Journal of Web Semantics最新文献

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IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

IF 3.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2025-01-01

引用次数: 0

LLM experimentation through knowledge graphs: Towards improved management, repeatability, and verification 通过知识图谱进行法学硕士实验：改进管理、可重复性和可验证性

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2024-12-31 DOI: 10.1016/j.websem.2024.100853

John S. Erickson , Henrique Santos , Vládia Pinheiro , Jamie P. McCusker , Deborah L. McGuinness

Generative large language models (LLMs) have transformed AI by enabling rapid, human-like text generation, but they face challenges, including managing inaccurate information generation. Strategies such as prompt engineering, Retrieval-Augmented Generation (RAG), and incorporating domain-specific Knowledge Graphs (KGs) aim to address their issues. However, challenges remain in achieving the desired levels of management, repeatability, and verification of experiments, especially for developers using closed-access LLMs via web APIs, complicating integration with external tools. To tackle this, we are exploring a software architecture to enhance LLM workflows by prioritizing flexibility and traceability while promoting more accurate and explainable outputs. We describe our approach and provide a nutrition case study demonstrating its ability to integrate LLMs with RAG and KGs for more robust AI solutions.

生成式大型语言模型（llm）通过实现快速的、类似人类的文本生成，改变了人工智能，但它们面临着挑战，包括管理不准确的信息生成。诸如快速工程、检索增强生成（RAG）和结合领域特定知识图（KGs）等策略旨在解决这些问题。然而，在实现期望的管理水平、可重复性和实验验证方面仍然存在挑战，特别是对于通过web api使用封闭访问llm的开发人员来说，这使得与外部工具的集成变得复杂。为了解决这个问题，我们正在探索一种软件架构，通过优先考虑灵活性和可追溯性来增强LLM工作流，同时促进更准确和可解释的输出。我们描述了我们的方法，并提供了一个营养案例研究，展示了其将法学硕士与RAG和kg集成在一起的能力，以实现更强大的人工智能解决方案。

引用次数: 0

Education in the era of Neurosymbolic AI 神经符号人工智能时代的教育

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2024-12-30 DOI: 10.1016/j.websem.2024.100857

Chris Davis Jaldi , Eleni Ilkou , Noah Schroeder , Cogan Shimizu

Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. NAI-powered education systems will be capable of interpreting complex human concepts and contexts while employing advanced problem-solving strategies, all grounded in established pedagogical frameworks. In this paper, we propose a system that leverages the unique affordances of KGs, LLMs, and pedagogical agents – embodied characters designed to enhance learning – as critical components of a hybrid NAI architecture. We discuss the rationale for our system design and the preliminary findings of our work. We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills. This is an era that will explore a new depth of understanding in educational tools.

随着神经符号人工智能（NAI）的出现，教育即将发生革命性的转变，这将重新定义我们如何支持深度适应和个性化的学习体验。知识图（KGs）与大型语言模型（llm）的集成是一种重要而流行的NAI形式，它为通过神经符号教育代理推进个性化教学提供了一条有前途的途径。通过利用结构化知识，这些智能体可以提供个性化的学习体验，与特定的学习者偏好和期望的学习路径保持一致，同时还可以减轻传统人工智能系统固有的偏见。ai驱动的教育系统将能够解释复杂的人类概念和背景，同时采用先进的解决问题的策略，所有这些都以既定的教学框架为基础。在本文中，我们提出了一个系统，该系统利用了KGs， llm和教学代理（旨在增强学习的具体化字符）的独特功能，作为混合NAI架构的关键组件。我们讨论了我们系统设计的基本原理和我们工作的初步发现。我们的结论是，人工智能时代的教育将使学习更容易获得、更公平，并与现实世界的技能保持一致。这是一个将在教育工具中探索新的理解深度的时代。

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引用次数: 0

Pattern-based engineering of Neurosymbolic AI Systems 基于模式的神经符号人工智能系统工程

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2024-12-27 DOI: 10.1016/j.websem.2024.100855

Fajar J. Ekaputra

The symbiotic combination of sub-symbolic and symbolic AI techniques is a significant trend in AI, leading to the fast-paced development of various techniques that integrate these paradigms to build intelligent systems. However, the wealth of heterogeneous architectural options for combining the paradigms into Neurosymbolic AI (NeSy-AI) systems poses significant challenges. In particular, there is currently no standardized way to design, engineer, and document such systems that encompass visual and formal notations. Existing works aim to address this challenge by systematically modelling NeSy-AI systems as design patterns that include process, data, and human interactions. However, these works focus on capturing specific views of the system rather than aiming to support the broad process of AI system engineering. This paper outlines a vision of pattern-based AI Systems engineering, aiming to support the engineering process of NeSy-AI systems with tasks such as system documentation and artefact generation through interlinked visual and formal notations with Knowledge Graphs at its core.

子符号和符号人工智能技术的共生组合是人工智能的一个重要趋势，导致各种技术的快速发展，整合这些范式来构建智能系统。然而，将这些范式结合到神经符号人工智能（NeSy-AI）系统中的丰富的异构架构选项带来了重大挑战。特别是，目前还没有标准化的方法来设计、设计和记录这种包含可视和形式化符号的系统。现有的工作旨在通过系统地将NeSy-AI系统建模为包括过程、数据和人类交互的设计模式来解决这一挑战。然而，这些工作侧重于捕获系统的特定视图，而不是旨在支持AI系统工程的广泛过程。本文概述了基于模式的人工智能系统工程的愿景，旨在支持NeSy-AI系统的工程过程，其任务包括系统文档和人工制品生成，通过以知识图为核心的相互关联的可视化和形式化符号。

引用次数: 0

Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective 知识图谱、大型语言模型和幻觉：一个NLP的视角

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics

Pub Date : 2024-12-24 DOI: 10.1016/j.websem.2024.100844

Ernests Lavrinovics , Russa Biswas , Johannes Bjerva , Katja Hose

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This undermines trust and limits the applicability of LLMs in different domains. Knowledge Graphs (KGs), on the other hand, provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges). In recent research, KGs have been leveraged to provide context that can fill gaps in an LLM’s understanding of certain topics offering a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy while benefiting from their wide applicability. Nonetheless, it is still a very active area of research with various unresolved open problems. In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations. In our discussion, we consider the current use of KGs in LLM systems and identify future directions within each of these challenges.

大型语言模型（llm）已经彻底改变了基于自然语言处理（NLP）的应用程序，包括自动文本生成、问答、聊天机器人等。然而，他们面临着一个重大挑战：幻觉，即模型产生看似合理但实际上不正确的反应。这破坏了信任，限制了法学硕士在不同领域的适用性。另一方面，知识图（Knowledge Graphs, KGs）提供了一个相互关联的事实的结构化集合，表示为实体（节点）及其关系（边）。在最近的研究中，KGs已被利用来提供背景，可以填补法学硕士对某些主题的理解空白，提供了一种有希望的方法来减轻法学硕士的幻觉，提高其可靠性和准确性，同时受益于其广泛的适用性。尽管如此，它仍然是一个非常活跃的研究领域，有各种尚未解决的开放问题。在本文中，我们讨论了这些开放的挑战，涵盖了最先进的数据集和基准，以及知识整合和评估幻觉的方法。在我们的讨论中，我们考虑了目前KGs在LLM系统中的使用情况，并确定了这些挑战的未来方向。

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