Journal of Web Semantics最新文献_第2页

Towards leveraging explicit negative statements in knowledge graph embeddings

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100851

Rita T. Sousa , Catia Pesquita , Heiko Paulheim

{"title":"Towards leveraging explicit negative statements in knowledge graph embeddings","authors":"Rita T. Sousa , Catia Pesquita , Heiko Paulheim","doi":"10.1016/j.websem.2024.100851","DOIUrl":"10.1016/j.websem.2024.100851","url":null,"abstract":"<div><div>Knowledge Graphs are used in various domains to represent knowledge about entities and their relations. In the vast majority of cases, they capture what is known to be true about those entities, i.e., positive statements, while the Open World Assumption implicitly states that everything not expressed in the graph may or may not be true. This makes it difficult and less frequent to capture information explicitly known not to be true, i.e., negative statements. Moreover, while those negative statements could bear the potential to learn more useful representations in knowledge graph embeddings, that direction has been explored only rarely. However, in many domains, negative information is particularly interesting, for example, in recommender systems, where negative associations of users and items can help in learning better user representations, or in the biomedical domain, where the knowledge that a patient does exhibit a specific symptom can be crucial for accurate disease diagnosis.</div><div>In this paper, we argue that negative statements should be given more attention in knowledge graph embeddings. Moreover, we investigate how they can be used in knowledge graph embedding methods, highlighting their potential in some interesting use cases. We discuss some existing works and preliminary results that incorporate explicitly declared negative statements in walk-based knowledge graph embedding methods. Finally, we outline promising avenues for future research in this area.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"84 ","pages":"Article 100851"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the role of knowledge graphs in AI-based scientific discovery

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100854

Mathieu d’Aquin

{"title":"On the role of knowledge graphs in AI-based scientific discovery","authors":"Mathieu d’Aquin","doi":"10.1016/j.websem.2024.100854","DOIUrl":"10.1016/j.websem.2024.100854","url":null,"abstract":"<div><div>Research and the scientific activity are widely seen as an area where the current trends in AI, namely the development of deep learning models (including large language models), are having an increasing impact. Indeed, the ability of such models to extrapolate from data, seemingly finding unknown patterns relating implicit features of the objects under study to their properties can, at the very least, help accelerate and scale up those studies as demonstrated in fields such as molecular biology and chemistry. Knowledge graphs, on the other hand, have more traditionally been used to organize information around the scientific activity, keeping track of existing knowledge, of conducted experiments, of interactions within the research community, etc. However, for machine learning models to be truly used as a tool for scientific advancement, we have to find ways for the knowledge implicitly gained by these models from their training to be integrated with the explicitly represented knowledge captured through knowledge graphs. Based on our experience in ongoing projects in the domain of material science, in this position paper, we discuss the role that knowledge graphs can play in new methodologies for scientific discovery. These methodologies are based on the creation of large and opaque neural models. We therefore focus on the research challenges we need to address to support aligning such neural models to knowledge graphs for them to become a knowledge-level interface to those neural models.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"84 ","pages":"Article 100854"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the legal implications of Large Language Model answers: A prompt engineering approach and a view beyond by exploiting Knowledge Graphs

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100843

George Hannah , Rita T. Sousa , Ioannis Dasoulas , Claudia d’Amato

{"title":"On the legal implications of Large Language Model answers: A prompt engineering approach and a view beyond by exploiting Knowledge Graphs","authors":"George Hannah , Rita T. Sousa , Ioannis Dasoulas , Claudia d’Amato","doi":"10.1016/j.websem.2024.100843","DOIUrl":"10.1016/j.websem.2024.100843","url":null,"abstract":"<div><div>With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response. Nevertheless, there are cases where the LLM recommends actions that have potential legal implications and this may put the user in danger. We provide an empirical analysis on multiple existing LLMs showing the urgency of the problem. Hence, we propose a first short-term solution, consisting in an approach for isolating these legal issues through prompt engineering. We prove that this solution is able to stem some risks related to legal implications, nonetheless we also highlight some limitations. Hence, we argue on the need for additional knowledge-intensive resources and specifically Knowledge Graphs for fully solving these limitations. For the purpose, we draw our proposal aiming at designing and developing a solution powered by a legal Knowledge Graph (KG) that, besides capturing and alerting the user on possible legal implications coming from the LLM answers, is also able to provide actual evidence for them by supplying citations of the interested laws. We conclude with a brief discussion on the issues that may be needed to solve for building a comprehensive legal Knowledge Graph</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"84 ","pages":"Article 100843"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Knowledge Graphs with Symbolic AI: The Path to Interpretable Hybrid AI Systems in Medicine

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2025-01-01 DOI: 10.1016/j.websem.2024.100856

Maria-Esther Vidal , Yashrajsinh Chudasama , Hao Huang , Disha Purohit , Maria Torrente

{"title":"Integrating Knowledge Graphs with Symbolic AI: The Path to Interpretable Hybrid AI Systems in Medicine","authors":"Maria-Esther Vidal , Yashrajsinh Chudasama , Hao Huang , Disha Purohit , Maria Torrente","doi":"10.1016/j.websem.2024.100856","DOIUrl":"10.1016/j.websem.2024.100856","url":null,"abstract":"<div><div>Knowledge Graphs (KGs) are graph-based structures that integrate heterogeneous data, capture domain knowledge, and enable explainable AI through symbolic reasoning. This position paper examines the challenges and research opportunities in integrating KGs with neuro-symbolic AI, highlighting their potential to enhance explainability, scalability, and context-aware reasoning in hybrid AI systems. Using a lung cancer use case, we illustrate how hybrid approaches address tasks such as link prediction—uncovering hidden relationships in medical data—and counterfactual reasoning—analyzing alternative scenarios to understand causal factors. The discussion is framed around TrustKG, which demonstrates how constraint validation, causal reasoning, and user-centric communication can support transparent and reliable decision-making. Additionally, we identify current limitations of KGs, including gaps in knowledge coverage, evolving data integration challenges, and the need for improved usability and impact assessment. These insights are not limited to healthcare but extend to other domains like energy, manufacturing, and mobility, showcasing the broad applicability of KGs. Finally, we propose research directions to unlock their full potential in building robust, transparent, and widely adopted real-world applications.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"84 ","pages":"Article 100856"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM experimentation through knowledge graphs: Towards improved management, repeatability, and verification

IF 2.1 3区计算机科学

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

引用次数: 0

Education in the era of Neurosymbolic AI

IF 2.1 3区计算机科学

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

{"title":"Education in the era of Neurosymbolic AI","authors":"Chris Davis Jaldi , Eleni Ilkou , Noah Schroeder , Cogan Shimizu","doi":"10.1016/j.websem.2024.100857","DOIUrl":"10.1016/j.websem.2024.100857","url":null,"abstract":"<div><div>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 <em>neurosymbolic educational agents</em>. 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.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100857"},"PeriodicalIF":2.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pattern-based engineering of Neurosymbolic AI Systems

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2024-12-27 DOI: 10.1016/j.websem.2024.100855

Fajar J. Ekaputra

引用次数: 0

Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2024-12-24 DOI: 10.1016/j.websem.2024.100844

Ernests Lavrinovics , Russa Biswas , Johannes Bjerva , Katja Hose

{"title":"Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective","authors":"Ernests Lavrinovics , Russa Biswas , Johannes Bjerva , Katja Hose","doi":"10.1016/j.websem.2024.100844","DOIUrl":"10.1016/j.websem.2024.100844","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100844"},"PeriodicalIF":2.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Serendipitous knowledge discovery on the Web of Wisdom based on searching and explaining interesting relations in knowledge graphs

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2024-12-24 DOI: 10.1016/j.websem.2024.100852

Eero Hyvönen

{"title":"Serendipitous knowledge discovery on the Web of Wisdom based on searching and explaining interesting relations in knowledge graphs","authors":"Eero Hyvönen","doi":"10.1016/j.websem.2024.100852","DOIUrl":"10.1016/j.websem.2024.100852","url":null,"abstract":"<div><div>This paper maintains that the Semantic Web is changing into a kind of Web of Wisdom (WoW) where AI-based problem solving, based on symbolic search and sub-symbolic methods, and Information Retrieval (IR) merge: IR is seen as a process for solving information-related problems of the end user with explanations, a form of knowledge discovery. As a case of example, relational search is concerned, i.e., solving problems of the type “How are <span><math><mrow><msub><mrow><mi>X</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>…</mo><msub><mrow><mi>X</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></math></span> related to <span><math><mrow><msub><mrow><mi>Y</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>…</mo><msub><mrow><mi>Y</mi></mrow><mrow><mi>m</mi></mrow></msub></mrow></math></span>?”. For example: how is <em>Pablo Picasso</em> related to <em>Barcelona</em>? The idea is to find explainable “interesting” or even serendipitous associations in Knowledge Graphs (KG) and textual web contents. It is argued that domain knowledge-based symbolic methods based of KGs are needed to complement domain-agnostic graph-based methods and Generative AI (GenAI) boosted by Large Language Models (LLM). By using domain specific knowledge, it is possible to find and explain meaningful reliable textual answers, answer quantitative questions, and use data analyses and visualizations for explaining and studying the relations.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100852"},"PeriodicalIF":2.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KG.GOV: Knowledge graphs as the backbone of data governance in AI

IF 2.1 3区计算机科学

Journal of Web Semantics Pub Date : 2024-12-20 DOI: 10.1016/j.websem.2024.100847

Albert Meroño-Peñuela, Elena Simperl, Anelia Kurteva, Ioannis Reklos

{"title":"KG.GOV: Knowledge graphs as the backbone of data governance in AI","authors":"Albert Meroño-Peñuela, Elena Simperl, Anelia Kurteva, Ioannis Reklos","doi":"10.1016/j.websem.2024.100847","DOIUrl":"10.1016/j.websem.2024.100847","url":null,"abstract":"<div><div>As (generative) Artificial Intelligence continues to evolve, so do the challenges associated with governing the data that powers it. Ensuring data quality, privacy, security, and ethical use become more and more challenging due to the increasing volume and variety of the data, the complexity of AI models, and the rapid pace of technological advancement. Knowledge graphs have the potential to play a significant role in enabling data governance in AI, as we move beyond their traditional use as data organisational systems. To address this, we present <span>KG.gov</span>, a framework that positions KGs at a higher abstraction level within AI workflows, and enables them as a backbone of AI data governance. We illustrate the three dimensions of <span>KG.gov</span>: modelling data, alternative representations, and describing behaviour; and describe the insights and challenges of three use cases implementing them: Croissant, a vocabulary to model and document ML datasets; WikiPrompts, a collaborative KG of prompts and prompt workflows to study their behaviour at scale; and Multimodal transformations, an approach for multimodal KGs harmonisation and completion aiming at broadening access to knowledge.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100847"},"PeriodicalIF":2.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0