GraphNews

Discover how Graph of Thoughts aims to revolutionize prompt engineering, and LLMs more broadly, enabling more flexible and human-like problem-solving.

From 5th to 9th of February, I had the privilege to participate to a seminar entitled "Are Knowledge Graphs Ready for the Real World? Challenges and Perspective" at the Dagstuhl Schlosse. It was my third Dagstuhl experience, so I was ready for a very intense week of work and discussion and I have to

KGLens: Unlocking Hidden Connections: Knowledge Graphs Reveal What Models Don't Know …. Evaluating AI Knowledge Just Got Smarter … How do we know if AI…

Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks. However, a versatile GFM has not yet been achieved. The key challenge in building GFM is how to enable positive transfer across graphs with diverse structural patterns. Inspired by the existing foundation models in the CV and NLP domains, we propose a novel perspective for the GFM development by advocating for a "graph vocabulary", in which the basic transferable units underlying graphs encode the invariance on graphs. We ground the graph vocabulary construction from essential aspects including network analysis, theoretical foundations, and stability. Such a vocabulary perspective can potentially advance the future GFM design following the neural scaling laws.

Lately I’ve become intrigued about the published research + open source code for a relatively specific topic: generating graphs to use for…

Exciting news for the data science community! PyG v2.5, the most widely used Graph Machine Learning Library, has just been released in collaboration with Intel…

Concepts form the very core of all our work on representing, accumulating, and manipulating knowledge with algorithms. Without them, we would simply have no framework to even start thinking about knowledge.

-- Source link: http://tinyurl.com/y8b8az2h-- Note: Reuters has not verified this story and does not vouch for its accuracy

Symbolic Artificial Intelligence, Semantic Web State of the art The Semantic Web project was formulated at the end of the 20th century and is based on the availability of inference engines and onto…

Entity-based SEO is the future. Discover the art of crafting search-relevant content through expert entity extraction and linking techniques.

Yes, Structured Data Markup = E-E-A-T fairy dust, or so the popular answer goes. Complete nonsense, because magic does not compute! The real story is...

The NLQ integration for Ontotext GraphDB in LangChain: a framework designed to simplify the creation of applications using LLMs

Although I have of course heard this question more often in recent months than in all the years before, it is really just a reiteration of the question of all questions, which is probably the most fundamental question of all for AI: How much human (or symbolic AI) does statistical AI need? With ever

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

Our new preprint, "Towards Learning on Graphs with State Space Models" is out!We present Graph Mamba Networks (GMNs), a scalable (linear time) class of Graph Neural Networks that aggregates the hierarchical neighborhoods of nodes using selective state space models.Arxiv:… pic.twitter.com/OjCHMYTvW5— Ali Behrouz (@behrouz_ali) February 14, 2024

Microsoft is transforming retrieval-augmented generation with GraphRAG, using LLM-generated knowledge graphs to significantly improve Q&A when analyzing complex information and consistently outperforming baseline RAG. Get the details.

Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them

A semantic layer for end-to-end, intelligent automation

And this is the amazing prof. Maurizio Lenzerini on the benefits of using ontologies for data integration, captured when showing the long journey of knowledge…

Unlocking the Potential of Large Language Models for Graph Learning … The astounding capabilities of Large Language Models continue to push the boundaries of…

Leveraging LLMs for Causal Reasoning: Why Knowledge and Algorithms are Key 🌳 Causal reasoning — the capacity to understand cause-effect relationships and…

𝗟𝗲𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗽𝗲𝗮𝗸! Inject structured data directly with GraphTokens and supercharge your LLM's reasoning abilities. Our exciting research is… | 16 comments on LinkedIn

Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge, enhancing their meaning and explainability. Let's delve into… | 25 comments on LinkedIn

Information developers, technical writers, and knowledge management professionals face enormous challenges that are often not clear to their "customers", and not even to their managers. In a nutshell, they put significant effort into organizing and managing enormous collections of information – in t

A Step-by-step Walkthrough with GenAI-Stack and OpenAI

This article gives a practical introduction to the potential of causal graphs.

We explore the 3 types of recursive retrieval — Page-Based, Information-Centric, and Concept-Centric retrieval

There is no intelligence without knowledge. Despite the incredible power of Large Language Models (LLM), they still significantly struggle…