Unifying Large Language Models and Knowledge Graphs: A Roadmap
This January 2024 paper explores the integration of large language models (LLMs) and knowledge graphs (KGs) to enhance artificial intelligence applications.
Large language models can struggle with factual knowledge access and interpretation. The article presents a roadmap for unifying LLMs and knowledge graphs, proposing three frameworks:
Knowledge Graph-enhanced large language models
This approach integrates knowledge graphs during the pre-training and inference phases of large language models or to enhance the understanding of their inherent knowledge learned.
It aims to leverage the explicit knowledge from knowledge graphs to improve language model's factual understanding and reasoning capabilities.
Large language model-augmented knowledge graphs
This framework uses large language models to assist in various knowledge graph tasks, such as embedding, completion, construction, graph-to-text generation, and question answering.
Large language models can provide context and linguistic nuance to enrich knowledge graph's and address their limitations in evolving and representing unseen knowledge.
Synergized large language models and knowledge graphs
In this model, large language models and knowledge graphs work together in a mutually beneficial way, enhancing both systems for bidirectional reasoning, driven by data and knowledge.
This synergy aims to leverage the strengths of both to improve knowledge representation and reasoning.
What are knowledge graphs?
Knowledge Graphs (KGs) are structured models that store and organise information in the form of entities and relationships, encapsulated in triples (head entity, relation, tail entity), denoted as (h, r, t).
These graphs are pivotal in many AI and data-driven applications due to their ability to provide structured and interpretable knowledge.
Encyclopedic Knowledge Graphs
These KGs aggregate general knowledge from various sources like Wikipedia, databases, and expert inputs. Wikidata is a prime example, containing diverse knowledge extracted from Wikipedia articles. Other notable encyclopedic KGs include Freebase, Dbpedia, YAGO, and NELL, each with its unique method of knowledge extraction and representation.
Commonsense Knowledge Graphs
These graphs focus on everyday knowledge, capturing the common understanding of objects, events, and their interrelations. ConceptNet is a significant commonsense KG, offering insights into the general meanings of words and concepts. Other examples like ATOMIC and ASER emphasize causal relationships and commonsense reasoning, vital for AI systems to interpret and predict human-like reasoning patterns.
Domain-specific Knowledge Graphs
Tailored to specific fields such as medicine, finance, or biology, these KGs offer precise and reliable information pertinent to their respective domains. For instance, the Unified Medical Language System (UMLS) serves the medical field with a comprehensive set of biomedical terms and relationships. Domain-specific KGs are generally smaller than their encyclopedic counterparts but are highly valued for their accuracy and relevance in specialized areas.
Multi-modal Knowledge Graphs
Extending beyond textual data, multi-modal KGs incorporate various data types like images, videos, and sounds to create a richer and more versatile knowledge base. Examples include IMGpedia, MMKG, and Richpedia, which blend textual and visual data, enhancing tasks like image-text matching, visual question answering, and recommendation systems.
In the context of integrating KGs with Large Language Models (LLMs), KGs offer a structured and factual knowledge base that can complement the generative and inferential capabilities of LLMs. While LLMs excel in language processing and generalizability, they often lack the precision and explicit factual knowledge that KGs provide. This synergy between LLMs and KGs can lead to more informed and accurate AI systems capable of better reasoning, decision-making, and domain-specific applications.
Conclusion
The article provides a comprehensive overview of the current research integrating Large Language Models (LLMs) and Knowledge Graphs (KGs), a field gaining traction in both academia and industry.
It outlines various approaches to augment LLMs with KGs to improve their performance and understanding. Additionally, it presents methods where LLMs contribute to enhancing KG-related tasks, establishing a taxonomy based on different KG applications. The article concludes by addressing the challenges in this research area and suggesting potential future directions.
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