基于语义推理的知识对齐(knowledge alignment with semantic reasoning)。 模型能力增强(model capability enhancement)。 HybridRAG(VectorRAG+GraphRAG) HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction HybridRAG|传统RAG集成GraphRAG的初步方案 HybridRAG...
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion Conversational Question Answering with Reformulations over Knowledge Graph Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models KnowledgeNavigator: Leveraging Large Language Models for...
Discover a smarter way to build GenAI apps with Neo4j GraphRAG. By combining knowledge graphs and vector search, GraphRAG infuses your AI with deep context and multi-hop reasoning for more accurate, relevant, and explainable results. Read More Capabilities Build Your Next GenAI Breakthrough Kno...
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion 「方法:」本文提出了一种新颖的方法,将时间链接预测视为历史事件链中的事件生成任务,以提高语言模型的推理能力。研究通过有效的微调方法,使语言模型适应特定的图文信息和时间线模式。同时,引入基于结构的历史数据增强和逆向...
SYS_PROMPT = ( "You are a network graph maker who extracts terms and their relations from a given context. " "You are provided with a context chunk (delimited by ```) Your task is to extract the ontology " "of terms mentioned in the given context. These terms should represent...
KG-enhanced Recommendation 是一个增强型推荐算法，它利用知识图谱（Knowledge Graph，KG）来提升推荐系统的性能。这个算法的核心思想是将用户和物品在知识图谱中的路径转换为文本信息，然后融入到大模型（Large Language Model，LLM）中，以此来增强用户代理（user agent）的记忆，从而提高推荐质量。2、用户-商品上下文...
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具体的实现方法在这个 PR中已经可以做到了,只需要设置with_text2cypher=True,Graph RAG 就会包含 Text2Cypher 上下文,敬请期待它的合并。 结论 通过将知识图谱、图存储集成到 LLM 技术栈中,Graph RAG 把 RAG 的上下文学习推向了一个新的高度。它能在 LLM 应用中,通过利用现有(或新建)的知识图谱,提取细粒度、精...
Translate the following user text to an RDF graph using the SCHEMA.ORG ontologies formatted as TTL. Use the prefix ex: with IRI <http://example.com/> for any created entities. 也会得到相应的输出 代码语言:javascript 代码运行次数:0 运行 AI代码解释 @prefix ex: <http://example.com/> . @...
LLM 与 Knowledge Graph 的融合 #61 winterpi opened this issue Dec 18, 2023· 0 comments Comments Owner winterpi commented Dec 18, 2023 • edited LLM 与 KG 的优缺点分析 LLM大语言模型 优点:通用知识的理解及泛化能力、语言理解和知识处理能力; 缺点:幻觉导致准确性低,缺少领域内知识(新知识)...