{"id":"https://openalex.org/W2109436069","doi":"https://doi.org/10.1145/2390068.2390082","title":"High precision rule based PPI extraction and per-pair basis performance evaluation","display_name":"High precision rule based PPI extraction and per-pair basis performance evaluation","publication_year":2012,"publication_date":"2012-10-29","ids":{"openalex":"https://openalex.org/W2109436069","doi":"https://doi.org/10.1145/2390068.2390082","mag":"2109436069"},"language":"en","primary_location":{"id":"doi:10.1145/2390068.2390082","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2390068.2390082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078925783","display_name":"Junkyu Lee","orcid":"https://orcid.org/0000-0003-1985-5116"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Junkyu Lee","raw_affiliation_strings":["Korea University, Seoul, South Korea","Korea university - Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Korea university - Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072695717","display_name":"Seongsoon Kim","orcid":"https://orcid.org/0000-0002-9872-0430"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongsoon Kim","raw_affiliation_strings":["Korea University, Seoul, South Korea","Korea university - Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Korea university - Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005418049","display_name":"Sunwon Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunwon Lee","raw_affiliation_strings":["Korea University, Seoul, South Korea","Korea university - Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Korea university - Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004629166","display_name":"Kyubum Lee","orcid":"https://orcid.org/0000-0003-2015-3939"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyubum Lee","raw_affiliation_strings":["Korea University, Seoul, South Korea","Korea university - Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Korea university - Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076917278","display_name":"Jaewoo Kang","orcid":"https://orcid.org/0000-0001-6798-9106"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaewoo Kang","raw_affiliation_strings":["Korea University, Seoul, South Korea","Korea university - Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Korea university - Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5078925783"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":1.7125,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.86669687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"69","last_page":"76"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9997000098228455,"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":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.7524300813674927},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.7212127447128296},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6769090294837952},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6582772135734558},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.6359164714813232},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5948797464370728},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5309861302375793},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.48622193932533264},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4828498959541321},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.4579225480556488},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4565867781639099},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.45315030217170715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37755098938941956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36215394735336304},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12179094552993774},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0923546850681305}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524300813674927},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.7212127447128296},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6769090294837952},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6582772135734558},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.6359164714813232},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5948797464370728},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5309861302375793},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.48622193932533264},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4828498959541321},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.4579225480556488},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4565867781639099},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.45315030217170715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37755098938941956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36215394735336304},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12179094552993774},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0923546850681305},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2390068.2390082","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2390068.2390082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W151685798","https://openalex.org/W1850865022","https://openalex.org/W1980127292","https://openalex.org/W1986704581","https://openalex.org/W1990794790","https://openalex.org/W1996787131","https://openalex.org/W2004917988","https://openalex.org/W2016928406","https://openalex.org/W2048059249","https://openalex.org/W2063389953","https://openalex.org/W2077997913","https://openalex.org/W2097960255","https://openalex.org/W2104012281","https://openalex.org/W2109070394","https://openalex.org/W2112454182","https://openalex.org/W2121844933","https://openalex.org/W2123112337","https://openalex.org/W2127713198","https://openalex.org/W2132288458","https://openalex.org/W2138627627","https://openalex.org/W2148146609","https://openalex.org/W2152698860","https://openalex.org/W2157628440","https://openalex.org/W2163362093","https://openalex.org/W2166111585","https://openalex.org/W2167072947","https://openalex.org/W2169974160","https://openalex.org/W2968573267","https://openalex.org/W3007535931","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2358294942","https://openalex.org/W4361804730","https://openalex.org/W2142113611","https://openalex.org/W2334467465","https://openalex.org/W2018387840","https://openalex.org/W2087870008","https://openalex.org/W2045629210","https://openalex.org/W2162534555","https://openalex.org/W2752178021","https://openalex.org/W2135622761"],"abstract_inverted_index":{"Virtually":[0],"all":[1],"current":[2,49],"PPI":[3,37,69,98],"extraction":[4,38,70,79,99],"studies":[5],"focus":[6],"on":[7,15,108],"improving":[8],"F-score,":[9],"aiming":[10],"to":[11,60],"balance":[12],"the":[13,48,109],"performance":[14,52,88],"both":[16],"precision":[17,36,78],"and":[18,104],"recall.":[19],"However,":[20],"in":[21,94],"many":[22],"realistic":[23],"scenarios":[24],"involving":[25],"large":[26],"corpora,":[27],"one":[28],"can":[29],"benefit":[30],"more":[31,92],"from":[32],"an":[33],"extremely":[34],"high":[35],"tool":[39],"than":[40],"a":[41,66,74,84],"high-recall":[42],"counterpart.":[43],"We":[44,81],"also":[45,82],"argue":[46],"that":[47],"\"per-instance\"":[50],"basis":[51,87],"evaluation":[53],"method":[54,71,100],"should":[55],"be":[56],"revisited.":[57],"In":[58],"order":[59],"address":[61],"these":[62],"problems,":[63],"we":[64],"introduce":[65],"new":[67,85],"rule-based":[68],"equipped":[72],"with":[73],"set":[75],"of":[76],"ultra-high":[77],"rules.":[80],"propose":[83],"\"per-pair\"":[86],"metric,":[89],"which":[90],"is":[91],"pragmatic":[93],"practice.":[95],"The":[96],"proposed":[97],"achieves":[101],"95-96%":[102],"per-pair":[103],"94-97%":[105],"per-instance":[106],"precisions":[107],"AIMed":[110],"benchmark":[111],"corpus.":[112]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
