article · Discover Artificial Intelligence
Integrating large language models with knowledge graphs improves the interpretability and performance of artificial intelligence systems. Research in this area categorises integration strategies into three core paradigms: knowledge graph-augmented language models, language model-augmented knowledge graphs, and synergised frameworks. A detailed examination of these approaches assesses their methodologies, benefits, limitations, and practical uses across real-world situations. Combining these two technologies substantially enhances real-time data analysis, streamlines operational decision-making, and drives innovation across diverse sectors. Additionally, establishing suitable evaluation metrics and standard benchmarks is critical for measuring system performance. Key operational bottlenecks, such as system scalability and significant computational overhead, present ongoing difficulties, but identified pathways and solutions aim to resolve these technical constraints.
Artificial intelligence systems often struggle with transparency and factual grounding. Combining knowledge graphs with large language models helps create more interpretable, reliable tools capable of handling complex information. Understanding how to merge these technologies enables better real-time data analysis and more efficient decision-making across diverse industries, while clarifying the technical trade-offs between computational cost and performance.
The survey highlights applications in real-time data analysis and decision-making across varied domains, which could benefit enterprise software developers and data analysts. However, because this work provides a high-level review of models, benchmarks, and challenges rather than a deployable tool, it sits at an early, foundational stage. Real-world deployment remains constrained by computational overhead and scalability issues that require targeted technical resolution.
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Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.
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DOI: 10.1007/s44163-024-00175-8
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