Knowledge Graph

What is a Knowledge Graph?

A knowledge graph is a way of representing and organizing the world’s structured knowledge, using a graph-structured data model or topology. A knowledge graph consists of nodes, edges, and labels that describe the entities and their relationships in the graph. For example, a node could be a person, a place, or a thing, and an edge could be a property, an attribute, or a connection between two nodes. A label is a way of naming or categorizing the nodes and edges.

Knowledge graphs are useful for integrating information from multiple data sources, encoding the semantics and context of the data, and supporting various applications such as search engines, question answering systems, recommender systems, and natural language processing.

Knowledge Graph & LLM

or what is KGLLM?

By using KGLLM, we can achieve a bidirectional communication between Knowledge Graphs and LLMs, where both sides can benefit from each other's strengths and compensate for each other's weaknesses. We can also enable a more natural and intuitive interaction between humans and machines using natural language and structured data.

LLMs & Knowledge GraphsLLMs & Knowledge Graphs

How to Use Chat-GPT and Python to Build a Knowledge Graph in Neo4j Based on Your Own ArticlesA graph containing structured knowledge from more than 120 articles on mathematics and data science

Using A Knowledge Graph To Improve Question Answering With LLMsI recently wrote an article explaining how to create a knowledge graph from any text using a LLM (GPT-3 for instance). Knowledge graphs are…

Harnessing the Power of Knowledge Graphs: Enriching an LLM with Structured DataA step-by-step guide to creating a knowledge graph and exploring its potential to enhance an LLM

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LLM Ontology-prompting for Knowledge Graph ExtractionPrompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents

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Knowledge Graph vs. Vector Database for Grounding Your LLM - Graph Database & AnalyticsExplore the benefits of grounding Large Language Models (LLMs) in knowledge graphs vs. vector databases for reliable and accurate data.

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Graph your Document: How To Use Langchain and Matplotlib To Build a Knowledge Graph | DataDrivenInvestorIn this easy-to-follow guide, we are going to see a complete example of how to use Langchain and Matplotlib To Build a Knowledge Graph. Recently,

Knowledge Graphs and Large Language Models: Taming Unruly Language Models with Reliable KnowledgeDenny Vrandečić, from the Wikimedia Foundation, gave a keynote at the Knowledge Graph Conference last May (2023) on the future of knowledge graphs in a world of large language models (LLMs). He argued that LLMs are powerful tools, but they also have some limitations, such as being expensive to train

GitHub - RManLuo/Awesome-LLM-KG: Awesome papers about unifying LLMs and KGsAwesome papers about unifying LLMs and KGs. Contribute to RManLuo/Awesome-LLM-KG development by creating an account on GitHub.

Exploring a Knowledge Graph-Based Solution to ChatGPT’s Inherent Limitations - TechNativeJim Webber says it’s time to extend ChatGPT to get answers we can better rely on ChatGPT has changed how I, and probably most of you, look at AI. But let’s be clear, it’s not ‘intelligent’ in the biological sense. It can complete writing tasks and some coding challenges, but in both cases human expertise […]

ChatGPT (LLMs) vs. Knowledge Graphs - The Cagle ReportThis is the second of three articles about ChatGPT and Knowledge Graphs. In the first article, I looked at how...

KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora

"Graph or Vector: Contrasting Approaches for Data Modelling, Querying and let’s not forget Business Value"What are the key considerations and trade-offs in choosing between a vectored database and a graph database for a real-time application that requires efficient querying, relationship exploration, and semantic analysis?" This question allows me to delve into the high level technical aspects of both

Make Meaningful Knowledge Graph from OpenSource REBEL ModelIn this tutorial, we will explore how to construct a knowledge graph database from any webpage by leveraging llama index in conjunction…

FalkorDB - The fastest way to your Knowledge | Low latency Graph Database - Scale outRoi Lipman

Enterprise Data Management: Knowledge Graphs & Vector SearchThis blog delves into the world of knowledge graphs and vector search and explores how they can manage and extract value from enterprise data.

LLM will Accelerate Knowledge Graph Adoption | Stardog We’re at the beginning of a revolution in machine-understandable semantics powered by Large Language Model breakthroughs. Stardog Voicebox, powered by LLMs, will fundamentally flatten the enterprise knowledge graph (EKG) implementation cost curve.

Unraveling Knowledge Graphs & LLMs: Multi-Hop Question Answering for Advanced AI InsightsDiscover the powerful fusion of Knowledge Graphs and LLMs as they redefine complex text data processing and enable multi-hop question answering, shaping the future of artificial intelligence capabilities.

Integrating Neo4j into the LangChain ecosystemLearn how to develop a LangChain agent that has multiple ways of interacting with the Neo4j database

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Unifying Large Language Models and Knowledge Graphs: A Roadmap

Integrate LLM workflows with Knowledge Graph using Neo4j and APOCOpenAI and VertexAI endpoints are now available as APOC Extended procedures

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