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Are our novel Spatio-Spectral GNNs (SยฒGNNs) the better alternative to graph transformers?
Are our novel Spatio-Spectral GNNs (SยฒGNNs) the better alternative to graph transformers?
๐Ÿค” Are our novel Spatio-Spectral GNNs (SยฒGNNs) the better alternative to graph transformers? โž• SยฒGNNs (https://lnkd.in/dbrCTuxH, joint work with Simonโ€ฆ
Are our novel Spatio-Spectral GNNs (SยฒGNNs) the better alternative to graph transformers?
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Are our novel Spatio-Spectral GNNs (SยฒGNNs) the better alternative to graph transformers?
Graphs, Logic, and Lambda Calculus
Graphs, Logic, and Lambda Calculus
Here are my slides from KGC 2024: https://lnkd.in/gAhMRE_U. In a nutshell, I made the point that when it comes to adding advanced features to property graphsโ€ฆ
Graphs, Logic, and Lambda Calculus
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Graphs, Logic, and Lambda Calculus
Trip Report: ESWC 2024
Trip Report: ESWC 2024
Last week, I attended the 21st Extended (European) Semantic Web Conference. The conference was well organised by Dr. Albert Meroรฑo Peรฑuela from Kingโ€™s College London. He seemed surprisingly cโ€ฆ
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Trip Report: ESWC 2024
๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐˜€ ๐—ผ๐—ป ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐˜‚๐—บ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ๐˜€
๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐˜€ ๐—ผ๐—ป ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐˜‚๐—บ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ๐˜€
"๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐˜€ ๐—ผ๐—ป ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐˜‚๐—บ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ๐˜€" ๐Ÿ“‘ -- a Paper from a long-term project in my PhD has finally been released!โ€ฆ
๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐˜€ ๐—ผ๐—ป ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐˜‚๐—บ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ๐˜€
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๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ๐˜€ ๐—ผ๐—ป ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐˜‚๐—บ ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ๐˜€
LlamaIndex on LinkedIn: Build-your-own Graph RAG
LlamaIndex on LinkedIn: Build-your-own Graph RAG
Build-your-own Graph RAG ๐Ÿ•ธ๏ธ There are two prepackaged ways to do RAG with knowledge graphs: vector/keyword search with graph traversal, and text-to-cypher.โ€ฆ | 15 comments on LinkedIn
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LlamaIndex on LinkedIn: Build-your-own Graph RAG
Knowledge Graphs for mimicking human memory to integrate โ€œnew experiencesโ€ in LLMs
Knowledge Graphs for mimicking human memory to integrate โ€œnew experiencesโ€ in LLMs
๐Ÿ’กย Knowledge Graphs for mimicking human memory to integrate โ€œnew experiencesโ€ in LLMs. ๐Ÿ”ฌย In a paper entitled โ€œHippoRAG: Neurobiologically Inspired Long-Termโ€ฆ
Knowledge Graphs for mimicking human memory to integrate โ€œnew experiencesโ€ in LLMs
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Knowledge Graphs for mimicking human memory to integrate โ€œnew experiencesโ€ in LLMs
Named Node Expressions and Reifications
Named Node Expressions and Reifications
I presented this (with some variation) a few days ago, following a post I wrote a few weeks ago about named node expressions and reifications in RDF, Turtleโ€ฆ
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Named Node Expressions and Reifications
GraphRAG: Design Patterns, Challenges, Recommendations
GraphRAG: Design Patterns, Challenges, Recommendations
Subscribe โ€ข Previous Issues Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines By Ben Lorica and Prashanth Rao. GraphRAG (Graph-based Retrieval Augmented Generation) enhances the traditional Retrieval Augmented Generation (RAG) method by integrating knowledge graphs (
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GraphRAG: Design Patterns, Challenges, Recommendations
Knowledge Graphs: Chat With Your Data
Knowledge Graphs: Chat With Your Data
This is a continuation of my previous article on creating a Knowledge Graph in 100 lines of code. In this article I will show you how you can use the โ€œchat with your dataโ€ paradigm to cโ€ฆ
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Knowledge Graphs: Chat With Your Data
AutoMR with Graph-Based Models for O-RAN
AutoMR with Graph-Based Models for O-RAN
AutoMR with Graph-Based Models for O-RAN A pivotal innovation propelling the telecom transformation can be the integration of Automated Machine Reasoningโ€ฆ
AutoMR with Graph-Based Models for O-RAN
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AutoMR with Graph-Based Models for O-RAN
The Alzheimerโ€™s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research
The Alzheimerโ€™s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research
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 diseaseโ€™s etiology and response to drugs. Objective: We designed the Alzheimerโ€™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.
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The Alzheimerโ€™s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research
How to Start with Graph Neural Networks for Time Series Forecasting
How to Start with Graph Neural Networks for Time Series Forecasting
๐Ÿ” How to Start with Graph Neural Networks for Time Series Forecastingโ“ ๐Ÿ“ˆ As Large Language Models continue to evolve, there are many debates about whetherโ€ฆ | 21 comments on LinkedIn
How to Start with Graph Neural Networks for Time Series Forecasting
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How to Start with Graph Neural Networks for Time Series Forecasting
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
๐Ÿ’กย How important are learning paths for gaining the skills needed to tackle real-life problems? ๐Ÿ”ฌResearchers from the University of Siegen (Germany) and Keioโ€ฆ
an approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
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An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
GitHub - Orange-OpenSource/noria-ontology: 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 realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies.
GitHub - Orange-OpenSource/noria-ontology: 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 realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies.
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...
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GitHub - Orange-OpenSource/noria-ontology: 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 realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies.