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

Last week, I attended the 21st Extended (European) Semantic Web Conference. The conference was well organised by Dr. Albert Meroño Peñuela from King’s College London. He seemed surprisingly c…

RAG meets GNNs: Integrating graphs into a modern workflow. Knowledge Graphs (KGs) are a powerful way to represent factual knowledge, but querying them with…

"𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀" 📑 -- a Paper from a long-term project in my PhD has finally been released!…

Use of a 20 frames per second (fps) RGB camera plus an event camera can achieve the same latency as a 5,000-fps camera with the bandwidth of a 45-fps camera without compromising accuracy.

GNN-RAG Combines the language understanding abilities of LLMs with the reasoning abilities of GNNs in a RAG style. The GNN extracts useful and relevant…

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

Background: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease’s etiology and response to drugs. Objective: We designed the Alzheimer’s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. Methods: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. Results: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. Conclusions: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PDF | There are thousands of cultural events occurring across UK weekly, such as theatre, comedy shows, festivals, exhibitions etcetera, leaving huge... | Find, read and cite all the research you need on ResearchGate

Cite as: Hong Yung Yip and Amit Sheth, "..

🔍 How to Start with Graph Neural Networks for Time Series Forecasting❓ 📈 As Large Language Models continue to evolve, there are many debates about whether… | 21 comments on LinkedIn

💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…

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...

In applications such as personal assistants, large language models (LLMs) must consider the user's personal information and preferences. However, LLMs lack the inherent ability to learn from user...

To Graph or Not to Graph Knowledge Graph Architectures and LLMs - Download as a PDF or view online for free

Revolutionizing Document Reranking with G-RAG: A Graph-Based Approach ... Discover how a novel graph-based reranker is transforming the way we retrieve…

PDF | The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the... | Find, read and cite all the research you need on ResearchGate

Spotify's catalog includes millions of music tracks and podcasts and has recently expanded to Audiobooks. Personalizing this content to users requires our algorithms to “understand” user preferences as well as content relationships across all content types...

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

How can we further increase the accuracy of LLM-powered question answering systems? Ontologies to the rescue! That is the conclusion of the latest research… | 16 comments on LinkedIn

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

Integrating temporal data into static knowledge graphs

Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee that the model utilizes a relevant piece of knowledge from the KG. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG. Specifically, our SURGE framework first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned by the retrieved subgraph. Then, we utilize contrastive learning to ensure that the generated texts have high similarity to the retrieved subgraphs. We validate our SURGE framework on OpendialKG and KOMODIS datasets, showing that it generates high-quality dialogues that faithfully reflect the knowledge from KG.

📢 How to apply Graph Neural Networks to stock predictions? The following paper presents the ideal starting point ….keep reading! 👇 This paper proposes the…

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

Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networks that leverages both spatial and temporal features of network states to learn latent tactical behaviors effectively. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states, leverages a recurrent neural network to temporally encode the evolution of states, and a fully-connected feed-forward network to decode the connectivity in the future state. Through extensive experiments, we demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input, achieving an accuracy of up to 99.2\% for the future state prediction task of tactical communication networks.