GNN-RAG: combining LLMs language abilities with GNNs reasoning in RAG style
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…
Foundations and Frontiers of Graph Learning Theory
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures...
Foundations and Frontiers of Graph Learning Theory
𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘪𝘴 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘵𝘢𝘪𝘭𝘰𝘳𝘦𝘥 𝘧𝘰𝘳 𝘪𝘯𝘵𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭𝘭𝘺 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭.
𝗥𝗔𝗚 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗶𝗹 𝗗𝘂𝗲 𝗧𝗼 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗼𝗰𝘂𝘀 𝗢𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗻𝘁 𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎… | 12 comments on LinkedIn
𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘪𝘴 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘵𝘢𝘪𝘭𝘰𝘳𝘦𝘥 𝘧𝘰𝘳 𝘪𝘯𝘵𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭𝘭𝘺 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭.
Today, I’d like to discuss the evaluation methods for GraphRAG.
Additionally, there are evaluation methods where LLMs serve as the evaluators, including single point, reference-based, and pairwise-based…
An Overview of Knowledge Graph Embeddings
An Overview of Knowledge Graph Embeddings (KGEs) – Part 1. 🧠🍄 Knowledge Graphs represent real-world facts as structured data. Nodes represent entities or…
An Overview of Knowledge Graph Embeddings
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
This is something very cool! 3. GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models "GraphReader addresses the…
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI
Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific Knowledge) uses vector hashtag#embeddings to find the most relevant papers and an open-source hashtag#LLM to synthesize the answer for you
Ask your (research) question against 76 Million scientific articles: https://ask.orkg.org Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific…
Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific Knowledge) uses vector hashtag#embeddings to find the most relevant papers and an open-source hashtag#LLM to synthesize the answer for you
Synergizing LLMs and KGs in the GenAI Landscape | LinkedIn
Our paper "Are Large Language Models a Good Replacement of Taxonomies?" was just accepted to VLDB'2024! This finished our last stroke of study on how knowledgeable LLMs are and confirmed our recommendation for the next generation of KGs. How knowledgeable are LLMs? 1.
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
(PDF) BIFROST: A Future Graph Database Runtime
PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate
Multimodal Graph Benchmark
Associating unstructured data with structured information is crucial for real-world tasks that require relevance search. However, existing graph learning benchmarks often overlook the rich...
GraphReader: Long-Context Processing in AI
GraphReader: Long-Context Processing in AI ... As AI systems tackle increasingly complex tasks, the ability to effectively process and reason over long…
GraphReader: Long-Context Processing in AI
Large Generative Graph Models
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous...
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
💡 How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks? 🔎…
Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems
🚀 When #Transformers meet Graph Neural Networks (#GNNs)! Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems. 📄 [#SIGIR2023] 🔥Graph…
GraphEdit: Large Language Models for Graph Structure Learning
🚀 Enhance your graph analytics with 🔥GraphEdit 🔥 - the innovative Large Language Model (#LLM) tailored for graph structure learning and editing! 🌟…
GraphEdit: Large Language Models for Graph Structure Learning
Foundation Models in Graph & Geometric Deep Learning
In this post, we argue that the era of Graph FMs has already begun and provide a few examples of how one can use them already today.
Giving a Voice to Your Graph: Representing Structured Data for LLMs
By request, here are the slides today from my keynote at the #CVPR workshop on scene graphs (SG2RL)! papers discussed: 1. Talk Like a Graph (ICLR'24) -… | 19 comments on LinkedIn
GraphStorm: all-in-one graph machine learning framework for industry applications
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.
RAG + Knowledge Graphs cut customer support resolution time by 29.6%
RAG + Knowledge Graphs cut customer support resolution time by 29.6%. 📉 A case study from LinkedIn. 🤝💼 Conventional RAG methods treat historical issue… | 10 comments on LinkedIn
[2310.01061v1] Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can...
A repo for ICML graph papers
Following ICLR Graph Papers, I've created a repo for ICML graph papers, grouped by topic. We've got around 250 papers focusing on Graphs and GNNs in ICML'24.…
Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting
Excited to share that our recent work "Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting" is published at Scientific Reports…
Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary...
Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...
Extended SemanticWeb Conference trip report
This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…
Extended hashtag#SemanticWeb Conference
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...
Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models
Introducing Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data ... Did you know that 80% of enterprise data resides in unstructured… | 13 comments on LinkedIn
Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data
SPARQL CDTs: Representing and Querying Lists and Maps as RDF Literals
This specification defines an approach to represent generic forms of composite values (lists and maps, in particular) as literals in RDF, and corresponding extensions of the SPARQL language. These extensions include an aggregation function to produce such composite values, functions to operate on such composite values in expressions, and a new operator to transform such composite values into their individual components.
Semantic Layer as the Data Interface for LLMs
A new benchmark for natural language questions against databases dropped last week. What does it mean and how does the dbt Semantic Layer stack up?