KAG - A professional domain knowledge service framework launched by Ant Group
KAG (Knowledge Augmented Generation) is a domain-specific knowledge service framework launched by Ant Group. It enhances the question-answering performance of large language models (LLMs) in specific domains based on knowledge augmentation, providing knowledge services for vertical fields...
What is KAG?
KAG (Knowledge Augmented Generation) is a professional domain knowledge service framework launched by Ant Group. It enhances the question-answering performance of large language models (LLMs) in specific domains through knowledge augmentation, building logical reasoning and question-answering solutions for knowledge bases in vertical fields. KAG integrates unstructured data, structured information, and business expert experience based on a mutual indexing structure of knowledge and text blocks, forming a unified business knowledge graph. KAG introduces a hybrid reasoning engine guided by logical form, transforming natural language problems into a problem-solving process combining language and symbols. It supports logical reasoning and multi-hop fact-based question answering, effectively overcoming the ambiguity of traditional RAG vector similarity calculations and the noise problem introduced by OpenIE, significantly improving the accuracy and efficiency of reasoning and question answering.
KAG's main functions
- Enhanced Q&A in professional fieldsBy combining knowledge graphs and vector retrieval, we can enhance the question-answering capabilities of large-scale language models in specific domains, generating more accurate, professional, and logical answers.
- Knowledge Representation and Retrieval OptimizationUsing an LLM-friendly knowledge representation framework, we can achieve mutual indexing between knowledge graphs and raw text blocks, optimize the knowledge representation, reasoning, and retrieval process, and improve the accuracy and relevance of retrieval results.
- Hybrid Reasoning and Problem SolvingA hybrid reasoning engine guided by logical forms transforms natural language problems into a problem-solving process that combines language and symbols, integrating retrieval, knowledge graph reasoning, language reasoning, and numerical computation to effectively handle complex problems.
- Knowledge Alignment and Semantic EnhancementKnowledge alignment is based on semantic reasoning, defining domain knowledge as various semantic relations to improve the standardization and connectivity of knowledge, and enhance the accuracy and consistency of knowledge representation.
KAG's technical principles
- Combining knowledge graphs and vector retrievalBased on the structured semantic information of knowledge graphs and the efficiency of vector retrieval, knowledge graphs organize and represent domain knowledge, and vector retrieval is used to quickly obtain knowledge fragments related to the problem.
- LLM-friendly knowledge representationThe LLMRiSPG framework was designed to adapt the data, information, and knowledge hierarchy of knowledge graphs to the input and output formats of large-scale language models, thereby achieving unified representation and effective transmission of knowledge.
- Mutual indexing mechanismEstablish a mutual indexing relationship between the knowledge graph structure and the original text blocks, so that the entities and relationships in the graph structure are associated with the content in the text blocks, thereby enhancing the semantic connectivity of knowledge and the accuracy of retrieval.
- Logical form guides reasoningIt uses a logical formal language to decompose complex problems into multiple subproblems, and solves them based on operators such as planning, reasoning, and retrieval, thereby symbolizing and structuring the problem-solving process and improving the rigor and interpretability of reasoning.
- Semantic Reasoning and Knowledge AlignmentIn the process of constructing and retrieving knowledge graphs, semantic reasoning technology is used to align and integrate knowledge, identify and establish semantic relationships between knowledge, and improve the accuracy and consistency of knowledge.
KAG's project address
- GitHub repository:https://github.com/OpenSPG/KAG
- arXiv technical paper:https://arxiv.org/pdf/2409.13731
Application scenarios of KAG
- Financial risk controlIt integrates financial data and knowledge graphs to identify and warn of risks such as credit defaults and fraud, and assists financial institutions in formulating risk control strategies.
- Healthcare: Construct a medical knowledge graph to assist doctors in disease diagnosis, treatment plan recommendation, and drug development, thereby improving the quality and efficiency of medical services.
- Intelligent Customer ServiceAs the core engine of intelligent customer service, it enables accurate understanding and rapid responses to complex user needs, thereby improving user satisfaction and enterprise service efficiency.
- Knowledge Management and Decision SupportIt helps enterprises build enterprise-level knowledge graphs, provides decision support, and assists enterprises in making decisions such as strategic planning, market analysis, and product innovation.
- Education and Scientific ResearchIt integrates educational and research resources to assist teachers in instructional design and researchers in research and analysis, thereby promoting knowledge dissemination and innovation.