{"id":"https://openalex.org/W175897666","doi":"https://doi.org/10.3115/v1/d14-1044","title":"Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases","display_name":"Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W175897666","doi":"https://doi.org/10.3115/v1/d14-1044","mag":"175897666"},"language":"en","primary_location":{"id":"doi:10.3115/v1/d14-1044","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1044","pdf_url":"https://aclanthology.org/D14-1044.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/D14-1044.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035088083","display_name":"Matt Gardner","orcid":"https://orcid.org/0000-0001-8458-1727"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matt Gardner","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033696194","display_name":"Partha Talukdar","orcid":"https://orcid.org/0000-0001-8825-589X"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["IN","US"],"is_corresponding":false,"raw_author_name":"Partha Talukdar","raw_affiliation_strings":["Research carried out while at the Machine Learning Department, Carnegie Mellon University","Indian Institute of Science Bangalore, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research carried out while at the Machine Learning Department, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Indian Institute of Science Bangalore, Bengaluru, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062893860","display_name":"Jayant Krishnamurthy","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jayant Krishnamurthy","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102921433","display_name":"Tom M. Mitchell","orcid":"https://orcid.org/0000-0001-7373-0301"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tom Mitchell","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, United States","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":178,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"397","last_page":"406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9993000030517578,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9969000220298767,"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/similarity","display_name":"Similarity (geometry)","score":0.6755127310752869},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.6361529231071472},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.617036759853363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5980279445648193},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5142247676849365},{"id":"https://openalex.org/keywords/vector-space","display_name":"Vector space","score":0.49148356914520264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48402780294418335},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32776013016700745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32164788246154785},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13308507204055786},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.08324867486953735}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6755127310752869},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.6361529231071472},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.617036759853363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5980279445648193},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5142247676849365},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.49148356914520264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48402780294418335},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32776013016700745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32164788246154785},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13308507204055786},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.08324867486953735},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":9,"locations":[{"id":"doi:10.3115/v1/d14-1044","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1044","pdf_url":"https://aclanthology.org/D14-1044.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},{"id":"pmh:oai:figshare.com:article/6475790","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Incorporating_Vector_Space_Similarity_in_Random_Walk_Inference_over_Knowledge_Bases/6475790","pdf_url":"https://figshare.com/articles/journal_contribution/Incorporating_Vector_Space_Similarity_in_Random_Walk_Inference_over_Knowledge_Bases/6475790","source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:repository.cmu.edu:machine_learning-1060","is_oa":false,"landing_page_url":"http://repository.cmu.edu/cgi/viewcontent.cgi?article=1060&context=machine_learning","pdf_url":null,"source":{"id":"https://openalex.org/S4306400668","display_name":"Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I74973139","host_organization_name":"Carnegie Mellon University","host_organization_lineage":["https://openalex.org/I74973139"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning Department","raw_type":"text"},{"id":"pmh:doi:10.1184/r1/6475790","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal contribution"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.670.7336","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.670.7336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D14/D14-1044.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.682.460","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.682.460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://emnlp2014.org/papers/pdf/EMNLP2014044.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.696.6453","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.6453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://rtw.ml.cmu.edu/emnlp2014_vector_space_pra/paper.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.719.2155","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.719.2155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://talukdar.net/papers/emnlp2014_vector_space_pra.pdf","raw_type":"text"},{"id":"doi:10.1184/r1/6475790.v1","is_oa":true,"landing_page_url":"https://doi.org/10.1184/r1/6475790.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050927","display_name":"KiltHub Repository","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"doi:10.3115/v1/d14-1044","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1044","pdf_url":"https://aclanthology.org/D14-1044.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5600000023841858,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2378590343","display_name":null,"funder_award_id":"FA8750-13-2-0005","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"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/W175897666.pdf","grobid_xml":"https://content.openalex.org/works/W175897666.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W114118985","https://openalex.org/W1512387364","https://openalex.org/W1788180225","https://openalex.org/W1852412531","https://openalex.org/W1950142954","https://openalex.org/W2016089260","https://openalex.org/W2019054776","https://openalex.org/W2022166150","https://openalex.org/W2029249040","https://openalex.org/W2094728533","https://openalex.org/W2107658650","https://openalex.org/W2125297418","https://openalex.org/W2128407051","https://openalex.org/W2129842875","https://openalex.org/W2133280805","https://openalex.org/W2251939518","https://openalex.org/W2251958415","https://openalex.org/W2952386310","https://openalex.org/W2964266863"],"related_works":["https://openalex.org/W1597743604","https://openalex.org/W2038723108","https://openalex.org/W2033630974","https://openalex.org/W2325737604","https://openalex.org/W4245069437","https://openalex.org/W2042537860","https://openalex.org/W2382333285","https://openalex.org/W4297449606","https://openalex.org/W2055243143","https://openalex.org/W2084194111"],"abstract_inverted_index":{"Much":[0],"work":[1,43,163],"in":[2,40,103,126,141,168,178],"recent":[3],"years":[4],"has":[5,44],"gone":[6],"into":[7,90,115],"the":[8,34,67,122,169,179],"construction":[9],"of":[10,36,50,69,171,181],"large":[11,52,71],"knowledge":[12],"bases":[13],"(KBs),":[14],"such":[15,70],"as":[16],"Freebase,":[17],"DBPedia,":[18],"NELL,":[19],"and":[20,87,143,177],"YAGO.":[21],"While":[22],"these":[23],"KBs":[24],"are":[25,29],"very":[26,31],"large,":[27],"they":[28],"still":[30],"incomplete,":[32],"necessitating":[33],"use":[35,49,68],"inference":[37,59,118,140],"to":[38,47,55,66,73,110,133],"fill":[39],"gaps.":[41],"Prior":[42],"shown":[45],"how":[46,109],"make":[48],"a":[51,80,91],"text":[53,89],"corpus":[54],"augment":[56,74],"random":[57,116],"walk":[58,117],"over":[60,119],"KBs.":[61],"We":[62],"present":[63,79],"two":[64,152],"improvements":[65],"corpora":[72],"KB":[75,85,165],"inference.":[76],"First,":[77],"we":[78,107,155],"new":[81],"technique":[82],"for":[83],"combining":[84],"relations":[86,150],"surface":[88,128],"single":[92],"graph":[93],"representation":[94],"that":[95,157],"is":[96],"much":[97],"more":[98],"compact":[99],"than":[100],"graphs":[101],"used":[102],"prior":[104,162],"work.":[105],"Second,":[106],"describe":[108],"incorporate":[111],"vector":[112],"space":[113],"similarity":[114,136],"KBs,":[120,154],"reducing":[121],"feature":[123],"sparsity":[124],"inherent":[125],"using":[127],"text.":[129],"This":[130],"allows":[131],"us":[132],"combine":[134],"distributional":[135],"with":[137],"symbolic":[138],"logical":[139],"novel":[142],"effective":[144],"ways.":[145],"With":[146],"experiments":[147],"on":[148,164],"many":[149],"from":[151],"separate":[153],"show":[156],"our":[158,173],"methods":[159,174],"significantly":[160],"outperform":[161],"inference,":[166],"both":[167],"size":[170],"problem":[172],"can":[175],"handle":[176],"quality":[180],"predictions":[182],"made.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":15},{"year":2016,"cited_by_count":14},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
