Knowledge Graph

Understanding the Main Technologies of Knowledge Graphs

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Understanding the Main Technologies of Knowledge Graphs

Introduction: The main technologies of knowledge graphs include knowledge acquisition, knowledge representation, knowledge storage, knowledge modeling, knowledge fusion, knowledge understanding, and knowledge maintenance. The main technologies of knowledge graphs include knowledge acquisition, knowledge representation, knowledge storage, knowledge modeling, knowledge fusion, knowledge understanding, and knowledge maintenance. These seven aspects support the construction of knowledge graphs from … Read more

Understanding Knowledge Graphs Clearly

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Understanding Knowledge Graphs Clearly

01 Origin of Knowledge Graphs In 1977, American computer scientist Feigenbaum officially named knowledge engineering. He received the Turing Award in 1994 and is hailed as the father of expert systems and the founder of knowledge engineering. Knowledge engineering is a top-down approach that heavily relies on expert intervention, with the fundamental goal of imparting … Read more

Fudan University: Latest Survey on Multi-Modal Knowledge Graphs

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Fudan University: Latest Survey on Multi-Modal Knowledge Graphs

This article is approximately 2500 words long and is recommended for a 5-minute read. This article summarizes a knowledge-based direction paper, integrating multi-modal knowledge into multi-modal knowledge graphs. This blog post summarizes a knowledge-based direction paper, integrating multi-modal knowledge into multi-modal knowledge graphs. From Fudan University, here’s the path: Title: Multi-Modal Knowledge Graph Construction and … Read more

Research on Knowledge Graph Construction Technology Based on LLM Graph Transformer

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Research on Knowledge Graph Construction Technology Based on LLM Graph Transformer

Source: DeepHub IMBA This article is approximately 7000 words long and is recommended to be read in over 10 minutes. This article delves into the LLM Graph Transformer framework of LangChain and its dual-mode implementation mechanism for text-to-graph conversion. The conversion from text to graph is a research area with technical challenges, where the core … Read more

Graph RAG: Merging Knowledge Graphs with Large Models

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Training Materials September 2024 Issue This video is a community sharing report from the AI-themed discussion meeting of the Yunhan Community held on August 3, 2024. Speaker: Chen Xiaoyang (Member of the Yunhan Community Committee, General Manager of Fucheng Company) Report Title: “Graph RAG – When Knowledge Graphs Meet Large Models“ AI Video Summary: This … Read more

Enhancing RAG Capabilities with Knowledge Graphs to Reduce LLM Hallucinations

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Enhancing RAG Capabilities with Knowledge Graphs to Reduce LLM Hallucinations

Source: DeepHub IMBA This article is approximately 2600 words long and is recommended to be read in 8 minutes. For hallucinations in large language models (LLM), knowledge graphs have proven to be superior to vector databases. When using large language models (LLMs), hallucination is a common issue. LLMs generate fluent and coherent text but often … Read more

What Is LightRAG, Better Than GraphRAG?

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What Is LightRAG, Better Than GraphRAG?

1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … Read more

Google Automatically Generates Text from Knowledge Graphs

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Google Automatically Generates Text from Knowledge Graphs

New Intelligence Report Source: Google AI Editor: LRS [New Intelligence Guide] Based on pre-training experience, more data leads to better performance! Google recently published a paper at NAACL 2021 that can automatically generate text data from knowledge graphs, so there’s no need to worry about insufficient corpora anymore! Large pre-trained natural language processing (NLP) models, … Read more

When BERT Meets Knowledge Graphs

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When BERT Meets Knowledge Graphs

Author: Gao Kaiyuan School: Shanghai Jiao Tong University Research Direction: Natural Language Processing Zhihu Column: BERT on the Shoulders of Giants Original Article Link: https://zhuanlan.zhihu.com/p/91052495 Introduction In the previous blog, I discussed some knowledge representation learning models. Today, let’s explore the current most popular BERT model and how it develops with the addition of external … Read more

K-BERT Model: Knowledge Empowerment with Knowledge Graphs

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K-BERT Model: Knowledge Empowerment with Knowledge Graphs

Author丨Zhou Peng Affiliation丨Tencent Research Direction丨Natural Language Processing, Knowledge Graph Background In the past two years, unsupervised pre-trained language representation models such as Google’s BERT have achieved remarkable results in various NLP tasks. These models are pre-trained on large-scale open-domain corpora to obtain general language representations and then fine-tuned on specific downstream tasks to absorb domain-specific … Read more