Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions

TL;DR

This paper explores how Large Language Models, Knowledge Graphs, and Search Engines can be combined to better address diverse user information needs, emphasizing user perspective and proposing a research roadmap.

Contribution

It introduces a taxonomy of user information needs and analyzes the potential synergies among LLMs, Knowledge Graphs, and Search Engines.

Findings

Taxonomy of user information needs established

Analysis of pros and cons of each technology

Roadmap for future research directions

Abstract

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.