{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:37:13Z","timestamp":1772933833567,"version":"3.50.1"},"reference-count":56,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11400992","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"5662-5670","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of GraphRAG Strategies for Efficient Information Retrieval"],"prefix":"10.1109","author":[{"given":"Asma","family":"Houimli","sequence":"first","affiliation":[{"name":"Euranova,Tunis,Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaineb","family":"Gabsi","sequence":"additional","affiliation":[{"name":"Euranova,Tunis,Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabri","family":"Skhiri","sequence":"additional","affiliation":[{"name":"Euranova,Brussels,Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"A survey on large language models: Applications, challenges, limitations, and practical usage","author":"Hadi","journal-title":"Authorea Preprints, 2023"},{"key":"ref2","article-title":"Gpt-4 technical report","author":"Achiam","year":"2023","journal-title":"arxiv preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MIPRO60963.2024.10569238"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.11"},{"key":"ref5","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref6","author":"Yan","year":"2024","journal-title":"Corrective retrieval augmented generation"},{"key":"ref7","article-title":"Evaluating rag-fusion with ragelo: an automated elo-based framework","author":"Rackauckas","year":"2024","journal-title":"arxiv preprint"},{"key":"ref8","article-title":"From local to global: A graph rag approach to query-focused summarization","author":"Edge","year":"2024","journal-title":"arxiv preprint"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671470"},{"key":"ref10","article-title":"Retrieval augmented generation with graphs (graphrag)","volume-title":"arxiv preprint","author":"Han","year":"2024"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4224"},{"key":"ref13","article-title":"Ares: An automated evaluation framework for retrieval-augmented generation systems","author":"Saad-Falcon","year":"2023","journal-title":"arxiv preprint"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.26726"},{"key":"ref15","article-title":"Kg-rag: Bridging the gap between knowledge and creativity","author":"Sanmartin","year":"2024","journal-title":"arxiv preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.558"},{"key":"ref17","article-title":"Graph rag-tool fusion","author":"Lumer","year":"2025","journal-title":"arxiv preprint"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29889"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.401"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.631"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.99"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.580"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"ref24","first-page":"37 309","article-title":"Deep bidirectional language-knowledge graph pretraining","volume":"35","author":"Yasunaga","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref25","article-title":"Greaselm: Graph reasoning enhanced language models for question answering","author":"Zhang","year":"2022","journal-title":"arxiv preprint"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"ref27","article-title":"Causal reasoning in large language models: A knowledge graph approach","author":"Kim","year":"2024","journal-title":"arxiv preprint"},{"key":"ref28","article-title":"Mitigating hallucinations in large language models via self-refinement-enhanced knowledge retrieval","author":"Niu","year":"2024","journal-title":"arxiv preprint"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29875"},{"key":"ref30","article-title":"Text2cypher: Bridging natural language and graph databases","author":"Ozsoy","year":"2024","journal-title":"arxiv preprint"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01527-8"},{"key":"ref32","article-title":"Reasoning on graphs: Faith-ful and interpretable large language model reasoning","author":"Luo","year":"2023","journal-title":"arxiv preprint"},{"key":"ref33","article-title":"Counter-intuitive: Large language models can better understand knowledge graphs than we thought","author":"Dai","year":"2024","journal-title":"CoRR"},{"key":"ref34","article-title":"Gentkg: Generative forecasting on temporal knowledge graph with large language models","author":"Liao","year":"2023","journal-title":"arxiv preprint"},{"key":"ref35","article-title":"keqing: knowledge-based question answering is a nature chain-of-thought mentor of llm","author":"Wang","year":"2023","journal-title":"arxiv preprint"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3007669.3007722"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W13-5001"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2008.4761190"},{"key":"ref39","article-title":"Gnn-rag: Graph neural retrieval for large language model reasoning","author":"Mavromatis","year":"2024","journal-title":"arxiv preprint"},{"key":"ref40","article-title":"Explore then determine: A gnn-llm synergy framework for reasoning over knowledge graph","author":"Liu","year":"2024","journal-title":"arXiv e-prints"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.115"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3648324"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3698590"},{"key":"ref44","article-title":"Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph","author":"Sun","year":"2023","journal-title":"arxiv preprint"},{"key":"ref45","article-title":"Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering","author":"Wu","year":"2023","journal-title":"arxiv preprint"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.5220\/0013065700003838"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.eacl-demo.16"},{"key":"ref48","article-title":"Rag vs. graphrag: A systematic evaluation and key insights","author":"Han","year":"2025","journal-title":"arxiv preprint"},{"key":"ref49","article-title":"Graphrag-bench: Challenging domain-specific reasoning for evaluating graph retrieval-augmented generation","author":"Xiao","year":"2025","journal-title":"arxiv preprint"},{"key":"ref50","article-title":"How significant are the real performance gains? an unbiased evaluation framework for graphrag","author":"Zeng","year":"2025","journal-title":"arxiv preprint"},{"key":"ref51","article-title":"Multi-hop question answering under temporal knowledge editing","author":"Cheng","year":"2024","journal-title":"arxiv preprint"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"issue":"2","key":"ref53","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"ICLR"},{"key":"ref54","article-title":"Parameter-efficient fine-tuning for large models: A comprehensive survey","author":"Han","year":"2024","journal-title":"arxiv preprint"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1126\/science.adi6000"},{"key":"ref56","first-page":"1183","article-title":"Augment to interpret: Unsupervised and inherently interpretable graph embeddings","volume-title":"Asian Conference on Machine Learning","author":"Scafarto"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11400992.pdf?arnumber=11400992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:53:04Z","timestamp":1772866384000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11400992\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11400992","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}