Tootfinder

"[T]raditional positions, such as subject or liaison librarian, may feel relatively peripheral, predictable, or routine." 🤨😆
["Notes from the Underground: On being a scholarly communication librarian without ever applying". In: "Scholarly Communication Librarianship and Open Knowledge".

Wasabi Can Be Used as Ecofriendly Preservation Tool for the Ancient Knowledge Contained on Papyrus https://www.goodnewsnetwork.org/wasabi-can-be-used-as-ecofriendly-preservation-tool-for-the-ancient-know…

Wasabi Can Be Used as Ecofriendly Preservation Tool for the Ancient Knowledge Contained on Papyrus
The spicy green goop you get with your sushi can save ancient papyrus manuscripts from fungal contamination.

Deming’s Management System, as expressed in his book: The New Economics has four interdependent parts:
- Appreciation for a System (systems thinking)
- Knowledge about Variation (see: variation definition)
- Theory of Knowledge
- Psychology (the human element of management systems)
...
With, even a fairly simple understanding of the theory of knowledge the effectiveness of management improvement efforts are greatly increased...

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Mengnan Du, Yongfeng Zhang
https://arxiv.org/abs/2403.07311

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link pr…

Sharing special issue love: We invite contributions to discuss the mediation of knowledge practices and planetary matter through data infrastructures.
https://sciencetechnologystudies.journal.fi/announcement/view/689

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Mengnan Du, Yongfeng Zhang
https://arxiv.org/abs/2403.07311

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link pr…

The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework
Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Huajun Chen, Wen Zhang
https://arxiv.org/abs/2403.06832

The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework
The advancement of Multi-modal Pre-training highlights the necessity for a robust Multi-Modal Knowledge Graph (MMKG) representation learning framework. This framework is crucial for integrating structured knowledge into multi-modal Large Language Models (LLMs) at scale, aiming to alleviate issues like knowledge misconceptions and multi-modal hallucinations. In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal K…

Editing Conceptual Knowledge for Large Language Models
Xiaohan Wang, Shengyu Mao, Ningyu Zhang, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen
https://arxiv.org/abs/2403.06259

Editing Conceptual Knowledge for Large Language Models
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, while whether LLMs possess the capability to modify concepts remains unclear. This paper pioneers the investigation of editing conceptual knowledge for LLMs, by constructing a novel benchmark dataset ConceptEdit and establishing a suite of new metrics for evaluation. The experimental results reveal that, although existin…

KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models
Han Huang, Haitian Zhong, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
https://arxiv.org/abs/2403.07350

KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models
Currently, little research has been done on knowledge editing for Large Vision-Language Models (LVLMs). Editing LVLMs faces the challenge of effectively integrating diverse modalities (image and text) while ensuring coherent and contextually relevant modifications. An existing benchmark has three metrics (Reliability, Locality and Generality) to measure knowledge editing for LVLMs. However, the benchmark falls short in the quality of generated images used in evaluation and cannot assess whether…

KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models
Han Huang, Haitian Zhong, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
https://arxiv.org/abs/2403.07350

KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models
Currently, little research has been done on knowledge editing for Large Vision-Language Models (LVLMs). Editing LVLMs faces the challenge of effectively integrating diverse modalities (image and text) while ensuring coherent and contextually relevant modifications. An existing benchmark has three metrics (Reliability, Locality and Generality) to measure knowledge editing for LVLMs. However, the benchmark falls short in the quality of generated images used in evaluation and cannot assess whether…