W2147750455","https://openalex.org/W3099811568"],"abstract_inverted_index":{"BACKGROUND:":[0],"Relation":[1,219],"extraction":[2,23,130,154,160,194,298,442],"from":[3,43],"biomedical":[4,158,424],"publications":[5],"is":[6,236,266,299,345,451],"an":[7,156,196,223,228,273],"important":[8,157],"task":[9,143,206,247,374],"in":[10,185,321,352,385,391,445],"the":[11,53,84,110,141,150,167,213,216,253,263,279,288,308,318,322,334,349,398,402,409,413,418,431,438,448,459],"area":[12],"of":[13,16,46,77,115,137,198,215,225,230,275,305],"semantic":[14],"mining":[15],"text.":[17],"Kernel":[18,56,60],"methods":[19,139],"for":[20,67,102,140,181,191,293,307,317,332,423,434],"supervised":[21],"relation":[22,129,159,193,297,366,425,441],"are":[24,383,389],"often":[25],"preferred":[26],"over":[27],"manual":[28],"feature":[29],"engineering":[30],"methods,":[31,234],"when":[32],"classifying":[33,68,103],"highly":[34],"ordered":[35],"structures":[36],"such":[37,51,82,108,436],"as":[38,52,83,109,421,437],"trees":[39,71],"and":[40,57,72,90,144,174,262,290,342,354,404,443],"graphs":[41,76,105],"obtained":[42,267],"syntactic":[44],"parsing":[45],"a":[47,78,116,123,134,175,242,360],"sentence.":[48,79,117],"Tree":[49,55,59],"kernels":[50,81,180,257,260,292,382],"Subset":[54],"Partial":[58],"have":[61,96],"been":[62,97],"shown":[63,98,452],"to":[64,99,149,166,170,177,239,430,453],"be":[65,100,454],"effective":[66,384,422],"constituency":[69],"parse":[70,75,113],"basic":[73],"dependency":[74,112],"Graph":[80,87],"All":[85],"Path":[86],"kernel":[88,95,138,169,270,281,316,351,420,433,450],"(APG)":[89],"Approximate":[91],"Subgraph":[92],"Matching":[93],"(ASM)":[94],"suitable":[101],"general":[104],"with":[106,241,268,427],"cycles,":[107],"enhanced":[111],"graph":[114,179,256,381,399,414],"In":[118,211,302],"this":[119],"work,":[120],"we":[121,416],"present":[122,133],"high":[124,361],"performance":[125,173,265,285,362,429,464],"Chemical-Induced":[126],"Disease":[127,365],"(CID)":[128],"system.":[131],"We":[132,162,358,394],"comparative":[135],"study":[136,148],"CID":[142,192,254,294],"also":[145,395],"extend":[146],"our":[147,303],"Protein-Protein":[151],"Interaction":[152],"(PPI)":[153],"task,":[155,255,310],"task.":[161],"discuss":[163],"novel":[164],"modifications":[165],"ASM":[168,280,289,306,311,344,403,419,460],"boost":[171],"its":[172],"method":[176],"apply":[178],"extracting":[182,386],"relations":[183,387],"expressed":[184,390],"multiple":[186,231,392],"sentences.":[187,393],"RESULTS:":[188],"Our":[189,377],"system":[190,221,245],"attains":[195,272],"F-score":[197,224,274,353],"60%,":[199,276],"without":[200,368],"using":[201,227],"external":[202,370],"knowledge":[203,371],"sources":[204,372],"or":[205,209,373],"specific":[207,248,375],"heuristic":[208],"rules.":[210,251],"comparison,":[212],"state":[214],"art":[217],"Chemical-Disease":[218],"Extraction":[220],"achieves":[222],"56%":[226],"ensemble":[229],"machine":[232],"learning":[233],"which":[235],"then":[237],"boosted":[238],"61%":[240],"rule":[243],"based":[244],"employing":[246,369],"post":[249],"processing":[250],"For":[252],"outperform":[258,408],"tree":[259,410],"substantially,":[261],"best":[264],"APG":[269,291,315,350,405,432,449],"that":[271,380,388,397],"followed":[277],"by":[278,348],"at":[282],"57%.":[283],"The":[284],"difference":[286],"between":[287],"sentence":[295],"level":[296,440],"not":[300],"significant.":[301],"evaluation":[304],"PPI":[309,336],"performed":[312],"better":[313,463],"than":[314,458],"BioInfer":[319,444],"dataset,":[320],"Area":[323],"Under":[324],"Curve":[325],"(AUC)":[326],"measure":[327],"(74%":[328],"vs":[329],"69%).":[330],"However,":[331],"all":[333],"other":[335],"datasets,":[337],"namely":[338,401],"AIMed,":[339],"HPRD50,":[340],"IEPA":[341],"LLL,":[343],"substantially":[346,407],"outperformed":[347],"AUC":[355],"measures.":[356],"CONCLUSIONS:":[357],"demonstrate":[359],"Chemical":[363],"Induced":[364],"extraction,":[367,426],"heuristics.":[376],"work":[378],"shows":[379],"show":[396],"kernels,":[400,406,415],"kernels.":[411],"Among":[412],"showed":[417],"comparable":[428],"datasets":[435],"CID-sentence":[439],"PPI.":[446],"Overall,":[447],"significantly":[455],"more":[456],"accurate":[457],"kernel,":[461],"achieving":[462],"on":[465],"most":[466],"datasets.":[467]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":6}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}