Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions
This work addresses the gap in considering user perspectives for combining AI tools in information retrieval, but it is incremental as it focuses on taxonomy and roadmap without new empirical results.
The paper tackles the problem of addressing diverse user information needs by introducing a taxonomy to study the pros, cons, and synergies of Large Language Models, Knowledge Graphs, and Search Engines, resulting in a derived roadmap for future research.
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is the user perspective. In particular, there remain many open questions regarding how best to address the diverse information needs of users, incorporating varying facets and levels of difficulty. This paper introduces a taxonomy of user information needs, which guides us to study the pros, cons and possible synergies of Large Language Models, Knowledge Graphs and Search Engines. From this study, we derive a roadmap for future research.