Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy
Background: Both early researchers, such as new graduate students, and experienced researchers face the challenge of sifting through vast amounts of literature to find their needle in a haystack. This process can be time-consuming, tedious, or frustratingly unproductive. Methods: Using only abstract...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2673-2688/6/3/47 |
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| author | Joëd Ngangmeni Danda B. Rawat |
| author_facet | Joëd Ngangmeni Danda B. Rawat |
| author_sort | Joëd Ngangmeni |
| collection | DOAJ |
| description | Background: Both early researchers, such as new graduate students, and experienced researchers face the challenge of sifting through vast amounts of literature to find their needle in a haystack. This process can be time-consuming, tedious, or frustratingly unproductive. Methods: Using only abstracts and titles of research articles, we compare three retrieval methods—Bibliographic Indexing/Databasing (BI/D), Retrieval-Augmented Generation (RAG), and Graph Retrieval-Augmented Generation (GraphRAG)—which reportedly offer promising solutions to these common challenges. We assess their performance using two sets of Large Language Model (LLM)-generated queries: one set of queries with context and the other set without context. Our study evaluates six sub-models—four from Light Retrieval-Augmented Generation (LightRAG) and two from Microsoft’s Graph Retrieval-Augmented Generation (MGRAG). We examine these sub-models across four key criteria—comprehensiveness, diversity, empowerment, and directness—as well as the overall combination of these factors. Results: After three separate experiments, we observe that MGRAG has a slight advantage over LightRAG, naïve RAG, and BI/D for answering queries that require a semantic understanding of our data pool. The results (displayed in grouped bar charts) provide clear and accessible comparisons to help researchers quickly make informed decisions on which method best suits their needs. Conclusions: Supplementing BI/D with RAG or GraphRAG pipelines would positively impact the way both beginners and experienced researchers find and parse through volumes of potentially relevant information. |
| format | Article |
| id | doaj-art-a0ff6d3468ae470693af294c5be0f580 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-a0ff6d3468ae470693af294c5be0f5802025-08-20T03:43:50ZengMDPI AGAI2673-26882025-03-01634710.3390/ai6030047Swamped with Too Many Articles? GraphRAG Makes Getting Started EasyJoëd Ngangmeni0Danda B. Rawat1Department of Electrical Engineering and Computer Science, Howard University, 2400 6th St NW, Washington, DC 20059, USADepartment of Electrical Engineering and Computer Science, Howard University, 2400 6th St NW, Washington, DC 20059, USABackground: Both early researchers, such as new graduate students, and experienced researchers face the challenge of sifting through vast amounts of literature to find their needle in a haystack. This process can be time-consuming, tedious, or frustratingly unproductive. Methods: Using only abstracts and titles of research articles, we compare three retrieval methods—Bibliographic Indexing/Databasing (BI/D), Retrieval-Augmented Generation (RAG), and Graph Retrieval-Augmented Generation (GraphRAG)—which reportedly offer promising solutions to these common challenges. We assess their performance using two sets of Large Language Model (LLM)-generated queries: one set of queries with context and the other set without context. Our study evaluates six sub-models—four from Light Retrieval-Augmented Generation (LightRAG) and two from Microsoft’s Graph Retrieval-Augmented Generation (MGRAG). We examine these sub-models across four key criteria—comprehensiveness, diversity, empowerment, and directness—as well as the overall combination of these factors. Results: After three separate experiments, we observe that MGRAG has a slight advantage over LightRAG, naïve RAG, and BI/D for answering queries that require a semantic understanding of our data pool. The results (displayed in grouped bar charts) provide clear and accessible comparisons to help researchers quickly make informed decisions on which method best suits their needs. Conclusions: Supplementing BI/D with RAG or GraphRAG pipelines would positively impact the way both beginners and experienced researchers find and parse through volumes of potentially relevant information.https://www.mdpi.com/2673-2688/6/3/47GraphRAGLightRAGretrieval-augmented generationgraphlarge language modelLLM |
| spellingShingle | Joëd Ngangmeni Danda B. Rawat Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy AI GraphRAG LightRAG retrieval-augmented generation graph large language model LLM |
| title | Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy |
| title_full | Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy |
| title_fullStr | Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy |
| title_full_unstemmed | Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy |
| title_short | Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy |
| title_sort | swamped with too many articles graphrag makes getting started easy |
| topic | GraphRAG LightRAG retrieval-augmented generation graph large language model LLM |
| url | https://www.mdpi.com/2673-2688/6/3/47 |
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