{"id":"https://openalex.org/W2160069741","doi":"https://doi.org/10.1145/1273496.1273644","title":"Dynamic hierarchical Markov random fields and their application to web data extraction","display_name":"Dynamic hierarchical Markov random fields and their application to web data extraction","publication_year":2007,"publication_date":"2007-06-20","ids":{"openalex":"https://openalex.org/W2160069741","doi":"https://doi.org/10.1145/1273496.1273644","mag":"2160069741"},"language":"en","primary_location":{"id":"doi:10.1145/1273496.1273644","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","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/A5100606995","display_name":"Jun Zhu","orcid":"https://orcid.org/0000-0002-6254-2388"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047496977","display_name":"Zaiqing Nie","orcid":"https://orcid.org/0000-0002-1134-2343"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zaiqing Nie","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335187","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-9958-6181"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025631695","display_name":"Ji-Rong Wen","orcid":"https://orcid.org/0000-0002-9777-9676"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji-Rong Wen","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100606995"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.891,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.94537536,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1175","last_page":"1182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/discriminative-model","display_name":"Discriminative model","score":0.773230254650116},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6919957399368286},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6316652894020081},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.5260845422744751},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5243502259254456},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5068078637123108},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.48972538113594055},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4843403100967407},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.469091534614563},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4590226113796234},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.44398438930511475},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.42532944679260254},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4239649772644043},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.42041274905204773},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4063072204589844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40539219975471497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38651490211486816},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36861056089401245},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2589711546897888},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08938857913017273},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0860639214515686}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.773230254650116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919957399368286},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6316652894020081},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.5260845422744751},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5243502259254456},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5068078637123108},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.48972538113594055},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4843403100967407},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.469091534614563},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4590226113796234},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.44398438930511475},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.42532944679260254},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4239649772644043},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.42041274905204773},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4063072204589844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40539219975471497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38651490211486816},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36861056089401245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2589711546897888},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08938857913017273},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0860639214515686},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1273496.1273644","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.91.7349","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.7349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://research.microsoft.com/users/znie/ICML2007_DHMRF.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W66838807","https://openalex.org/W1487588218","https://openalex.org/W1516111018","https://openalex.org/W1565064763","https://openalex.org/W1603152151","https://openalex.org/W1746680969","https://openalex.org/W1903968474","https://openalex.org/W1934017349","https://openalex.org/W1973483159","https://openalex.org/W2051434435","https://openalex.org/W2088600132","https://openalex.org/W2095844239","https://openalex.org/W2098228235","https://openalex.org/W2108344269","https://openalex.org/W2110361616","https://openalex.org/W2116064496","https://openalex.org/W2123579313","https://openalex.org/W2125662744","https://openalex.org/W2128962821","https://openalex.org/W2130451161","https://openalex.org/W2139549194","https://openalex.org/W2143309843","https://openalex.org/W2147880316","https://openalex.org/W2150871663","https://openalex.org/W2151322733","https://openalex.org/W2156358825","https://openalex.org/W2158823144","https://openalex.org/W2166866346","https://openalex.org/W4237840503","https://openalex.org/W4285719527","https://openalex.org/W6602739985","https://openalex.org/W6676152496","https://openalex.org/W6682671827","https://openalex.org/W6729308270"],"related_works":["https://openalex.org/W4300555310","https://openalex.org/W2690111411","https://openalex.org/W4225625430","https://openalex.org/W4377371475","https://openalex.org/W2131810416","https://openalex.org/W2063966378","https://openalex.org/W2115823769","https://openalex.org/W2348419309","https://openalex.org/W2120340025","https://openalex.org/W2104064333"],"abstract_inverted_index":{"Hierarchical":[0,27],"models":[1,11],"have":[2],"been":[3],"extensively":[4],"studied":[5],"in":[6,36,69],"various":[7],"domains.":[8],"However,":[9],"existing":[10],"assume":[12],"fixed":[13],"model":[14,47,90,98],"structures":[15],"or":[16],"incorporate":[17,33],"structural":[18,34],"uncertainty":[19,35],"generatively.":[20],"In":[21],"this":[22],"paper,":[23],"we":[24],"propose":[25],"Dynamic":[26],"Markov":[28],"Random":[29],"Fields":[30],"(DHMRFs)":[31],"to":[32,82,86,99],"a":[37,77,100],"discriminative":[38],"manner.":[39],"DHMRFs":[40,62],"consist":[41],"of":[42],"two":[43],"parts":[44],"--":[45],"structure":[46,91],"and":[48,85,92],"class":[49],"label":[50,93],"model.":[51],"Both":[52],"are":[53],"defined":[54],"as":[55,67],"exponential":[56],"family":[57],"distributions.":[58],"Conditioned":[59],"on":[60,113],"observations,":[61],"relax":[63],"the":[64,88,97,114],"independence":[65],"assumption":[66],"made":[68],"directed":[70],"models.":[71],"As":[72],"exact":[73],"inference":[74],"is":[75,80],"intractable,":[76],"variational":[78],"method":[79],"developed":[81],"learn":[83],"parameters":[84],"find":[87],"MAP":[89],"assignment.":[94],"We":[95],"apply":[96],"real-world":[101],"web":[102],"data":[103],"extraction":[104],"task,":[105],"which":[106],"automatically":[107],"extracts":[108],"product":[109],"items":[110],"for":[111],"sale":[112],"Web.":[115],"The":[116],"results":[117],"show":[118],"promise.":[119]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}