{"id":"https://openalex.org/W7131619484","doi":"https://doi.org/10.1109/ickg66886.2025.00021","title":"Quantifying Informativeness in Knowledge Graph-Augmented In-Context Learning for Multiple Choice Query Answering","display_name":"Quantifying Informativeness in Knowledge Graph-Augmented In-Context Learning for Multiple Choice Query Answering","publication_year":2025,"publication_date":"2025-11-13","ids":{"openalex":"https://openalex.org/W7131619484","doi":"https://doi.org/10.1109/ickg66886.2025.00021"},"language":null,"primary_location":{"id":"doi:10.1109/ickg66886.2025.00021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickg66886.2025.00021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Knowledge Graph (ICKG)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5126916292","display_name":"Maryam Ghanbari","orcid":null},"institutions":[{"id":"https://openalex.org/I186803428","display_name":"Brock University","ror":"https://ror.org/056am2717","country_code":"CA","type":"education","lineage":["https://openalex.org/I186803428"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Maryam Ghanbari","raw_affiliation_strings":["Brock University,Computer Science Department,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brock University,Computer Science Department,Canada","institution_ids":["https://openalex.org/I186803428"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078237873","display_name":"Renata Dividino","orcid":null},"institutions":[{"id":"https://openalex.org/I186803428","display_name":"Brock University","ror":"https://ror.org/056am2717","country_code":"CA","type":"education","lineage":["https://openalex.org/I186803428"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Renata Dividino","raw_affiliation_strings":["Brock University,Computer Science Department,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brock University,Computer Science Department,Canada","institution_ids":["https://openalex.org/I186803428"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I186803428"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"106","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8996000289916992,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8996000289916992,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.017999999225139618,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.017899999395012856,"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/leverage","display_name":"Leverage (statistics)","score":0.649399995803833},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5604000091552734},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5468000173568726},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4765999913215637},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.42010000348091125},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4124000072479248},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.39980000257492065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487000226974487},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.649399995803833},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5604000091552734},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4765999913215637},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3675000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36059999465942383},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34279999136924744},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C2777877512","wikidata":"https://www.wikidata.org/wiki/Q1116097","display_name":"Common ground","level":2,"score":0.26739999651908875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickg66886.2025.00021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickg66886.2025.00021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Knowledge Graph (ICKG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2963448850","https://openalex.org/W3034273250","https://openalex.org/W3034862985","https://openalex.org/W3105082862","https://openalex.org/W3156128319","https://openalex.org/W3172335055","https://openalex.org/W3185341429","https://openalex.org/W4385571693","https://openalex.org/W4385572149","https://openalex.org/W4402671243","https://openalex.org/W4404782964"],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"face":[4],"significant":[5],"challenges":[6],"when":[7],"attempting":[8],"to":[9,31,77,98,125],"utilize":[10],"knowledge":[11,34,44,104,232],"not":[12,92],"encoded":[13],"during":[14],"pre-training.":[15],"In-context":[16],"learning":[17],"(ICL)":[18],"addresses":[19],"this":[20,226],"limitation":[21],"by":[22],"embedding":[23],"relevant":[24,97],"information":[25,47,123],"directly":[26],"into":[27,48,89,166],"prompts,":[28,49,167],"enabling":[29],"LLMs":[30],"leverage":[32],"external":[33],"without":[35],"updating":[36],"their":[37,171],"parameters.":[38],"Recent":[39],"advances":[40],"have":[41,193],"explored":[42],"integrating":[43],"graph":[45],"(KG)":[46],"taking":[50],"advantage":[51],"of":[52,58,218,230,240],"the":[53,99,153,214,238],"structured":[54],"and":[55,60,86,101,149,168,191,216,236],"factual":[56],"representations":[57],"entities":[59],"relationships":[61],"that":[62,142,198],"KGs":[63],"provide.":[64],"A":[65],"common":[66],"approach":[67],"involves":[68],"identifying":[69],"key":[70,145],"concepts":[71,127,146],"in":[72,152,207,234],"a":[73,106,139],"task,":[74,100],"grounding":[75],"them":[76,88,151],"KG":[78,201,220],"nodes,":[79],"extracting":[80],"ego-subgraphs":[81,158],"centered":[82],"on":[83,131,173],"these":[84,164],"concepts,":[85],"incorporating":[87],"prompts.":[90],"However,":[91],"all":[93],"subgraphs":[94,113,165,184,202,221],"are":[95],"equally":[96],"selecting":[102],"appropriate":[103],"remains":[105],"critical":[107],"challenge,":[108],"as":[109],"irrelevant":[110],"or":[111],"noisy":[112],"can":[114,203],"reduce":[115],"model":[116,223],"accuracy.":[117],"This":[118,246],"study":[119],"investigates":[120],"how":[121,213],"KG-based":[122,231],"related":[124],"task-specific":[126],"influences":[128],"LLM":[129,174],"performance":[130],"multiple-choice":[132],"question":[133],"answering":[134],"(MCQA)":[135],"tasks.":[136],"We":[137,196],"implement":[138],"four-stage":[140],"pipeline":[141],"(1)":[143],"identifies":[144],"from":[147],"questions":[148],"grounds":[150],"KG,":[154],"(2)":[155],"extracts":[156],"individual":[157],"for":[159],"each":[160],"concept,":[161],"(3)":[162],"integrates":[163],"(4)":[169],"evaluates":[170],"impact":[172],"reasoning":[175],"via":[176],"probability":[177],"scoring.":[178],"Our":[179],"analysis":[180],"highlights":[181],"which":[182,187,192],"concept-specific":[183],"enhance":[185],"performance,":[186],"introduce":[188],"misleading":[189],"information,":[190],"neutral":[194],"effects.":[195],"show":[197],"carefully":[199],"selected":[200,219],"substantially":[204],"outperform":[205],"others":[206],"semantic":[208],"relevance.":[209],"Furthermore,":[210],"we":[211],"examine":[212],"size":[215],"connectivity":[217],"influence":[222],"performance.":[224],"Overall,":[225],"work":[227],"deepens":[228],"understanding":[229],"selection":[233],"ICL":[235],"informs":[237],"design":[239],"more":[241],"effective,":[242],"targeted":[243],"prompting":[244],"strategies.":[245],"project's":[247],"source":[248],"code":[249],"is":[250],"publicly":[251],"available":[252],"at":[253],"https://github.com/maryam-ghanbari/InformativenessOfKGs.":[254]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-27T00:00:00"}