Summary of Research Trends For the Interplay Between Large Language Models and Knowledge Graphs, by Hanieh Khorashadizadeh et al.
Research Trends for the Interplay between Large Language Models and Knowledge Graphs
by Hanieh Khorashadizadeh, Fatima Zahra Amara, Morteza Ezzabady, Frédéric Ieng, Sanju Tiwari, Nandana Mihindukulasooriya, Jinghua Groppe, Soror Sahri, Farah Benamara, Sven Groppe
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), crucial for advancing AI capabilities in understanding, reasoning, and language processing. The study explores areas like KG Question Answering, ontology generation, KG validation, and enhancing KG accuracy through LLMs. It examines LLM roles in generating texts and natural language queries for KGs. Through a structured analysis, the paper categorizes LLM-KG interactions, examines methodologies, and investigates collaborative uses and potential biases to provide new insights into combined LLM and KG potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey looks at how Large Language Models (LLMs) and Knowledge Graphs (KGs) work together to make AI better. It talks about things like asking questions of knowledge graphs, making lists of things that are related, checking if the graph is correct, and using LLMs to improve the graph’s accuracy. The study also looks at how LLMs can be used to create text descriptions and natural language queries for KGs. By looking closely at these interactions, the paper aims to show us new ways that LLMs and KGs can work together to make AI more powerful. |
Keywords
» Artificial intelligence » Question answering
