{"id":"https://openalex.org/W3003594370","doi":"https://doi.org/10.1145/3366423.3380197","title":"Complex Factoid Question Answering with a Free-Text Knowledge Graph","display_name":"Complex Factoid Question Answering with a Free-Text Knowledge Graph","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3003594370","doi":"https://doi.org/10.1145/3366423.3380197","mag":"3003594370"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380197","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380197","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chen Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chen Zhao","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chenyan Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chenyan Xiong","raw_affiliation_strings":["Microsoft"],"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xin Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Qian","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jordan Boyd-Graber","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jordan Boyd-Graber","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":1.9199,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.88579202,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1205","last_page":"1216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9973000288009644,"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":0.9890000224113464,"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/question-answering","display_name":"Question answering","score":0.8963000178337097},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6097000241279602},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.524399995803833},{"id":"https://openalex.org/keywords/semantic-memory","display_name":"Semantic memory","score":0.36959999799728394},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.36800000071525574},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.3465999960899353},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3314000070095062}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8963000178337097},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7967000007629395},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6097000241279602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5306000113487244},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.524399995803833},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4860000014305115},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4616999924182892},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.36959999799728394},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C49929091","wikidata":"https://www.wikidata.org/wiki/Q1930471","display_name":"General knowledge","level":2,"score":0.2752000093460083}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3366423.3380197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380197","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.12876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.12876","pdf_url":"https://arxiv.org/pdf/2103.12876","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380197","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1894439495","https://openalex.org/W2016753842","https://openalex.org/W2022166150","https://openalex.org/W2073792299","https://openalex.org/W2094728533","https://openalex.org/W2104583100","https://openalex.org/W2123142779","https://openalex.org/W2126170172","https://openalex.org/W2130237711","https://openalex.org/W2148721079","https://openalex.org/W2171278097","https://openalex.org/W2250539671","https://openalex.org/W2251287417","https://openalex.org/W2889787757","https://openalex.org/W2889789465","https://openalex.org/W2890961898","https://openalex.org/W2912924812","https://openalex.org/W2949134692","https://openalex.org/W2951434086","https://openalex.org/W2951682790","https://openalex.org/W2951862794","https://openalex.org/W2953163841","https://openalex.org/W2953867581","https://openalex.org/W2962718483","https://openalex.org/W2962922117","https://openalex.org/W2962985038","https://openalex.org/W2963159735","https://openalex.org/W2963339397","https://openalex.org/W2963448850","https://openalex.org/W2963662654","https://openalex.org/W2963748441","https://openalex.org/W2963866616","https://openalex.org/W2980401255"],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"delft,":[2],"a":[3,28,65,92],"factoid":[4],"question":[5,17,54,98],"answering":[6,18,99],"system":[7],"which":[8,41,143],"combines":[9],"the":[10,21,51,79,84,124,138,146],"nuance":[11],"and":[12,38,67,116],"depth":[13],"of":[14,24,127,135],"knowledge":[15,30,130],"graph":[16,31,74,140],"approaches":[19],"with":[20,34],"broader":[22],"coverage":[23,69,126],"free-text.":[25],"delft":[26,49,102],"builds":[27],"free-text":[29,80,129,150],"from":[32,122],"Wikipedia,":[33],"entities":[35,42],"as":[36,44,62],"nodes":[37,56,85],"sentences":[39,61],"in":[40],"co-occur":[43],"edges.":[45],"For":[46],"each":[47],"question,":[48],"finds":[50],"subgraph":[52],"linking":[53],"entity":[55],"to":[57],"candidates":[58],"using":[59],"text":[60],"edges,":[63],"creating":[64],"dense":[66],"high":[68,125],"semantic":[70],"graph.":[71],"A":[72],"novel":[73,139],"neural":[75,141],"network":[76,142],"reasons":[77,144],"over":[78],"graph\u2014combining":[81],"evidence":[82],"on":[83,96,145],"via":[86],"information":[87],"along":[88],"edge":[89],"sentences\u2014to":[90],"select":[91],"final":[93],"answer.":[94],"Experiments":[95],"three":[97],"datasets":[100],"show":[101],"can":[103],"answer":[104,114],"entity-rich":[105],"questions":[106],"better":[107],"than":[108,132],"machine":[109],"reading":[110],"based":[111],"models,":[112],"bert-based":[113],"ranking":[115],"memory":[117],"networks.":[118],"delft\u2019s":[119],"advantage":[120],"comes":[121],"both":[123],"its":[128],"graph\u2014more":[131],"double":[133],"that":[134],"dbpedia":[136],"relations\u2014and":[137],"rich":[147],"but":[148],"noisy":[149],"evidence.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-02-07T00:00:00"}
