{"id":"https://openalex.org/W4200566067","doi":"https://doi.org/10.1007/s10994-021-06069-5","title":"A taxonomy of weight learning methods for statistical relational learning","display_name":"A taxonomy of weight learning methods for statistical relational learning","publication_year":2021,"publication_date":"2021-12-13","ids":{"openalex":"https://openalex.org/W4200566067","doi":"https://doi.org/10.1007/s10994-021-06069-5"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-021-06069-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06069-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06069-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06069-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017449169","display_name":"Sriram Srinivasan","orcid":"https://orcid.org/0000-0003-0085-309X"},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sriram Srinivasan","raw_affiliation_strings":["UC Santa Cruz (CSE), Santa Cruz, CA, USA"],"affiliations":[{"raw_affiliation_string":"UC Santa Cruz (CSE), Santa Cruz, CA, USA","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067653239","display_name":"Charles Dickens","orcid":null},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Dickens","raw_affiliation_strings":["UC Santa Cruz (CSE), Santa Cruz, CA, USA"],"affiliations":[{"raw_affiliation_string":"UC Santa Cruz (CSE), Santa Cruz, CA, USA","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046658632","display_name":"Eriq Augustine","orcid":null},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eriq Augustine","raw_affiliation_strings":["UC Santa Cruz (CSE), Santa Cruz, CA, USA"],"affiliations":[{"raw_affiliation_string":"UC Santa Cruz (CSE), Santa Cruz, CA, USA","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053667504","display_name":"Golnoosh Farnadi","orcid":"https://orcid.org/0000-0003-4504-8668"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Golnoosh Farnadi","raw_affiliation_strings":["Mila, Universite de Montreal, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Mila, Universite de Montreal, Montreal, QC, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086169451","display_name":"Lise Getoor","orcid":null},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lise Getoor","raw_affiliation_strings":["UC Santa Cruz (CSE), Santa Cruz, CA, USA"],"affiliations":[{"raw_affiliation_string":"UC Santa Cruz (CSE), Santa Cruz, CA, USA","institution_ids":["https://openalex.org/I185103710"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017449169"],"corresponding_institution_ids":["https://openalex.org/I185103710"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.2797,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65927711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"111","issue":"8","first_page":"2799","last_page":"2838"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9988999962806702,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9988999962806702,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9976999759674072,"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/T10028","display_name":"Topic Modeling","score":0.9968000054359436,"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/statistical-relational-learning","display_name":"Statistical relational learning","score":0.6784729957580566},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6208014488220215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5957176685333252},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5897276401519775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5716956257820129},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5015730857849121},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4234927296638489},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.357828289270401},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35275596380233765},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.2823403477668762}],"concepts":[{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.6784729957580566},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6208014488220215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5957176685333252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5897276401519775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5716956257820129},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5015730857849121},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4234927296638489},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.357828289270401},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35275596380233765},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2823403477668762},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-021-06069-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06069-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06069-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-021-06069-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06069-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06069-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2854562603","display_name":null,"funder_award_id":"CCF-1740850","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G309424589","display_name":null,"funder_award_id":"IVADO","funder_id":"https://openalex.org/F4320326644","funder_display_name":"Canada First Research Excellence Fund"},{"id":"https://openalex.org/G3667867886","display_name":"TRIPODS: Towards a Unified Theory of Structure, Incompleteness & Uncertainty in Heterogeneous Graphs","funder_award_id":"1740850","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G468413096","display_name":null,"funder_award_id":"CCF-2023495","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4807382101","display_name":null,"funder_award_id":"2023495","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5401686217","display_name":null,"funder_award_id":"(CFREF)","funder_id":"https://openalex.org/F4320326644","funder_display_name":"Canada First Research Excellence Fund"},{"id":"https://openalex.org/G6706293736","display_name":"III: Medium: Collaborative Research: A Unified and Declarative Approach to Causal Analysis for Big Data","funder_award_id":"1703331","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8787547879","display_name":null,"funder_award_id":"IIS-1703331","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320326644","display_name":"Canada First Research Excellence Fund","ror":null},{"id":"https://openalex.org/F4320330273","display_name":"Institut de Valorisation des Donn\u00e9es","ror":null},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200566067.pdf","grobid_xml":"https://content.openalex.org/works/W4200566067.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W13772370","https://openalex.org/W121649270","https://openalex.org/W121830907","https://openalex.org/W137706053","https://openalex.org/W203049729","https://openalex.org/W1480330138","https://openalex.org/W1483202606","https://openalex.org/W1490768968","https://openalex.org/W1501299699","https://openalex.org/W1535439311","https://openalex.org/W1567012231","https://openalex.org/W1585529040","https://openalex.org/W1824971879","https://openalex.org/W1835243625","https://openalex.org/W1877183374","https://openalex.org/W1959840315","https://openalex.org/W1965583749","https://openalex.org/W1977970897","https://openalex.org/W2001828104","https://openalex.org/W2008652694","https://openalex.org/W2018345399","https://openalex.org/W2031248101","https://openalex.org/W2039522160","https://openalex.org/W2061144551","https://openalex.org/W2090761873","https://openalex.org/W2096649264","https://openalex.org/W2102506621","https://openalex.org/W2112576450","https://openalex.org/W2117354486","https://openalex.org/W2121075864","https://openalex.org/W2131241448","https://openalex.org/W2136801552","https://openalex.org/W2137213923","https://openalex.org/W2139193890","https://openalex.org/W2144159061","https://openalex.org/W2144429462","https://openalex.org/W2150475393","https://openalex.org/W2164278908","https://openalex.org/W2166566250","https://openalex.org/W2169003314","https://openalex.org/W2192203593","https://openalex.org/W2250322043","https://openalex.org/W2283350629","https://openalex.org/W2403004111","https://openalex.org/W2419658023","https://openalex.org/W2425398464","https://openalex.org/W2469049024","https://openalex.org/W2513410637","https://openalex.org/W2522334551","https://openalex.org/W2527419728","https://openalex.org/W2547239171","https://openalex.org/W2556522401","https://openalex.org/W2565166462","https://openalex.org/W2606119046","https://openalex.org/W2618311116","https://openalex.org/W2741775618","https://openalex.org/W2775160451","https://openalex.org/W2782838038","https://openalex.org/W2798536157","https://openalex.org/W2904554615","https://openalex.org/W2914108316","https://openalex.org/W2950865781","https://openalex.org/W2951665052","https://openalex.org/W2952047749","https://openalex.org/W2986978058","https://openalex.org/W2996971519","https://openalex.org/W2997788840","https://openalex.org/W3094296680","https://openalex.org/W3144619878","https://openalex.org/W4211049957","https://openalex.org/W4253934678","https://openalex.org/W4300602297","https://openalex.org/W6601201599","https://openalex.org/W6601926695","https://openalex.org/W6630626424","https://openalex.org/W6674385629","https://openalex.org/W6675204935","https://openalex.org/W6676179485","https://openalex.org/W6685643784"],"related_works":["https://openalex.org/W4360764288","https://openalex.org/W44619616","https://openalex.org/W2426860188","https://openalex.org/W1585375882","https://openalex.org/W2104279947","https://openalex.org/W2553103903","https://openalex.org/W2752254334","https://openalex.org/W3090021550","https://openalex.org/W2894777571","https://openalex.org/W4287865155"],"abstract_inverted_index":{"Abstract":[0],"Statistical":[1],"relational":[2,14],"learning":[3,40,87,212],"(SRL)":[4],"frameworks":[5,91],"are":[6,33],"effective":[7],"at":[8],"defining":[9],"probabilistic":[10,30,185],"models":[11],"over":[12],"complex":[13],"data.":[15,37],"They":[16],"often":[17],"use":[18],"weighted":[19],"first-order":[20],"logical":[21],"rules":[22,28],"where":[23],"the":[24,27,125,136,141,145,153,166,247],"weights":[25,49,95],"of":[26,48,54,84,124,155,162,168,236,249],"govern":[29],"interactions":[31],"and":[32,139,184,196,226,255,266],"usually":[34],"learned":[35],"from":[36],"Existing":[38],"weight":[39,86,127,137,147,170,211],"approaches":[41,88,175,222,225],"typically":[42],"attempt":[43],"to":[44,63,114,151,229,245,252],"learn":[45],"a":[46,67,82,97,110,230,234],"set":[47],"that":[50,92,119,131,218,260],"maximizes":[51],"some":[52],"function":[53],"data":[55],"likelihood;":[56],"however,":[57],"this":[58,78],"does":[59],"not":[60],"always":[61],"translate":[62],"optimal":[64],"performance":[65,100,237],"on":[66,96,176,192,200],"desired":[68],"domain":[69,99],"metric,":[70],"such":[71],"as":[72,115],"accuracy":[73],"or":[74],"F1":[75],"score.":[76],"In":[77,149],"paper,":[79],"we":[80,108,157,240],"introduce":[81,109,159],"taxonomy":[83],"search-based":[85,106,221],"for":[89],"SRL":[90,179],"directly":[93],"optimize":[94],"chosen":[98],"metric.":[101],"To":[102],"effectively":[103],"apply":[104],"these":[105,174],"approaches,":[107],"novel":[111],"projection,":[112],"referred":[113],"scaled":[116],"space":[117,138],"(SS),":[118],"is":[120,263],"an":[121,160,242],"accurate":[122,265],"representation":[123],"true":[126],"space.":[128],"We":[129,172,188,204],"show":[130,217],"SS":[132,163],"removes":[133],"redundancies":[134],"in":[135],"captures":[140],"semantic":[142],"distance":[143],"between":[144],"possible":[146],"configurations.":[148,171],"order":[150],"improve":[152],"efficiency":[154],"search,":[156],"also":[158,205],"approximation":[161],"which":[164],"simplifies":[165],"process":[167],"sampling":[169],"demonstrate":[173],"two":[177,201],"state-of-the-art":[178],"frameworks:":[180],"Markov":[181],"logic":[182],"networks":[183],"soft":[186],"logic.":[187],"perform":[189,241],"empirical":[190],"evaluation":[191,244],"five":[193],"real-world":[194],"datasets":[195],"evaluate":[197],"them":[198,207],"each":[199],"different":[202,253],"metrics.":[203,238],"compare":[206],"against":[208],"four":[209],"other":[210],"approaches.":[213],"Our":[214],"experimental":[215],"results":[216,258],"our":[219,250,261],"proposed":[220],"outperform":[223],"likelihood-based":[224],"yield":[227],"up":[228],"10%":[231],"improvement":[232],"across":[233],"variety":[235],"Further,":[239],"extensive":[243],"measure":[246],"robustness":[248],"approach":[251,262],"initializations":[254],"hyperparameters.":[256],"The":[257],"indicate":[259],"both":[264],"robust.":[267]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-16T09:10:04.655348","created_date":"2025-10-10T00:00:00"}