Tootfinder

At the Semantic Digital Humanities 2025 Workshop, Jose Maldonado-Rodríguez is presenting "Natural Language Querying for Humanities #KnowledgeGraphs A case study on the GOLEM KG". Main contribution is a bilingual dataset (English-Spanish) specifically designed to evaluate automatic text-to-SPARQL translation systems for GOLEM, a specialized humanities KG.
paper:

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Tom Sheffer, Alon Miron, Yaniv Dover, Ariel Goldstein
https://arxiv.org/abs/2507.22889

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Conversations transform individual knowledge into collective insight, allowing groups of humans and increasingly groups of artificial intelligence (AI) agents to collaboratively solve complex problems. Whether interactions between AI agents can replicate the synergy observed in human discussions remains an open question. To investigate this, we systematically compared four conversational configurations: pairs of large language models (LLM-LLM), trios of LLMs, trios of humans, and mixed human-LL…

DriveQA: Passing the Driving Knowledge Test
Maolin Wei, Wanzhou Liu, Eshed Ohn-Bar
https://arxiv.org/abs/2508.21824 https://arxiv.org/pdf/2508.21824…

DriveQA: Passing the Driving Knowledge Test
If a Large Language Model (LLM) were to take a driving knowledge test today, would it pass? Beyond standard spatial and visual question-answering (QA) tasks on current autonomous driving benchmarks, driving knowledge tests require a complete understanding of all traffic rules, signage, and right-of-way principles. To pass this test, human drivers must discern various edge cases that rarely appear in real-world datasets. In this work, we present DriveQA, an extensive open-source text and vision-…

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Siyuan Li, Ruitong Liu, Yan Wen, Te Sun
https://arxiv.org/abs/2506.23141

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li
https://arxiv.org/abs/2507.23740 …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we exp…

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Yufei Chen, Yao Wang, Haibin Zhang, Tao Gu
https://arxiv.org/abs/2507.23229

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge bases, but this advancement introduces significant privacy risks. Existing privacy attacks on RAG systems can trigger data leakage but often fail to accurately isolate knowledge-base-derived sentences within mixed responses. They also lack robustness when applied across multiple domains. This paper addresses these challenges by presenting a novel black-box attack framework that ex…

This is part of the overall assault on knowledge that the administration is undertaking.
Everything that has made this nation a shining beacon in any way whatsoever is being dismantled
-- with the worst aspects of American history being held up as aspirational.
Please call Congress if you are able.
🛰️🧪

Bug Fixing with Broader Context: Enhancing LLM-Based Program Repair via Layered Knowledge Injection
Ramtin Ehsani, Esteban Parra, Sonia Haiduc, Preetha Chatterjee
https://arxiv.org/abs/2506.24015

Bug Fixing with Broader Context: Enhancing LLM-Based Program Repair via Layered Knowledge Injection
Prompting LLMs with bug-related context (e.g., error messages, stack traces) improves automated program repair, but many bugs still remain unresolved. In real-world projects, developers often rely on broader repository and project-level context beyond the local code to resolve such bugs. In this paper, we investigate how automatically extracting and providing such knowledge can improve LLM-based program repair. We propose a layered knowledge injection framework that incrementally augments LLMs …

Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
Yongsheng Lian
https://arxiv.org/abs/2506.23026 https://

Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
We present Machine Assistant with Reliable Knowledge (MARK), a retrieval-augmented question-answering system designed to support student learning through accurate and contextually grounded responses. The system is built on a retrieval-augmented generation (RAG) framework, which integrates a curated knowledge base to ensure factual consistency. To enhance retrieval effectiveness across diverse question types, we implement a hybrid search strategy that combines dense vector similarity with sparse…

Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
Tingxuan Xu, Jiarui Feng, Justin Melendez, Kaleigh Roberts, Donghong Cai, Mingfang Zhu, Donald Elbert, Yixin Chen, Randall J. Bateman
https://arxiv.org/abs/2508.21238

Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering capabilities. However, their application in scientific research remains constrained by challenges such as hallucinations, limited domain-specific knowledge, and lack of explainability or traceability for the response. Graph-based Retrieval-Augmented Generation (GraphRAG) has emerged as a promising approach to improving …