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SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces
Brian Wang, Mani Srivastava
https://arxiv.org/abs/2509.00287 https://arxiv.org…

SIGMUS: Semantic Integration for Knowledge Graphs in Multimodal Urban Spaces
Modern urban spaces are equipped with an increasingly diverse set of sensors, all producing an abundance of multimodal data. Such multimodal data can be used to identify and reason about important incidents occurring in urban landscapes, such as major emergencies, cultural and social events, as well as natural disasters. However, such data may be fragmented over several sources and difficult to integrate due to the reliance on human-driven reasoning for identifying relationships between the mul…

Join us for an infoshare on the @… and its API!
As one of the world’s largest research knowledge graphs, it interlinks hundreds of millions of metadata records from over 100K trusted data sources & serves as the authoritative source for the European Open Science Cloud (EOSC).
Stefania Amodeo from @…

You are an expert in ontologies and kn owledge graphs? You have an interest (or even better expertise) in materials science engineering? Then you should apply! Open PostDoc/Senior Researcher position at FIZ Karlsruhe ISE Research Team in the Knowledge Graph department. More info at https://www.fiz-k…

XOPS, which uses AI and knowledge graphs to automate enterprise IT tasks, emerges from stealth with $40M in funding led by Activant Capital and FPV Ventures (Belle Lin/Wall Street Journal)
https://www.wsj.com/articles/it-…

On the $(k,\ell)$-multiset anonymity measure for social graphs
Alejandro Estrada-Moreno, Elena Fern\'andez, Dorota Kuziak, Manuel Mu\~noz-M\'arquez, Rolando Trujillo-Rasua, Ismael G. Yero
https://arxiv.org/abs/2507.08433

On the $(k,\ell)$-multiset anonymity measure for social graphs
The publication of social graphs must be preceded by a rigorous analysis of privacy threats against social graph users. When the threat comes from inside the social network itself, the threat is called an active attack, and the de-facto privacy measure used to quantify the resistance to such an attack is the $(k,\ell)$-anonymity. The original formulation of $(k,\ell)$-anonymity represents the adversary's knowledge as a vector of distances to the set of attacker nodes. In this article, we argue …

You are an expert in ontologies and knowledge graphs? You have an interest (or even better expertise) in materials science engineering? Then you should apply! Open PostDoc/Senior Researcher position at FIZ Karlsruhe ISE Research Team in the Knowledge Graph department. More info at https://www.fiz-ka…

Eine Masterarbeit auf dem Themengebiet Semantic Web, Knowledge Graphs und Reasoning zu schreiben und nennenswerte Ergebnisse zu suchen, bedeutet aktuell, dass man kategorisch alle Paper die irgendwie mit AI zu tun haben, kategorisch ausschließen kann.
Jedes interessant klingende Paper, das ich in den letzten 4 Monaten lesen wollte, hat irgendwo einfach ein LLM auf Daten geworfen und geschaut was passiert. Jedes!

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk
https://arxiv.org/abs/2507.05526

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph) are known, it is possible to estimate the intervention distributions. In the absence of this domain knowledge, the causal structure must be discovered from the available observational data. However, observational data are often compatible with multiple causal graphs, making met…

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Peyman Baghershahi, Gregoire Fournier, Pranav Nyati, Sourav Medya
https://arxiv.org/abs/2508.07117

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs, which are common in domains such as citation networks, social platforms, and knowledge graphs. GNNs are not inherently interpretable and thus, many explanation methods have been proposed. However, existing explanation methods often struggle to generate interpretable, fine-grained rationales, especially when node attributes include rich natural language. In this work, …