Journal of Web Semantics最新文献

BPO—A battery production ontology for traceable, transparent, and sustainable electric vehicle batteries bpo -电池生产本体，用于可追溯、透明和可持续的电动汽车电池

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

Journal of Web Semantics

Pub Date : 2026-01-21 DOI: 10.1016/j.websem.2026.100877

Cyrine Soufi, Ali Ayadi, Tedjani Mesbahi, Ahmed Samet, Christophe Lallement

The growing demand for lithium-ion batteries (LIBs) in industries such as electric vehicles (EVs) and renewable energy storage underscores the need for tools that ensure transparent, sustainable, and compliant production processes. This paper presents the Battery Production Ontology (BPO), a comprehensive framework designed to standardize the representation and traceability of LIB production lifecycles. By modeling key aspects such as material flows, energy consumption, carbon emissions, and production activities, the BPO supports environmental impact assessments, supply chain transparency, and process optimization.

The BPO is aligned with existing standards, including the EU’s digital battery passport requirements, ensuring interoperability across diverse systems. Developed using a structured methodology, the ontology underwent rigorous validation. Real-world case studies demonstrated its capacity to model emissions, trace materials, and represent production sequences, while quantitative assessments confirmed its scalability, reasoning efficiency, and accuracy for industrial applications. Additionally, the ontology integrates seamlessly with external standards like BONSAI, FOAF, Schema, and OWL Time, fostering semantic reuse and interoperability.

By addressing the critical need for transparency and sustainability in LIB production, the BPO provides stakeholders with a robust tool to drive the green energy transition and achieve global sustainability goals.

电动汽车（ev）和可再生能源存储等行业对锂离子电池（lib）的需求不断增长，这凸显了对确保透明、可持续和合规生产过程的工具的需求。本文提出了电池生产本体（BPO），这是一个旨在标准化电池生产生命周期的表示和可追溯性的综合框架。通过对物料流、能源消耗、碳排放和生产活动等关键方面进行建模，BPO支持环境影响评估、供应链透明度和流程优化。BPO与现有标准保持一致，包括欧盟的数字电池护照要求，确保了不同系统的互操作性。使用结构化方法开发的本体经过了严格的验证。实际案例研究证明了其模拟排放、痕量物质和表示生产序列的能力，而定量评估证实了其工业应用的可扩展性、推理效率和准确性。此外，本体与外部标准（如BONSAI、FOAF、Schema和OWL Time）无缝集成，促进了语义重用和互操作性。通过解决LIB生产中对透明度和可持续性的关键需求，BPO为利益相关者提供了一个强大的工具，以推动绿色能源转型，实现全球可持续性目标。

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引用次数: 0

Stop writing repetitive code! Scaffolding a semantic data access layer to abstract developers from semantic technologies 停止编写重复的代码！搭建语义数据访问层，将开发人员从语义技术中抽象出来

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

Journal of Web Semantics

Pub Date : 2025-11-13 DOI: 10.1016/j.websem.2025.100874

Herminio García-González , Mike Bryant , Veerle Vanden Daelen

Semantic web technologies are sometimes perceived as difficult to adopt and use, and when developing data-driven applications a significant portion of the overall effort involves the writing of data access code. We propose, therefore, a technique to automatically generate data access layer code from data in semantic formats, abstracting away from developers data querying concerns. We also explore its use inside the EHRI project in order to support cultural heritage institutions in publishing their data in a systematic and sustainable way and we compare it against other libraries covering a similar purpose by means of a subjective analysis and a quantitative performance evaluation. Our results show how their design choices affect the overall usability and ultimately the final performance when integrated in larger applications. As a result, we deliver some recommendations of use for each of the tools depending on the developers’ needs. Thus, we see this work as a first step in exploring the benefits that these libraries can provide in abstracting semantic technologies and, thereby, promoting their adoption for data-driven applications.

语义web技术有时被认为难以采用和使用，并且在开发数据驱动的应用程序时，总体工作的很大一部分涉及编写数据访问代码。因此，我们提出了一种从语义格式的数据中自动生成数据访问层代码的技术，从开发人员的数据查询问题中抽象出来。我们还探索了它在EHRI项目中的使用，以支持文化遗产机构以系统和可持续的方式发布他们的数据，我们通过主观分析和定量绩效评估的方式将其与其他涵盖类似目的的图书馆进行比较。我们的研究结果表明，当集成到大型应用程序中时，他们的设计选择如何影响整体可用性和最终的性能。因此，我们根据开发人员的需要为每个工具提供一些使用建议。因此，我们认为这项工作是探索这些库在抽象语义技术方面所能提供的好处的第一步，从而促进它们在数据驱动应用程序中的采用。

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引用次数: 0

Knowledge prompting: How knowledge engineers use generative AI 知识提示：知识工程师如何使用生成式人工智能

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

Journal of Web Semantics

Pub Date : 2025-10-15 DOI: 10.1016/j.websem.2025.100873

Elisavet Koutsiana, Johanna Walker, Michelle Nwachukwu, Bohui Zhang, Albert Meroño-Peñuela, Elena Simperl

Despite many advances in knowledge engineering (KE), challenges remain in areas such as engineering knowledge graphs (KGs) at scale, automating tasks, and keeping pace with evolving domain knowledge. KE has used NLP demonstrating notable advantages in knowledge-intensive tasks, but the most effective use of generative AI to support knowledge engineers across the KE activities is still in its infancy. To explore how generative AI may enhance KE and change existing KE practices, we conducted a multi-method study during a KE hackathon. We investigated participants’ views on the use of generative AI, the challenges they face, the skills they may need to integrate generative AI into their practices, and how they use generative AI responsibly. We found participants felt LLMs could indeed contribute to improving efficiency when engineering KGs, but presented increased challenges around the already complex issues of evaluating KE task success. We discovered prompting to be a useful but undervalued skill for knowledge engineers working with LLMs, and note that NLP skills may become more relevant across more roles in KE workflows. Integrating generative AI into KE tasks needs to be done with awareness of potential risks and harms. Given the limited ethical training most knowledge engineers receive, solutions such as our proposed ‘KG Cards’ based on Data Cards could be a useful guide for KG construction. Our findings can support designers of KE AI copilots, KE researchers, and practitioners using advanced AI to develop trustworthy applications, propose new methodologies for KE and operate new technologies responsibly.

尽管知识工程（KE）取得了许多进步，但在大规模的工程知识图（KGs）、自动化任务以及与不断发展的领域知识保持同步等领域仍然存在挑战。KE使用NLP在知识密集型任务中显示出显着的优势，但最有效地使用生成人工智能来支持KE活动中的知识工程师仍处于起步阶段。为了探索生成式人工智能如何增强KE并改变现有的KE实践，我们在KE黑客马拉松期间进行了一项多方法研究。我们调查了参与者对使用生成式人工智能的看法，他们面临的挑战，他们将生成式人工智能整合到实践中可能需要的技能，以及他们如何负责任地使用生成式人工智能。我们发现，参与者认为法学硕士确实有助于提高工程kg的效率，但在评估KE任务成功的本已复杂的问题上提出了更大的挑战。我们发现，对于与法学硕士一起工作的知识工程师来说，提示是一种有用但被低估的技能，并注意到NLP技能可能在KE工作流中的更多角色中变得更加相关。将生成式人工智能集成到KE任务中需要意识到潜在的风险和危害。鉴于大多数知识工程师接受的道德培训有限，我们提出的基于数据卡的“KG卡”等解决方案可能是KG建设的有用指南。我们的研究结果可以支持KE AI副驾驶的设计人员、KE研究人员和使用先进AI的从业者开发值得信赖的应用程序，为KE提出新的方法，并负责任地操作新技术。

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引用次数: 0

Proving correctness of the query containment solver SpeCS using SPARQL set semantics 使用SPARQL集合语义证明查询包含求解器规范的正确性

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

Journal of Web Semantics

Pub Date : 2025-08-26 DOI: 10.1016/j.websem.2025.100870

Mirko Spasić , Milena Vujošević Janičić

Solving the sparql query containment problem is of fundamental importance for the verification and optimization of sparql queries. With the increasing popularity of the Semantic Web and its applications, sparql 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.

In this paper, we prove the correctness of SpeCS a highly efficient state-of-the-art query containment solver that supports reasoning about queries containing all commonly used sparql language constructs. We outline set semantics that cover the most common subset of the sparql language and give precise definitions of all fundamental sparql concepts. We briefly discuss the procedure used by SpeCS 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 union operator, the optional 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.

解决sparql查询包含问题对于验证和优化sparql查询至关重要。随着语义Web及其应用程序的日益普及，sparql查询包含解决方案面临着重大挑战：覆盖广泛的语言结构，实现高效率，并保证正确性。虽然语言的覆盖率和效率可以通过相关的基准测试来可靠地评估，但我们需要正式的正确性证明来确保工具的可靠性。在本文中，我们证明了SpeCS的正确性，SpeCS是一个高效的最先进的查询包含求解器，它支持对包含所有常用sparql语言结构的查询进行推理。我们概述了涵盖sparql语言最常见子集的集合语义，并给出了所有基本sparql概念的精确定义。我们将简要讨论SpeCS用于将查询包含问题简化为形式化逻辑框架的过程。我们证明了这个过程对于合取查询以及一些重要的非合取查询类（包含联合操作符、可选操作符和子查询的查询）都是健全和完整的。我们考虑了包容和包容两种形式的健全性和完备性。我们还讨论了由正确性证明驱动的求解器开发的优点。

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引用次数: 0

The Musician’s Context Ontology: Modeling the context for smart musical applications 音乐家的语境本体：为智能音乐应用建模语境

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

Journal of Web Semantics

Pub Date : 2025-08-18 DOI: 10.1016/j.websem.2025.100871

Luca Turchet , Jacopo Tomelleri , Andrea Molinari , Paolo Bouquet

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: https://w3id.org/musico#.

上下文感知计算的范例允许以这样一种方式存储情景和环境信息，即可以轻松且更有意义地对其进行解释。反过来，这种理解被用来预测用户的需求，并主动为他们提供情境感知的内容和体验。虽然上下文感知在计算机科学和物联网学科中得到了广泛的研究，但在处理音乐界面设计的研究社区中，它在很大程度上被忽视了。现有的乐器不具备理解周围环境的能力，即演奏它们的音乐家是谁，正在进行什么音乐活动，以及何时何地。通过上下文感知来增强乐器，有可能使音乐家和音乐内容在各种情况下进行新颖的互动，从单独演奏到团队演奏，从音乐学习到音乐创作。为了实现这种嵌入乐器的智能愿景，有必要围绕它们的用户对环境进行建模。在本文中，我们提出了一个本体来表示与音乐家和音乐活动相关的知识，即“音乐家的上下文本体”（MUSICO），以促进上下文感知音乐应用程序的开发。音乐家背景领域之前没有全面的数据模型，然而，新的本体涉及到几个现有的本体，包括代表音乐物联网生态系统的音乐物联网本体和处理从生产到消费的音乐价值链描述的音乐本体。本文记录了本体的设计及其对从广泛的文献综述和对音乐家的采访中收集的具体要求的评估。通过一个智能手机应用程序演示了本体的实用性，该应用程序可以基于文本和基于内容的音乐查询来搜索音乐家。MUSICO网站可以访问：https://w3id.org/musico#。

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引用次数: 0

Enhancing SPARQL query generation for question answering with a hybrid encoder–decoder and cross-attention model 使用混合编码器-解码器和交叉注意模型增强SPARQL问答查询生成

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

Journal of Web Semantics

Pub Date : 2025-07-30 DOI: 10.1016/j.websem.2025.100869

Yi-Hui Chen , Eric Jui-Lin Lu , Kwan-Ho Cheng

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.

问答（QA）系统对于帮助用户根据他们的查询检索相关和准确的答案至关重要。SPARQL查询语法生成的准确性与所提供答案的准确性直接相关。最近，许多基于知识图的自然语言问答（KGQA）系统的研究利用神经机器翻译（NMT）框架将输入的问题翻译成SPARQL查询语法，这一过程被称为文本到SPARQL。在NMT中，基于交叉注意力的transformer、ConvS2S和BiLSTM模型通常用于训练。然而，比较这些模型的翻译性能是具有挑战性的，因为它们在架构上存在显著差异。为了解决这一问题，本文将各种编码器和交叉注意方法与固定的LSTM解码器集成在一起，形成混合模型，然后在QA系统上进行训练和评估。除了讨论的混合模型之外，本研究还介绍了一种改进的ConvS2S架构，该架构具有多头卷积（MHC）编码器，命名为QAWizer_MHC。MHC编码器结合了Transformer的多头注意机制来计算输入序列中的依赖关系。此外，增强的ConvS2S模型捕获了输入序列中不同接受域的局部隐藏特征。实验结果表明，QAWizer_MHC优于其他模型，在QALD-9和LC-QuAD-1.0数据集上分别获得了76.52%和83.37%的BLEU-1分数。此外，在相同数据集的端到端系统评估中，该模型分别获得了52%和66%的Macro F1分数，超过了其他KGQA系统。实验结果表明，即使在有限的计算资源和一般嵌入的情况下，设计良好的编码器-解码器架构集成了交叉注意，也可以达到与大型预训练模型相当的性能。

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引用次数: 0

Agreeing and disagreeing in collaborative knowledge graph construction: An analysis of Wikidata 协同知识图谱构建中的同意与不同意：基于维基数据的分析

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

Journal of Web Semantics

Pub Date : 2025-06-04 DOI: 10.1016/j.websem.2025.100868

Elisavet Koutsiana, Tushita Yadav, Nitisha Jain, Albert Meroño-Peñuela, Elena Simperl

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.

在这项工作中，我们研究了围绕Wikidata（一个构建维基百科数据后端的在线知识社区）讨论中的分歧。讨论在协作工作中是必不可少的，因为它们可以提高贡献者的绩效，并鼓励共享规范和实践的出现。虽然分歧可以在讨论中发挥富有成效的作用，但它们也可能导致冲突和争议，从而影响贡献者的福祉和他们参与的动机。我们想要了解这种现象是否以及何时在维基数据中出现，使用定量和定性分析的混合方法来确定人们不同意的主题类型、最常见的交互模式以及人们在争论或反对某个问题时所扮演的角色。我们发现创建Wikidata属性的决定比删除属性的决定要快得多，而且超过一半的争议性讨论没有达成共识。我们的分析表明，维基数据是一个包容的社区，在做决定时考虑不同的意见，在讨论中很少出现冲突和破坏行为。与此同时，虽然有四分之一的编辑参与了有争议的讨论，对维基数据出现的问题提出了合理而有见地的意见，但他们只回复了一两个帖子，并没有继续参与讨论以达成共识。我们的工作有助于通过对项目中的沟通和决策的见解，以及方法方向和开放数据集来分析协作KG建设。我们希望我们的发现能够帮助管理者和设计师支持社区决策，并改进讨论工具和实践。

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引用次数: 0

What can knowledge graph do for few-shot named entity recognition 知识图谱对少量的命名实体识别有什么帮助

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

Journal of Web Semantics

Pub Date : 2025-05-22 DOI: 10.1016/j.websem.2025.100866

Binling Nie, Yiming Shao, Yigang Wang

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.

由于其在各个下游领域的广泛适用性，少射命名实体识别（NER）越来越受到关注，特别是在获取足够的标记数据构成重大挑战的领域。最近的研究强调了知识图在增强自然语言处理（NLP）任务中的潜力。然而，对于KGs是否以及如何在低资源条件下有效改善NER性能的全面理解仍然难以捉摸。在本文中，我们首次定量地研究了不同类型的额外KG特征对少射NER的影响。我们通过将额外的KG特征聚合到NER框架中来实现我们的分析。通过大量的实验，我们发现结合类特征可以产生最好的性能。为了充分挖掘来自KGs的类特征的潜力，我们提出了一种新的网络架构KGen，以联合利用来自输入句子侧和标签语义侧的基于KGs的知识来进行少拍NER。通过在五个具有挑战性的数据集上进行大量实验，验证了我们提出的方法的有效性。

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引用次数: 0

MobiRDF: A cloud-based collaborative editing service for mobile RDF data sharing MobiRDF：用于移动RDF数据共享的基于云的协同编辑服务

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

Journal of Web Semantics

Pub Date : 2025-02-17 DOI: 10.1016/j.websem.2025.100864

Nadir Guetmi , Abdessamad Imine , Moulay Driss Mechaoui

In this paper, we present MobiRDF, a novel cloud-based approach designed for the efficient and scalable management of RDF data, enabling real-time sharing and editing. MobiRDF offers two main services: (i) Partial Replication of RDF Graphs: 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. (ii) Collaboration Protocol: 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 MobiRDF 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 MobiRDF is efficient in terms of network bandwidth and energy consumption, validating its effectiveness in real-world scenarios.

在本文中，我们提出了MobiRDF，这是一种新颖的基于云的方法，旨在有效和可扩展地管理RDF数据，实现实时共享和编辑。MobiRDF提供两个主要服务：(i) RDF图的部分复制：该服务促进了移动设备上RDF图的选择性复制，解决了其固有的资源限制。我们的部分图选择器允许只使用用户从RDF图请求的有用数据，而不是存储整个RDF图，从而实现高效的数据存储和检索。（ii）协作协议：该协议以完全分散的方式为协作工作提供同步机制。它使用基于交换性的一致性模型来维护共享RDF图的一致性，确保用户之间的无缝协作。更繁重的计算任务，如动态组管理、同步合并和推理过程，在云中进行管理，优化资源受限的移动设备的性能。MobiRDF的关键新颖之处在于，它能够通过使用封闭世界假设（Closed-World Assumption， CWA）来推断新的三元组，从而确保共享RDF数据的语法和语义一致性。实验评估表明，MobiRDF在网络带宽和能耗方面是高效的，验证了其在实际场景中的有效性。

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引用次数: 0

Two ontology design patterns in the domain of collections 集合领域的两种本体设计模式

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

Journal of Web Semantics

Pub Date : 2025-02-15 DOI: 10.1016/j.websem.2025.100863

Idoia Berges, Arantza Illarramendi

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.

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 Permutation. Moreover, another ODP named ListOfPermutations is also introduced, which allows to represent how the order of a Permutation 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.

集合是一种对象，用于将构成自然组的多个数据项排列成一个单元。不同类型的集合存在不同的约束条件，这些约束条件基于集合是否对其元素施加顺序以及是否允许元素重复。在我们日常生活的多个领域中，都能很容易地发现这些集合。例如，一副扑克牌、一个数字的素除数或参加锦标赛的队伍都可以看作是一个集合。因此，对集合进行有效建模是信息管理中经常出现的问题。在本体设计领域，经常出现的建模问题可以通过使用本体设计模式（ODPs）来解决。就集合而言，已经提出了用于表示序列、列表、集合和包的 ODPs。然而，这些模式都不能完全胜任表示无重复的有序元素集合。在本文中，我们提出了一种用于表示这一概念的 ODP，并将其命名为 Permutation。此外，我们还引入了另一种名为 ListOfPermutations 的 ODP，它可以表示 Permutation 的顺序是如何随时间变化的。由于这些 ODP 所需的所有约束并非都能用 OWL 2 表示，因此在定义时使用了 SHACL 形状。

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引用次数: 0