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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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Summen der Inbetriebnahmen minus Stilllegungen pro Jahr.
Der Datenbestand enthält ggf. unplausible Datensätze.
Anmerkung: Erster Versuch einer #Datenanalyse. 😁 Hinweise gerne. Wünsc…

Seeing Further on the Shoulders of Giants: Knowledge Inheritance for Vision Foundation Models
Jiabo Huang, Chen Chen, Lingjuan Lyu
https://arxiv.org/abs/2508.14707 https://

Seeing Further on the Shoulders of Giants: Knowledge Inheritance for Vision Foundation Models
Vision foundation models (VFMs) are predominantly developed using data-centric methods. These methods require training on vast amounts of data usually with high-quality labels, which poses a bottleneck for most institutions that lack both large-scale data and high-end GPUs. On the other hand, many open-source vision models have been pretrained on domain-specific data, enabling them to distill and represent core knowledge in a form that is transferable across diverse applications. Even though th…

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…

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 …

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …