What if creating Linked Open Data was less like coding and more like writing? Could anyone extend the Semantic Web by sharing a document? Publish a knowledge… | 13 comments on LinkedIn

SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…

🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn

✨ Attention Information Extraction Enthusiasts ✨ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edge… | 33 comments on LinkedIn

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.

OfficeGraph is a knowledge graph of IoT device measurements. - RoderickvanderWeerdt/OfficeGraph

SCOOP: Shape Integration Framework. Contribute to dtai-kg/SCOOP development by creating an account on GitHub.

The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for rea...

Reduce LLM hallucinations with RAG over textual as well as structured knowledge bases. Today we are releasing WSTaRK h, a large-scale LLM retrieval benchmark… | 22 comments on LinkedIn

An open-source library for building knowledge graphs from text corpus using open-source LLMs like Llama 3 and Mixtral.

Our paper "𝐺𝑟𝑎𝑝ℎ𝐸𝑅: 𝐴 𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒-𝑎𝑤𝑎𝑟𝑒 𝑇𝑒𝑥𝑡-𝑡𝑜-𝐺𝑟𝑎𝑝ℎ 𝑀𝑜𝑑𝑒𝑙 𝑓𝑜𝑟 𝐸𝑛𝑡𝑖𝑡𝑦 𝑎𝑛𝑑 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛… | 34 comments on LinkedIn

Exploring the Potential of AI-Powered Knowledge Graphs with MindGraph ... Have you ever wondered how we can harness the power of AI to create and explore…

🚀 Join us for a special webinar on March 6th, 8am PT/5pm CET, as we unveil the latest in GNN technology - PyG 2.5! 🎉 Dive deep into the features with a live…

OccO: Occupation Ontology. Contribute to Occupation-Ontology/OccO development by creating an account on GitHub.

Recently there has been much excitement related to Artificial Intelligence and Knowledge Graphs, especially regarding the emerging…

Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute

Retrieve information that spans across multiple documents

Understanding data points through the context of their relationships

Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with large language models (LLMs) remains an understudied problem. In this work, we perform the first comprehensive study of encoding graph-structured data as text for consumption by LLMs. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered. These novel results provide valuable insight on strategies for encoding graphs as text. Using these insights we illustrate how the correct choice of encoders can boost performance on graph reasoning tasks inside LLMs by 4.8% to 61.8%, depending on the task.