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Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions
https://arxiv.org/abs/2501.06699
This
research paper examines the interplay between large language models
(LLMs), knowledge graphs (KGs), and search engines (SEs) in fulfilling
user information needs. The authors analyze the strengths and
weaknesses of each technology across various dimensions, including
correctness, completeness, and freshness. A taxonomy of user
information needs is presented, showing how each technology—individually
or in combination—addresses different query types (e.g., factual,
explanatory, or advisory). Finally, the paper proposes research
directions for integrating these technologies synergistically to improve
information retrieval and user experience.
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By KnowledgeDBhttps://arxiv.org/abs/2501.06699
This
research paper examines the interplay between large language models
(LLMs), knowledge graphs (KGs), and search engines (SEs) in fulfilling
user information needs. The authors analyze the strengths and
weaknesses of each technology across various dimensions, including
correctness, completeness, and freshness. A taxonomy of user
information needs is presented, showing how each technology—individually
or in combination—addresses different query types (e.g., factual,
explanatory, or advisory). Finally, the paper proposes research
directions for integrating these technologies synergistically to improve
information retrieval and user experience.