{"id":"https://openalex.org/W4415082293","doi":"https://doi.org/10.1142/s1793351x25450023","title":"Extending TriRAG for Advancing Retrieval-Augmented Generation Method with Triple-Based Knowledge Graphs for Improved Question Answering","display_name":"Extending TriRAG for Advancing Retrieval-Augmented Generation Method with Triple-Based Knowledge Graphs for Improved Question Answering","publication_year":2025,"publication_date":"2025-10-11","ids":{"openalex":"https://openalex.org/W4415082293","doi":"https://doi.org/10.1142/s1793351x25450023"},"language":"en","primary_location":{"id":"doi:10.1142/s1793351x25450023","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1793351x25450023","pdf_url":null,"source":{"id":"https://openalex.org/S4210201727","display_name":"International Journal of Semantic Computing","issn_l":"1793-7108","issn":["1793-7108","1793-351X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Semantic Computing","raw_type":"journal-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":null,"display_name":"Hongzhi Zhang","orcid":"https://orcid.org/0009-0005-8772-6254"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hongzhi Zhang","raw_affiliation_strings":["School of Information Technology, Carleton University, Ottawa, ON, Canada"],"raw_orcid":"https://orcid.org/0009-0005-8772-6254","affiliations":[{"raw_affiliation_string":"School of Information Technology, Carleton University, Ottawa, ON, Canada","institution_ids":["https://openalex.org/I67031392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087753797","display_name":"M. Omair Shafiq","orcid":"https://orcid.org/0000-0002-1859-8296"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Shafiq","raw_affiliation_strings":["School of Information Technology, Carleton University, Ottawa, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-1859-8296","affiliations":[{"raw_affiliation_string":"School of Information Technology, Carleton University, Ottawa, ON, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67031392"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10732825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":"04","first_page":"547","last_page":"567"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.940500020980835,"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":0.940500020980835,"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":0.8026000261306763},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6503000259399414},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46869999170303345},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3693999946117401},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3659999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8593999743461609},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8026000261306763},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6503000259399414},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5019000172615051},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s1793351x25450023","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1793351x25450023","pdf_url":null,"source":{"id":"https://openalex.org/S4210201727","display_name":"International Journal of Semantic Computing","issn_l":"1793-7108","issn":["1793-7108","1793-351X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Semantic Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2746097825","https://openalex.org/W3099700870","https://openalex.org/W4282936652","https://openalex.org/W4313013044","https://openalex.org/W4317898419","https://openalex.org/W4320914959","https://openalex.org/W4391274633","https://openalex.org/W4392186815","https://openalex.org/W4401042753","https://openalex.org/W4404782436","https://openalex.org/W4406477144","https://openalex.org/W4411447528"],"related_works":[],"abstract_inverted_index":{"While":[0],"large":[1],"language":[2],"models":[3],"exhibit":[4],"broad":[5],"capabilities,":[6],"they":[7],"often":[8],"require":[9],"supplementation":[10],"for":[11,82],"optimal":[12],"performance":[13,57],"on":[14,60],"domain-specific":[15],"tasks.":[16,63,121],"To":[17],"address":[18],"this,":[19],"we":[20],"built":[21],"TriRAG,":[22],"an":[23,33],"innovative":[24],"enhancement":[25],"to":[26,165],"traditional":[27,135],"Retrieval-Augmented":[28],"Generation.":[29],"This":[30,110],"paper":[31],"presents":[32],"extended":[34],"version":[35],"of":[36,48,58,96,115],"our":[37,154],"earlier":[38],"TriRAG":[39,41,102,124],"work.":[40],"integrates":[42],"a":[43,83],"structured":[44],"knowledge":[45],"graph":[46],"composed":[47],"semantic":[49],"triples":[50,81],"derived":[51],"from":[52],"text,":[53],"significantly":[54],"improving":[55],"the":[56,78,92,97,113,126],"LLMs":[59],"multiple-choice":[61,119],"question-answering":[62,120],"Our":[64],"method":[65],"dynamically":[66],"converts":[67],"relevant":[68],"text":[69],"into":[70,74],"triples,":[71],"embeds":[72],"them":[73],"vectors,":[75],"and":[76,106,144,149,161],"retrieves":[77],"most":[79],"useful":[80],"given":[84],"question":[85],"by":[86],"calculating":[87],"vector":[88],"similarities.":[89],"By":[90],"having":[91],"triple-based":[93,155],"approach":[94],"instead":[95],"conventional":[98],"text-based":[99],"retrieval":[100,159],"approach,":[101],"enables":[103],"more":[104],"precise":[105],"efficient":[107],"information":[108],"retrieval.":[109],"directly":[111],"enhances":[112,157],"accuracy":[114,160],"LLM-generated":[116],"responses":[117],"in":[118],"We":[122],"evaluate":[123],"using":[125],"Textbook":[127],"Question":[128],"Answering":[129],"dataset,":[130],"demonstrating":[131],"consistent":[132],"improvements":[133],"over":[134],"RAG":[136],"methods":[137],"across":[138],"leading":[139,164],"LLMs,":[140],"including":[141],"Gemma,":[142],"Llama,":[143],"ChatGPT":[145],"variants.":[146],"Experimental":[147],"results":[148],"ablation":[150],"studies":[151],"confirm":[152],"that":[153],"system":[156],"both":[158],"processing":[162],"efficiency,":[163],"better":[166],"overall":[167],"model":[168],"performance.":[169]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-12T00:00:00"}
