PUMA publications for /group/researchcode/SemanticWebMon Mar 30 16:58:47 CEST 2026J Inform Technol Construction (ITcon)1208--1243A review of the application of ontologies and semantic web for building information modelling and digital twins based construction management302025metadata digitalTwin ontologie building semanticWeb Fri Jul 26 09:27:23 CEST 2024Data Science JournalHarmonizing GCW Cryosphere Vocabularies with ENVO and SWEET. Towards a General Model for Semantic Harmonization232024metadata ontologie interoperability semanticWeb Wed Jun 19 15:02:54 CEST 2024New York, NY, USAACM Comput. Surv.095Large-scale Semantic Integration of Linked Data: A Survey522019metadata linkedData semanticWeb integration A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,<?brk?>(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.Wed Jun 19 14:53:15 CEST 2024Journal of Intelligent Information Systems2233--259A survey on question answering systems over linked data and documents552020metadata linkedData semanticWeb Question Answering (QA) systems aim at supplying precise answers to questions, posed by users in a natural language form. They are used in a wide range of application areas, from bio-medicine to tourism. Their underlying knowledge source can be structured data (e.g. RDF graphs and SQL databases), unstructured data in the form of plain text (e.g. textual excerpts from Wikipedia), or combinations of the above. In this paper we survey the recent work that has been done in the area of stateless QA systems with emphasis on methods that have been applied in RDF and Linked Data, documents, and mixtures of these. We identify the main challenges, we categorize the existing approaches according to various aspects, we review 21 recent systems, and 23 evaluation and training datasets that are most commonly used in the literature categorized according to the type of the domain, the underlying knowledge source, the provided tasks, and the associated evaluation metrics.Wed Jun 19 08:47:39 CEST 2024New York, NY, USAACM Comput. Surv.0481–35Semantic Data Integration and Querying: A Survey and Challenges562024forschungsdaten metadata interoperability semanticWeb integration 26Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.Wed Jan 24 12:08:23 CET 2024DRAFT: Developing and implementing the semantic interoperability recommendations of the EOSC Interoperability Framework2024standard forschungsdaten interoperability eosc semanticWeb Fri Aug 18 12:07:57 CEST 2023Semantic Web2006metadata semanticWeb Fri Aug 18 12:06:13 CEST 2023Journal of Automated Reasoning103245--269HermiT: An OWL 2 Reasoner532014metadata reasoning semanticWeb hermit 01This system description paper introduces the OWL 2 reasoner HermiT. The reasoner is fully compliant with the OWL 2 Direct Semantics as standardised by the World Wide Web Consortium (W3C). HermiT is based on the hypertableau calculus, and it supports a wide range of standard and novel optimisations that improve the performance of reasoning on real-world ontologies. Apart from the standard OWL 2 reasoning task of entailment checking, HermiT supports several specialised reasoning services such as class and property classification, as well as a range of features outside the OWL 2 standard such as DL-safe rules, SPARQL queries, and description graphs. We discuss the system's architecture, and we present an overview of the techniques used to support the mentioned reasoning tasks. We further compare the performance of reasoning in HermiT with that of FaCT++ and Pellet---two other popular and widely used OWL 2 reasoners.Fri Aug 18 12:05:07 CEST 2023Berkeley, CAOntologies with Python: Programming OWL 2.0 Ontologies with Python and Owlready2157--185Automatic reasoning2021metadata reasoning python semanticWeb hermit In this chapter, we will see how to use the HermiT and Pellet reasoners in Python, in order to check the consistency of an ontology and to perform automatic deductions and classification, on the basis of the logical constructors.Thu Aug 03 07:54:08 CEST 2023Complex {\&} Intelligent Systems0211059--1095Scholarly knowledge graphs through structuring scholarly communication: a review92023metadata knowledgegraph semanticWeb publications 01The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.Wed Dec 08 14:40:12 CET 2021D2.5 FAIR Semantics Recommendations Second Iteration2020forschungsdaten metadata ontologie recommendation semanticWeb