Tootfinder

Perhaps future work should focus on establishing trusted enclaves of knowledge and expertise.
If you’re a knowledge worker, perhaps sharing your knowledge publicly isn’t the best idea anymore. It’s a real bummer because it makes knowledge a luxury of the elite and wealthy.
Rather than working directly on the thing, you focus on the data that feeds into the machine that works on “the thing”. :\

Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
Mariam Arustashvili, J\"org Deigm\"oller, Heiko Paulheim
https://arxiv.org/abs/2508.13675

Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
Knowledge Graphs are used for various purposes, including business applications, biomedical analyses, or digital twins in industry 4.0. In this paper, we investigate knowledge graphs describing household actions, which are beneficial for controlling household robots and analyzing video footage. In the latter case, the information extracted from videos is notoriously incomplete, and completing the knowledge graph for enhancing the situational picture is essential. In this paper, we show that, wh…

"For Fairer Open Access Deals, Base Article Eligibility on Submission Date" @ Katina Magazine:
https://katinamagazine.org/content/article/open-knowledge/2025/fairer-oa-deals-article-eligibility-submission-date…

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…

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
Wuyang Zhang, Yexin Tian, Xiandong Meng, Mengjie Wang, Junliang Du
https://arxiv.org/abs/2508.14427

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
This paper addresses the problems of missing reasoning chains and insufficient entity-level semantic understanding in large language models when dealing with tasks that require structured knowledge. It proposes a fine-tuning algorithm framework based on knowledge graph injection. The method builds on pretrained language models and introduces structured graph information for auxiliary learning. A graph neural network is used to encode entities and their relations, constructing a graph-based sema…

Efficient Knowledge Graph Unlearning with Zeroth-order Information
Yang Xiao, Ruimeng Ye, Bohan Liu, Xiaolong Ma, Bo Hui
https://arxiv.org/abs/2508.14013 https://

Efficient Knowledge Graph Unlearning with Zeroth-order Information
Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machine unlearning methods have been proposed. In this paper, we firstly present an efficient knowledge graph (KG) unlearning algorithm. We remark that KG unlearning is nontrivial due to the distinctive structure of KG and the semantic relations between entities. Also, unlearning by estimating the influence of removed compo…

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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…

Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method
Suleyman Olcay Polat, Poli A. Nemkova, Mark V. Albert
https://arxiv.org/abs/2508.14783 https:/…

Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method
Model distillation enables the transfer of knowledge from large-scale models to compact student models, facilitating deployment in resource-constrained environments. However, conventional distillation approaches often suffer from computational overhead and limited generalization. We propose a novel adaptive distillation framework that dynamically augments training data in regions of high student model loss. Using UMAP-based dimensionality reduction and nearest neighbor sampling, our method iden…

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Masoud Mansoury, Michael Cochez, Martijn Schut
https://arxiv.org/abs/2508.13663

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we …