Journal of Web Semantics最新文献

{"title":"Proving correctness of the query containment solver SpeCS using SPARQL set semantics","authors":"Mirko Spasić ,&nbsp;Milena Vujošević Janičić","doi":"10.1016/j.websem.2025.100870","DOIUrl":"10.1016/j.websem.2025.100870","url":null,"abstract":"<div><div>Solving the <span>sparql</span> query containment problem is of fundamental importance for the verification and optimization of <span>sparql</span> queries. With the increasing popularity of the Semantic Web and its applications, <span>sparql</span> query containment solvers face significant challenges: covering a wide range of language constructs, achieving high efficiency, and guaranteeing correctness. While language coverage and efficiency can be reliably evaluated by testing with relevant benchmarks, we need formal proof of correctness to ensure the trustworthiness of a tool.</div><div>In this paper, we prove the correctness of <span>SpeCS</span> a highly efficient state-of-the-art query containment solver that supports reasoning about queries containing all commonly used <span>sparql</span> language constructs. We outline set semantics that cover the most common subset of the <span>sparql</span> language and give precise definitions of all fundamental <span>sparql</span> concepts. We briefly discuss the procedure used by <span>SpeCS</span> for reducing the query containment problem into a formal logical framework. We prove that this procedure is both sound and complete for conjunctive queries as well as for some important classes of non-conjunctive queries (queries containing the <span>union</span> operator, the <span>optional</span> operator, and subqueries). We consider soundness and completeness in both containment and subsumption forms. We also discuss the advantages of solver development driven by correctness proofs.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"87 ","pages":"Article 100870"},"PeriodicalIF":3.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Musician’s Context Ontology: Modeling the context for smart musical applications","authors":"Luca Turchet ,&nbsp;Jacopo Tomelleri ,&nbsp;Andrea Molinari ,&nbsp;Paolo Bouquet","doi":"10.1016/j.websem.2025.100871","DOIUrl":"10.1016/j.websem.2025.100871","url":null,"abstract":"<div><div>The paradigm of context-aware computing allows storing situational and environmental information in such a way that its interpretation can be done easily and more meaningfully. In turn, this understanding is used to anticipate users’ needs, and proactively provide them with situation-aware content and experiences. Whereas context-awareness has been investigated extensively in the computer science and IoT disciplines, it has been largely overlooked by the research community dealing with musical interfaces design. Existing musical instruments are not equipped with the ability to understand the context around them, namely who is the musician playing them, what musical activity is being conducted, as well as where and when. Enhancing musical instruments with context-awareness has the concrete potential to enable novel kinds of interactions between musicians and musical content in a large variety of situations, from playing alone to playing in a group, from music learning to music composition. To accomplish such a vision of intelligence embedded in musical instruments it is necessary to model the context around their users. In this paper, we present an ontology devised to represent the knowledge related to musicians and musical activities, the “Musician’s Context Ontology” (MUSICO) to facilitate the development of context-aware musical applications. There was no previous comprehensive data model for the domain of musicians’ context, nevertheless, the new ontology relates to several existing ontologies, including the Internet of Musical Things Ontology to represent Internet of Musical Things ecosystems and the Music Ontology that deals with the description of the music value-chain from production to consumption. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review and interviews with musicians. The utility of the ontology is demonstrated by a smartphone application that enables to search for musicians based on both textual and content-based musical queries. MUSICO can be accessed at: <span><span>https://w3id.org/musico#</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"87 ","pages":"Article 100871"},"PeriodicalIF":3.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing SPARQL query generation for question answering with a hybrid encoder–decoder and cross-attention model","authors":"Yi-Hui Chen ,&nbsp;Eric Jui-Lin Lu ,&nbsp;Kwan-Ho Cheng","doi":"10.1016/j.websem.2025.100869","DOIUrl":"10.1016/j.websem.2025.100869","url":null,"abstract":"<div><div>A question-answering (QA) system is essential for helping users retrieve relevant and accurate answers based on their queries. The precision of SPARQL query syntax generation is directly linked to the accuracy of the answers provided. Recently, many studies on knowledge graph-based natural language question-answering (KGQA) systems have leveraged the Neural Machine Translation (NMT) framework to translate input questions into SPARQL query syntax, a process known as Text-to-SPARQL. In NMT, cross-attention-based Transformers, ConvS2S, and BiLSTM models are commonly used for training. However, comparing the translation performance of these models is challenging due to their significant architectural differences. To address this issue, this paper integrates various encoder and cross-attention methods with a fixed LSTM decoder to form hybrid models, which are then trained and evaluated on QA systems. Beyond the hybrid models discussed, this study introduces an improved ConvS2S architecture featuring a Multi-Head Convolutional (MHC) encoder, designated as QAWizer_MHC. The MHC encoder incorporates the Transformer’s multi-head attention mechanism to compute dependencies within the input sequence. Additionally, the enhanced ConvS2S model captures local hidden features across different receptive fields within the input sequence. Experimental results demonstrate that QAWizer_MHC outperforms other models, achieving BLEU-1 scores of 76.52% and 83.37% on the QALD-9 and LC-QuAD-1.0 datasets, respectively. Furthermore, in end-to-end system evaluations on the same datasets, the model attained Macro F1 scores of 52% and 66%, respectively, surpassing other KGQA systems. The experimental findings indicate that even with limited computational resources and general embeddings, a well-designed encoder–decoder architecture that integrates cross-attention can achieve performance comparable to large pre-trained models.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"87 ","pages":"Article 100869"},"PeriodicalIF":3.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agreeing and disagreeing in collaborative knowledge graph construction: An analysis of Wikidata","authors":"Elisavet Koutsiana,&nbsp;Tushita Yadav,&nbsp;Nitisha Jain,&nbsp;Albert Meroño-Peñuela,&nbsp;Elena Simperl","doi":"10.1016/j.websem.2025.100868","DOIUrl":"10.1016/j.websem.2025.100868","url":null,"abstract":"<div><div>In this work, we study disagreements in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are essential in collaborative work as they can increase contributor performance and encourage the emergence of shared norms and practices. While disagreements can play a productive role in discussions, they can also lead to conflicts and controversies, which impact contributor’ well-being and their motivation to engage. We want to understand if and when such phenomena arise in Wikidata, using a mix of quantitative and qualitative analyses to identify the types of topics people disagree about, the most common patterns of interaction, and roles people play when arguing for or against an issue. We find that decisions to create Wikidata properties are much faster than those to delete properties and that more than half of controversial discussions do not lead to consensus. Our analysis suggests that Wikidata is an inclusive community, considering different opinions when making decisions, and that conflict and vandalism are rare in discussions. At the same time, while one-fourth of the editors participating in controversial discussions contribute legitimate and insightful opinions about Wikidata’s emerging issues, they respond with one or two posts and do not remain engaged in the discussions to reach consensus. Our work contributes to the analysis of collaborative KG construction with insights about communication and decision-making in projects, as well as with methodological directions and open datasets. We hope our findings will help managers and designers support community decision-making and improve discussion tools and practices.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"86 ","pages":"Article 100868"},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What can knowledge graph do for few-shot named entity recognition","authors":"Binling Nie,&nbsp;Yiming Shao,&nbsp;Yigang Wang","doi":"10.1016/j.websem.2025.100866","DOIUrl":"10.1016/j.websem.2025.100866","url":null,"abstract":"<div><div>Due to its extensive applicability in various downstream domains, few-shot named entity recognition (NER) has attracted increasing attention, particularly in areas where acquiring sufficient labeled data poses a significant challenge. Recent studies have highlighted the potential of knowledge graphs (KGs) in enhancing natural language processing (NLP) tasks. However, a comprehensive understanding of whether and how KGs can effectively improve the NER performance under low-resource conditions remains elusive. In this paper, for the first time, we quantitatively investigate the effects of different kinds of extra KG features for few-shot NER. We enable our analysis by aggregating extra KG features into an NER framework. Through extensive experiments, we find that incorporating class features yields the best performance. To fully explore the potential of class features from KGs, we propose a novel network architecture, named KGen, to jointly leverage KG-based knowledge from both the input sentence side and the label semantic side for few-shot NER.The efficacy of our proposed method is validated through extensive experiments on five challenging datasets.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"86 ","pages":"Article 100866"},"PeriodicalIF":2.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MobiRDF: A cloud-based collaborative editing service for mobile RDF data sharing","authors":"Nadir Guetmi ,&nbsp;Abdessamad Imine ,&nbsp;Moulay Driss Mechaoui","doi":"10.1016/j.websem.2025.100864","DOIUrl":"10.1016/j.websem.2025.100864","url":null,"abstract":"<div><div>In this paper, we present <span>MobiRDF</span>, a novel cloud-based approach designed for the efficient and scalable management of RDF data, enabling real-time sharing and editing. <span>MobiRDF</span> offers two main services: <em>(i) Partial Replication of RDF Graphs</em>: This service facilitates the selective replication of RDF graphs on mobile devices, addressing their inherent resource limitations. Our partial graph selector allows using only the useful data requested by the user from the RDF graph instead of storing the entire RDF graph, which enables efficient data storage and retrieval. <em>(ii) Collaboration Protocol</em>: This protocol provides synchronization mechanisms for collaborative work in a fully decentralized manner. It uses commutativity-based consistency model to maintain the consistency of the shared RDF graph, ensuring seamless collaboration among users. The heavier computational tasks, such as dynamic group management, synchronization merging, and reasoning processes, are managed in the Cloud, optimizing the performance of resource-constrained mobile devices. The key novelty of <span>MobiRDF</span> is its ability to ensure both syntactic and semantic consistency of shared RDF data, through reasoning processes using the Closed-World Assumption (CWA) for inferring new triples. Experimental evaluations show that <span>MobiRDF</span> is efficient in terms of network bandwidth and energy consumption, validating its effectiveness in real-world scenarios.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"86 ","pages":"Article 100864"},"PeriodicalIF":2.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463946","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}

{"title":"Two ontology design patterns in the domain of collections","authors":"Idoia Berges,&nbsp;Arantza Illarramendi","doi":"10.1016/j.websem.2025.100863","DOIUrl":"10.1016/j.websem.2025.100863","url":null,"abstract":"<div><div>Collections are objects used to arrange, into a single unit, multiple data items that form a natural group. Different types of collections exist, due to different constraints based on whether or not they impose an order on their elements and whether or not they allow repetition of elements. Any of them are easily found in several domains of our everyday life. For instance, a deck of cards, the prime divisors of a number or the teams that compete in a championship can be seen as a collection. Thus, an effective modeling of collections is a recurring issue in information management.</div><div>In the ontology design field, recurring modeling problems can be addressed by the use of Ontology Design Patterns (ODPs). In the case of collections, ODPs have been proposed for representing sequences, lists, sets and bags. However, none of these patterns are completely adequate for representing collections of ordered elements without repetition. In this paper we present an ODP for representing that notion, which we have named <em>Permutation</em>. Moreover, another ODP named <em>ListOfPermutations</em> is also introduced, which allows to represent how the order of a <em>Permutation</em> varies along time. Because not all constraints required by these ODPs can be represented in OWL 2, SHACL shapes have been used in their definitions.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100863"},"PeriodicalIF":2.1,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445381","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}

{"title":"Accelerating knowledge graph and ontology engineering with large language models","authors":"Cogan Shimizu ,&nbsp;Pascal Hitzler","doi":"10.1016/j.websem.2025.100862","DOIUrl":"10.1016/j.websem.2025.100862","url":null,"abstract":"<div><div>Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular approaches to ontologies will be of central importance.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100862"},"PeriodicalIF":2.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419299","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}

{"title":"Logic Augmented Generation","authors":"Aldo Gangemi ,&nbsp;Andrea Giovanni Nuzzolese","doi":"10.1016/j.websem.2024.100859","DOIUrl":"10.1016/j.websem.2024.100859","url":null,"abstract":"<div><div>Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) may overcome those limitations, making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are hardly interpretable and often unreliable. To take the best out of LLMs and SKGs, we envision Logic Augmented Generation (LAG) to combine the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. LAG uses SKGs to inject a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100859"},"PeriodicalIF":2.1,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165568","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}

{"title":"Knowledge Graphs as a source of trust for LLM-powered enterprise question answering","authors":"Juan Sequeda,&nbsp;Dean Allemang,&nbsp;Bryon Jacob","doi":"10.1016/j.websem.2024.100858","DOIUrl":"10.1016/j.websem.2024.100858","url":null,"abstract":"<div><div>Generative AI provides an innovative and exciting way to manage knowledge and data at any scale; for small projects, at the enterprise level, and even at a world wide web scale. It is tempting to think that Generative AI has made other knowledge-based technologies obsolete; that anything we wanted to do with knowledge-based systems, Knowledge Graphs or even expert systems can instead be done with Generative AI. Our position is counter to that conclusion.</div><div>Our practical experience on implementing enterprise question answering systems using Generative AI has shown that Knowledge Graphs support this infrastructure in multiple ways: they provide a formal framework to evaluate the validity of a query generated by an LLM, serve as a foundation for explaining results, and offer access to governed and trusted data. In this position paper, we share our experience, present industry needs, and outline the opportunities for future research contributions.</div></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":"85 ","pages":"Article 100858"},"PeriodicalIF":2.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165567","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}