Knowledge Extraction
Knowledge extraction aims to automatically derive structured information and insights from unstructured data sources, such as scientific literature and medical images. Current research heavily utilizes large language models (LLMs) and deep learning architectures, often incorporating techniques like prompt engineering, knowledge graph integration, and multi-agent systems to improve accuracy and efficiency. This field is crucial for accelerating scientific discovery, enabling more effective medical diagnosis and treatment, and facilitating knowledge-based decision-making across various domains.
Papers
August 22, 2024
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Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models
Jinbin Zhang, Nasib Ullah, Rohit Babbar
A Document-based Knowledge Discovery with Microservices Architecture
Habtom Kahsay Gidey, Mario Kesseler, Patrick Stangl, Peter Hillmann, Andreas Karcher
March 30, 2024
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