Large language models(LLM) & Knowledge Graph(KG) : 1+1>2 ?

EpiK Protocol has always been committed to building robust knowledge graphs. Integrating knowledge graphs and large language models (LLMs) is a mutually beneficial relationship. Only by constructing accurate knowledge graphs can LLM-trained AI models possess logical reasoning abilities. Today, we’ll explore three main approaches to combining LLMs and knowledge graphs (KGs).

First, we have KG-enhanced LLMs, where KGs are introduced during the pre-training and inference stages of LLMs to enhance the models. For example, EpiK converts the triples from the knowledge graph into textual inputs and masks entities or relations during pre-training, enabling the model to learn information from the knowledge graph.

The second approach is LLM-enhanced KGs, where LLMs play a role in KG construction, KG embedding, KG completion, text generation based on KGs, and KG-based question answering. For instance, in KG construction, LLMs can extract entities and relations from raw data, enriching the knowledge graph.

The third approach is the collaborative use of LLMs and KGs, primarily applied to knowledge representation and reasoning. For example, the KEPLER model transforms text into embedding representations using LLMs and combines the optimization objectives of knowledge graph embedding and language modeling, achieving joint representation of textual corpora and KGs.

LLMs and KGs each have their strengths and limitations. KGs are symbolic knowledge repositories with reasoning capabilities and good interpretability but have high construction costs and limited generalization. LLMs are parameterized probabilistic knowledge repositories with semantic understanding and generalization capabilities but lack interpretability. Therefore, combining LLMs and KGs allows us to leverage their strengths and achieve complementarity.

In summary, integrating LLMs and KGs includes KG-enhanced LLMs, LLM-enhanced KGs, and collaborative use of LLMs+KGs. These approaches break down the boundaries between LLMs and KGs, enabling LLMs to utilize the static and symbolic knowledge of KGs while allowing KGs to benefit from the parameterized probabilistic knowledge of LLMs. In practical applications, combining LLMs and KGs can enhance knowledge’s credibility, interpretability, and reasoning capabilities, making it suitable for scenarios requiring high knowledge requirements, involving multi-hop reasoning, and requiring KG-based visualizations.

Finally, we suggest checking out the AI bots we’ve developed using our knowledge graph. You can give them a spin by visiting this link: https://bot.epik-protocol.io/