HBrain: A Knowledge Graph System for Building Human-like Semantic Memory Networks Using Large Language Models

HBrain is a knowledge graph system that uses large language models to simulate the human brain's semantic memory network. It aims to solve problems such as high construction costs, difficulty in maintenance, and weak generalization ability of traditional knowledge graphs, enabling automated knowledge extraction and intelligent question-answering. Its core innovations include human-like memory mechanisms and low-code deployment, which are applicable to scenarios like enterprise knowledge management and academic research assistance. It represents an important trend in the field of knowledge engineering from "manual engineering" to "bionic learning".

Knowledge graphs are crucial in scenarios like search engines and recommendation systems, but traditional construction methods face three major problems: high construction costs (requiring extensive annotation by domain experts), difficulty in maintenance (delayed knowledge updates), and weak generalization ability (hard to transfer across domains). Large language models, with their pre-trained world knowledge and language understanding capabilities, provide a new solution path for automated knowledge extraction.

HBrain simulates the human brain's semantic memory network, and its core architecture consists of four parts: 1. Document parsing and preprocessing (text chunking, entity recognition, context modeling); 2. Entity and relationship extraction (entity standardization, relationship classification, confidence evaluation); 3. Knowledge graph construction (nodes with attributes, edges with relationship types and confidence, property graph model); 4. Intelligent question-answering engine (parsing questions into graph queries, multi-hop reasoning, natural language answers combining retrieval and generation).

The innovative highlights of HBrain include: 1. Human-like memory mechanisms (activation diffusion, semantic similarity calculation, dynamic update of association strength); 2. Low-code/no-code deployment (concise API + visual interface, allowing users to upload documents to build knowledge bases and perform interactive queries without professional knowledge); 3. Modular design (supports replacement of multiple LLM backends, graph storage, and embedding models).

HBrain is applicable to multiple scenarios: 1. Enterprise knowledge management (integrating scattered documents, building queryable knowledge bases, assisting new employees in obtaining information); 2. Academic research assistance (extracting key concepts from papers, building domain knowledge maps, assisting literature reviews); 3. Intelligent customer service upgrade (flexible problem understanding, accurate answers to reduce hallucinations, continuous learning and content updates).

Current limitations of HBrain: dependence on LLM capabilities, high computing costs, and automated results requiring manual review. Future directions: introducing multimodal support for non-text knowledge sources, optimizing extraction strategies by combining reinforcement learning, and developing efficient incremental update mechanisms.

HBrain lowers the threshold for knowledge graph construction and improves the level of intelligence, reflecting the transformation of AI system design from manual engineering to bionic learning. It is an out-of-the-box knowledge management solution for developers and enterprises. As LLM capabilities improve, systems integrating neural-symbolic AI will demonstrate value in more fields.