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[null,"1.","Record Nr.",null,"UNISA996601563103316"]
[null,null,"Autore",null,"Meroño Peñuela Albert"]
[null,null,"Titolo",null,"The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part II"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-60635-3"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (277 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14665"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Resource -- PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips -- 1 Introduction -- 2 Related Work -- 3 PyGraft Description -- 3.1 Preliminaries -- 3.2 Overview -- 3.3 Schema Generation -- 3.4 Knowledge Graph Generation -- 4 PyGraft in Action -- 4.1 Efficiency and Scalability Details -- 4.2 Usage Illustration -- 5 Discussion -- 5.1 Potential Uses -- 5.2 Limitations, Sustainability, Maintenance and Future Work -- 6 Conclusion -- References -- NORIA-O: An Ontology for Anomaly Detection and Incident Management in ICT Systems -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Competency Questions and Conceptualization -- 3.2 Domain of Discourse and Modeling Strategy -- 4 NORIA-O: Formalization and Implementation -- 4.1 Resources, Network Interfaces, Network Links and Applications -- 4.2 Logs and Alarms -- 4.3 Trouble Tickets and Change Requests -- 4.4 Agents, Teams and Organizations -- 5 Evaluation -- 6 Use Case: Modeling a Complex IT Infrastructure -- 7 Conclusion and Future Work -- References -- FlexRML: A Flexible and Memory Efficient Knowledge Graph Materializer -- 1 Introduction -- 2 Related Work -- 3 FlexRML: Architecture and Implementation -- 3.1 Preprocessing Step -- 3.2 Mapping Step -- 4 Estimation of Generated RDF Elements -- 4.1 Estimation of triplesMaps Without Join -- 4.2 Estimation of triplesMaps with Join -- 4.3 Estimation of All triplesMaps -- 5 Empirical Evaluation -- 5.1 Accuracy of Result Size Estimator -- 5.2 Performance of Adaptive Hash Function Selection -- 5.3 Comparison with Existing RML Processors -- 6 The Resource FlexRML -- 7 Conclusion and Future "]
[{"$nulls":4},"Work -- References -- IICONGRAPH: Improved Iconographic and Iconological Statements in Knowledge Graphs -- 1 Introduction."]
[{"$nulls":4},"2 Background, Problem Statement, and Research Requirements -- 2.1 ICON Ontology 2.0 -- 2.2 HyperReal -- 2.3 Wikidata -- 2.4 ArCo -- 3 IICONGRAPH Development and Release -- 3.1 Wikidata's Conversion -- 3.2 ArCo's Conversion -- 3.3 IICONGRAPH Release -- 4 Quantitative Evaluation -- 5 Research-Based Evaluation -- 6 Discussion of the Results -- 7 Related Work -- 8 Conclusion and Future Work -- References -- VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph -- 1 Introduction -- 2 Related Work -- 3 Unified Access for Integrated Visual Datasets -- 3.1 VisionKG Architecture to Facilitate Unified Access -- 3.2 Linked Datasets and Tasks in VisionKG -- 3.3 Visual Dataset Explorer Powered by SPARQL -- 4 Enforcing FAIR Principles for Visual Datasets -- 4.1 Making Visual Data Assets Findable and Accessible -- 4.2 Ensure Interoperability Across Datasets and Tasks -- 4.3 Enhance Reusability Through a SPARQL Endpoint -- 5 VisionKG: Facilitating Visual Tasks Towards MLOps -- 5.1 Composing Visual Datasets in a Unified Taxonomy -- 5.2 Automating Training and Testing Pipelines -- 5.3 Robust Visual Learning over Diverse Data-Sources -- 6 Conclusions and Future Works -- References -- OfficeGraph: A Knowledge Graph of Office Building IoT Measurements -- 1 Introduction -- 2 Related Work -- 3 Converting the Source Data -- 3.1 Source Data -- 3.2 Original Data Structure -- 3.3 Data Model and Mapping Template -- 3.4 Enrichment -- 4 Description of OfficeGraph -- 4.1 Timepoints -- 4.2 Graph Structure Metrics -- 4.3 Enrichment -- 4.4 Accessing the KG -- 5 Using OfficeGraph -- 5.1 Building Management Data Analytics -- 5.2 Machine Learning on OfficeGraph -- 6 Conclusion -- References -- Musical Meetups Knowledge Graph (MMKG): A Collection of Evidence for Historical Social Network Analysis -- 1 Introduction -- 2 Motivation -- 3 Meetups Ontology."]
[{"$nulls":4},"4 Knowledge Graph Generation Pipeline -- 4.1 Data Collection and Preparation -- 4.2 Entity Recognition -- 4.3 Harmonisation -- 4.4 Knowledge Graph Construction -- 5 MMKG Evaluation -- 5.1 Answering CQs -- 5.2 Feedback Questionnaire from Domain Experts -- 6 Using the MMKG -- 6.1 Music Historians and Domain Experts -- 6.2 Developer Communities -- 7 Resource Availability, Reusability, Sustainability -- 8 Related Work -- 9 Conclusions and Future Work -- References -- Generate and Update Large HDT RDF Knowledge Graphs on Commodity Hardware -- 1 Introduction -- 2 HDT Internals -- 2.1 HDTq -- 3 Related Work -- 4 Contribution -- 4.1 k-HDTCat -- 4.2 k-HDTDiffCat -- 4.3 HDTq Integration -- 5 Experiments -- 5.1 Experiment: Experimentally Determine a Good Value of k in k-HDTCat -- 5.2 Experiment: Comparison with Existing HDT Compression Methods -- 5.3 Experiment: Indexing Wikidata -- 5.4 Experiment: k-HDTDiffCat to Keep Up to Date Datasets -- 6 Code -- 7 Conclusion -- References -- SMW Cloud: A Corpus of Domain-Specific Knowledge Graphs from Semantic MediaWikis -- 1 Introduction -- 2 Methods for Collecting the Corpus -- 2.1 Corpus Provision -- 3 Methods for Analysing the Corpus -- 4 Results and Corpus Statistics -- 4.1 Basic RDF Metrics -- 4.2 Topical Analysis -- 4.3 Ontological Analysis -- 5 Conclusion and Future Work -- 5.1 Limitations -- 5.2 Future Work -- 5.3 Sustainability Plan -- References -- SousLeSens - A Comprehensive Suite for the Industrial Practice of Semantic Knowledge Graphs -- 1 Introduction -- 2 State-of-the-art -- 2.1 Existing Knowledge Engineering Tools -- 2.2 Industrial Feedback -- 3 Specification of SLS -- 3.1 Methodological Foundations -- 3.2 Software Architecture -- 3.3 Tools in SLS -- 4 Validation -- 4.1 Case Studies -- 5 Comparative "]
[{"$nulls":4},"Analysis -- 6 Conclusion -- References -- MLSea: A Semantic Layer for Discoverable Machine Learning."]
[{"$nulls":4},"1 Introduction -- 2 Motivating Examples -- 3 Related Work -- 4 The MLS Ontology and Taxonomies -- 4.1 MLS Development Methodology and Maintenance -- 4.2 MLSO: The Machine Learning Sailor Ontology -- 4.3 MLST: Machine Learning Sailor Taxonomies -- 5 MLSea-KG: Construction, Publication and Usage -- 5.1 Knowledge Graph Construction Process -- 5.2 Use Case Examples -- 6 Impact of the Resource -- 7 Conclusions and Future Work -- References -- Enabling Social Demography Research Using Semantic Technologies -- 1 Introduction -- 2 Related Work -- 3 Knowledge Graph Construction -- 3.1 Ontology Creation -- 3.2 The MIRA Ontology -- 3.3 Ontology Population -- 3.4 The MIRA-KG -- 4 Evaluation -- 4.1 Ontology Evaluation -- 4.2 Knowledge Graph Evaluation -- 5 Conclusions -- References -- SCOOP All the Constraints' Flavours for Your Knowledge Graph -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Preliminaries -- 2.2 Related Work -- 3 Shape Integration Challenges -- 4 SCOOP -- 4.1 Post-adjustment -- 4.2 Equivalences Identification -- 4.3 Integration and Inconsistencies Resolution -- 4.4 Implementation -- 5 Evaluation -- 6 Conclusion -- References -- FidMark: A Fiducial Marker Ontology for Semantically Describing Visual Markers -- 1 Introduction -- 2 Approach and Methodology -- 2.1 Design Goals -- 3 Ontology Design -- 3.1 Properties and Terminologies -- 3.2 Procedures -- 3.3 Usage -- 4 Ontology Validation and Demonstrator -- 5 Conclusion and Future Work -- References -- Author Index."]