1 citations · 1 across the 2 of their papers we have counts for
4 papers
Benchmarking Large Language Models on Heterogeneous Cognitive and Biomedical Knowledge Graphs: The Role of Graph Grounding in Multi-Hop Reasoning and Hallucination Mitigation
Ali Sarabadani, Kheirolah Rahsepar Fard, Tahereh Jeirani
Large language models (LLMs) are increasingly used in biomedical and cognitive neuroscience, but their ability to reason reliably over heterogeneous knowledge graphs (KGs) remains…
MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
Ali Sarabadani, Kheirolah Rahsepar Fard
The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional…
ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs
Ali Sarabadani, Kheirolah Rahsepar Fard, Hamid Dalvand
The paper introduces ExKG-LLM, a framework designed to automate the expansion of cognitive neuroscience knowledge graphs (CNKG) using large language models (LLMs). It addresses lim…
SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models
Ali Sarabadani, Kheirolah Rahsepar Fard, Hamid Dalvand
The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large language models (LLMs). SKG-LLM e…