A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
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arXiv
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| Format: | Preprint |
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2024
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| author | Otal, Hakan T. Faraone, Stephen V. Canbaz, M. Abdullah |
| author_facet | Otal, Hakan T. Faraone, Stephen V. Canbaz, M. Abdullah |
| contents | Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating scientific literature and clinical data with the help of cutting-edge large language models. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder. Building on these findings, we curated a knowledge graph that is usable in a context-aware chatbot (Graph-RAG) with Large Language Models (LLMs), enabling accurate and informed interactions. Our knowledge graph not only advances the understanding of ADHD but also provides a powerful tool for research and clinical applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_12853 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights Otal, Hakan T. Faraone, Stephen V. Canbaz, M. Abdullah Social and Information Networks Computation and Language 68T30, 68T50, 92C30 I.2.4; I.2.7; J.3 Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating scientific literature and clinical data with the help of cutting-edge large language models. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder. Building on these findings, we curated a knowledge graph that is usable in a context-aware chatbot (Graph-RAG) with Large Language Models (LLMs), enabling accurate and informed interactions. Our knowledge graph not only advances the understanding of ADHD but also provides a powerful tool for research and clinical applications. |
| title | A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights |
| topic | Social and Information Networks Computation and Language 68T30, 68T50, 92C30 I.2.4; I.2.7; J.3 |
| url | https://arxiv.org/abs/2409.12853 |