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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Combining large language models with enterprise knowledge graphs: a perspective on enhanced natural language

Luca Mariotti1, Veronica Guidetti1, Federica Mandreoli1

  • 1Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena, Italy.

Frontiers in Artificial Intelligence
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Summary

Large Language Models (LLMs) offer advanced solutions for enriching Knowledge Graphs (KGs), automating complex processes. This research explores LLM-based KG enrichment techniques and industrial deployment challenges.

Keywords:
AILLMScarbon footprintenterprise AIhuman in the loopknowledge graphknowledge graph enrichmentrelation extraction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Knowledge Graphs (KGs) organize entities and relations, enhancing applications like recommendation and question-answering systems.
  • Enterprise KGs, such as Expert.AI's Sensigrafo, utilize Natural Language Understanding (NLU) for machine-oriented lexicons.
  • KG maintenance and enrichment traditionally require significant manual effort.

Purpose of the Study:

  • To review state-of-the-art Large Language Model (LLM) techniques for Knowledge Graph Enrichment (KGE).
  • To identify challenges in automating and deploying LLM-based KGE in industrial settings.
  • To propose solutions for data quality, scarcity, economic viability, privacy, and language evolution in automated KGE.

Main Methods:

  • Review of current LLM-based Knowledge Graph Enrichment (KGE) methodologies.
  • Analysis of challenges in industrial application and automation of KGE processes.
  • Discussion of strategies to address data, economic, privacy, and linguistic issues.

Main Results:

  • LLMs show significant potential for automating Knowledge Graph Enrichment (KGE).
  • Industrial deployment faces hurdles including data quality, scalability, cost, privacy, and evolving language.
  • Automating KGE requires balancing accuracy with practical implementation concerns.

Conclusions:

  • LLM-based KGE is a promising frontier for advancing knowledge representation and AI applications.
  • Overcoming industrial challenges is crucial for the widespread adoption of automated KGE.
  • Future work should focus on robust, accurate, and economically viable automated KGE solutions.