Tootfinder

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
https://arxiv.org/abs/2509.07905 https://

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tas…

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Swayamjit Saha
https://arxiv.org/abs/2507.04625 https://

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Large Language Models (LLMs) are powerful yet prone to generating factual errors, commonly referred to as hallucinations. We present a lightweight, interpretable framework for knowledge-aware self-correction of LLM outputs using structured memory graphs based on RDF triples. Without retraining or fine-tuning, our method post-processes model outputs and corrects factual inconsistencies via external semantic memory. We demonstrate the approach using DistilGPT-2 and show promising results on simpl…

ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy
Virgile Boraud (Mnemosyne), Yannis Bendi-Ouis (Mnemosyne), Paul Bernard (Mnemosyne), Xavier Hinaut (Mnemosyne)
https://arxiv.org/abs/2507.05279

ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy
We introduce a tool designed to improve the capabilities of Large Language Models (LLMs) in assisting with code development using the ReservoirPy library, as well as in answering complex questions in the field of Reservoir Computing. By incorporating external knowledge through Retrieval-Augmented Generation (RAG) and knowledge graphs, our approach aims to reduce hallucinations and increase the factual accuracy of generated responses. The system provides an interactive experience similar to Chat…

I would be happy if the AI industry actually had to pay authors for their work in accumulating ideas and knowledge and expressing them via their copyrighted works (registered copyright or not.)
The AI industry is whining "we are so big, we need to violate the law, we need to steal the work of others without even the pretense of compensating them, oh woe are we, oh woe are we".
There is a gap in our laws of copyright. Copyright covers expression, patents cover ideas. B…

AI industry horrified to face largest copyright class action ever certified
Copyright class actions could financially ruin AI industry, trade groups say.

Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach
Rawaa Alatrash, Mohamed Amine Chatti, Nasha Wibowo, Qurat Ul Ain
https://arxiv.org/abs/2509.05393

Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach
Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring con…

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…

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li
https://arxiv.org/abs/2509.06531

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities. While large language models offer strong generative reasoning capabilities, their limited exploitation of structural signals often results in structural sparsity and semantic ambiguity, especially under incomplete or zero-shot settings. To address these challenges, we propose SLiNT (Structure-aware Language model with Injection and coNtrastive Training), a modular fram…

An Optimized Pipeline for Automatic Educational Knowledge Graph Construction
Qurat Ul Ain, Mohamed Amine Chatti, Jean Qussa, Amr Shakhshir, Rawaa Alatrash, Shoeb Joarder
https://arxiv.org/abs/2509.05392

An Optimized Pipeline for Automatic Educational Knowledge Graph Construction
The automatic construction of Educational Knowledge Graphs (EduKGs) is essential for domain knowledge modeling by extracting meaningful representations from learning materials. Despite growing interest, identifying a scalable and reliable approach for automatic EduKG generation remains a challenge. In an attempt to develop a unified and robust pipeline for automatic EduKG construction, in this study we propose a pipeline for automatic EduKG construction from PDF learning materials. The process …

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge
Chunhui Gu, Mohammad Sadegh Nasr, James P. Long, Kim-Anh Do, Ehsan Irajizad
https://arxiv.org/abs/2507.05540

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge
Graph Neural Networks (GNNs) often struggle with noisy edges. We propose Latent Space Constrained Graph Neural Networks (LSC-GNN) to incorporate external "clean" links and guide embeddings of a noisy target graph. We train two encoders--one on the full graph (target plus external edges) and another on a regularization graph excluding the target's potentially noisy links--then penalize discrepancies between their latent representations. This constraint steers the model away from overfitting spur…

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
Sofiia Chorna, Kateryna Tarelkina, Elo\"ise Berthier, Gianni Franchi
https://arxiv.org/abs/2507.05810

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that extends these methods into the domain of mechanistic interpretability. Our approach enables a global dissection of model behavior by analyzing how high-level semantic attributes (referred to as concepts) emerge, interact, and propagate through internal model components. Unlike prior work that isolates individual neuro…