Tootfinder

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…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
https://arxiv.org/abs/2509.07133 https://…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These …

Difference Views for Visual Graph Query Building
Benedikt Kantz, Stefan Lengauer, Peter Waldert, Tobias Schreck
https://arxiv.org/abs/2508.05314 https://ar…

Difference Views for Visual Graph Query Building
Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing visual query builders enable non-expert users to construct SPARQL queries and utilize the knowledge contained in these graphs. Query building is, however, an iterative and, often, visual process where the question of the user can change and differ throughout the p…

In today's ISE 2025 lecture,, we will introduce SPARQL as a query language for knowledge graphs. Again, I'm trying out 'Dystopian Novels' as example knowledge graph playground. Let's see, if the students might know any of them. Wtat do you think? ;-)
#dystopia #literature

SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip
Ziteng Hu, Yingjie Xia, Xiyuan Chen, Li Kuang
https://arxiv.org/abs/2508.12910

SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip
Finite State Machines (FSMs) play a critical role in implementing control logic for Systems-on-Chip (SoC). Traditionally, FSMs are implemented by hardware engineers through Verilog coding, which is often tedious and time-consuming. Recently, with the remarkable progress of Large Language Models (LLMs) in code generation, LLMs have been increasingly explored for automating Verilog code generation. However, LLM-generated Verilog code often suffers from security vulnerabilities, which is particula…

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
https://arxiv.org/abs/2506.10347

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with S…