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The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part II
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part II
Autore Meroño Peñuela Albert
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (277 pages)
Altri autori (Persone) DimouAnastasia
TroncyRaphaël
HartigOlaf
AcostaMaribel
AlamMehwish
PaulheimHeiko
LisenaPasquale
Collana Lecture Notes in Computer Science Series
ISBN 3-031-60635-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Work -- References -- IICONGRAPH: Improved Iconographic and Iconological Statements in Knowledge Graphs -- 1 Introduction.
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.
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 Analysis -- 6 Conclusion -- References -- MLSea: A Semantic Layer for Discoverable Machine Learning.
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.
Record Nr. UNINA-9910861092003321
Meroño Peñuela Albert  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part I
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part I
Autore Meroño Peñuela Albert
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (0 pages)
Altri autori (Persone) DimouAnastasia
TroncyRaphaël
HartigOlaf
AcostaMaribel
AlamMehwish
PaulheimHeiko
LisenaPasquale
Collana Lecture Notes in Computer Science Series
ISBN 3-031-60626-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Research -- Do Similar Entities Have Similar Embeddings? -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Towards a Graph-Based Notion of Similarity -- 3.2 Embedding vs. Graph: A Different Notion of Similarity? -- 4 Experiments -- 4.1 Knowledge Graph Embedding Models -- 4.2 Datasets -- 5 Results and Discussion -- 5.1 Different Notions of Similarity (RQ1) -- 5.2 Correlation Between Rank-Based Metrics and RBO (RQ2) -- 5.3 Analyzing Predicate Importance (RQ3) -- 6 Conclusion and Outlook -- References -- Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Semantic-Enhanced Approaches -- 2.2 Loss Functions for the Link Prediction Task -- 3 Signature-Driven Loss Functions -- 4 Experimental Setting -- 4.1 Evaluation Metrics -- 4.2 Datasets and Models -- 4.3 Implementation Details -- 5 Results -- 5.1 Global Performance -- 5.2 Ablation Study -- 6 Discussion -- 6.1 Treating Different Negatives Differently (RQ1) -- 6.2 Impact of Signature-Driven Losses on Performance (RQ2) -- 6.3 On the Evaluation of KGEM Predictions for LP -- 7 Conclusion -- A Modified Versions of LSBCEL and LSPLL -- B Bucket Analysis -- References -- QAGCN: Answering Multi-relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 QAGCN - A Question-Aware GCN-Based Question Answering Model -- 3.1 Task Definition -- 3.2 The Question-Aware GCN -- 4 Experiments -- 4.1 Effectiveness Evaluation -- 4.2 Comparison with SOTA Reasoning-Based Method - NSM -- 4.3 Efficiency Evaluation -- 4.4 Ablation Study -- 4.5 Case Study with Embedding Visualization -- 5 Conclusion and Outlook -- References.
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs -- 1 Introduction -- 2 Preliminaries -- 3 The Framework -- 4 Experiments -- 4.1 The Datasets -- 4.2 Evaluation Metrics -- 4.3 Experimental Setup -- 4.4 Transductive Time Interval Prediction -- 4.5 Inductive Time Interval Prediction -- 4.6 Fine-Grained Analysis -- 5 Related Work -- 6 Conclusion -- 7 Appendix -- 7.1 Effect of Number of Negative Samples -- 7.2 Evaluation Terminology -- 7.3 Variance Analysis -- References -- A Language Model Based Framework for New Concept Placement in Ontologies -- 1 Introduction -- 2 Related Work -- 2.1 Ontology Concept Placement -- 2.2 Pre-trained Language Models for Ontology Concept Placement -- 3 Problem Statement -- 4 Methodology -- 4.1 Edge Search: Searching Seed Concepts or Edges -- 4.2 Edge Formation and Enrichment -- 4.3 Edge Selection -- 5 Experiments -- 5.1 Data Construction -- 5.2 Metrics -- 5.3 Experimental Settings and Baseline Methods -- 5.4 Results -- 5.5 Ablation Studies -- 5.6 Conclusion and Future Studies -- References -- Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 3.1 Knowledge Graph Embedding (KGE) -- 3.2 Riemannian Geometry -- 3.3 Extrapolation -- 3.4 Composition Patterns -- 4 Method: HolmE -- 4.1 HolmE: Holomorphic KG Embedding -- 4.2 Model Analysis -- 5 Experiments -- 5.1 Overall Experiment Design -- 5.2 Extrapolation to Unseen Triples -- 5.3 Extrapolation to Under-Represented Entities -- 5.4 Extrapolation to Under-Represented Relations -- 6 Conclusion -- A Result Details -- References -- SC-Block: Supervised Contrastive Blocking Within Entity Resolution Pipelines -- 1 Introduction -- 2 WDC-Block: A Large Product Blocking Benchmark.
3 SC-Block: Supervised Contrastive Blocking -- 4 Blocking-Only Evaluation -- 4.1 Baseline Blocking Methods -- 4.2 Implementation -- 4.3 Results for Fixed K -- 4.4 Results for 99.5% Recall on Validation Set -- 5 Evaluation Within Entity Resolution Pipelines -- 5.1 F1 Score and Runtime -- 5.2 Impact of Training Time -- 6 Related Work -- 7 Conclusion -- A Appendix - Additional Experiments -- References -- Navigating Ontology Development with Large Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Ontology Engineering -- 2.2 Ontology Learning and Generation -- 2.3 LLMs for Code Generation -- 3 Methodology -- 3.1 Large Language Models -- 3.2 Prompting Methods -- 3.3 Experimental Setup -- 4 Experimental Results -- 4.1 Initial Experiment Results -- 4.2 Main Experiment Results -- 5 Discussion -- 6 Conclusions and Future Work -- References -- ESLM: Improving Entity Summarization by Leveraging Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Entity Summarization -- 2.2 Contextual Language Models -- 3 Approach -- 3.1 Problem Statement -- 3.2 ESLM Model -- 3.3 ESLM Model Enrichment -- 3.4 Entity Summarization -- 4 Experimental Setup -- 4.1 Baselines -- 4.2 Datasets -- 4.3 Experimental Settings -- 5 Results and Analysis -- 5.1 Comparison with State-of-the-Art Approaches -- 5.2 Example Findings -- 5.3 Ablation Study -- 5.4 Computational Requirements and Efficiency of ESLM -- 6 Conclusion and Future Work -- References -- Explanation of Link Predictions on Knowledge Graphs via Levelwise Filtering and Graph Summarization -- 1 Introduction -- 2 Fundamentals -- 2.1 Knowledge Graphs and Embedding Models -- 2.2 Quotient Graph -- 3 Related Works -- 4 The Proposed Approach -- 4.1 Kelpie -- 4.2 Injecting Semantics in the Pre-Filter -- 4.3 Injecting Semantics in the Explanation Builder -- 5 Experimental Evaluation -- 5.1 Experimental Setting.
5.2 Quantitative Evaluation -- 5.3 Qualitative Evaluation -- 6 Conclusions -- A Appendix: Repair of Inconsistent and Unsatisfiable Ontologies -- B Appendix: Hyper-parameters -- References -- Large Language Models for Scientific Question Answering: An Extensive Analysis of the SciQA Benchmark -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Dataset -- 3.2 Approaches -- 4 Methodology -- 4.1 Experiments -- 4.2 Experimental Setup -- 5 Results and Discussion -- 6 Error Analysis -- 7 Limitations -- 8 Conclusions -- A Appendix - Examples -- References -- Efficient Evaluation of Conjunctive Regular Path Queries Using Multi-way Joins -- 1 Introduction -- 2 Preliminaries -- 2.1 RDF and SPARQL -- 2.2 Worst-Case Optimal Multi-way Joins -- 2.3 GenericJoinCRPQ -- 3 Related Work -- 4 Evaluating C2RPQs with Multi-way Joins -- 4.1 Evaluation of RPQs -- 4.2 Evaluation of C2RPQs -- 4.3 Query Planning and Optimizations -- 5 Experimental Results -- 5.1 Systems and Experimental Setup -- 5.2 Datasets and Queries -- 5.3 Evaluation of Execution Strategies -- 5.4 Comparison with Existing Solutions -- 6 Conclusion and Future Work -- References -- Can Contrastive Learning Refine Embeddings -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 2.1 Contrastive Learning -- 2.2 Problem Statement -- 3 Method -- 3.1 Contrastive Learning Limitation -- 3.2 SimSkip Details -- 3.3 Data Augmentation -- 3.4 Theoretical Proof -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 SimSkip for Federated Knowledge Graph Embedding -- 4.3 SimSkip for Image Embedding -- 4.4 SimSkip for Node embedding learned by Supervised Learning -- 4.5 SimSkip for Transformer-based Text Embedding -- 4.6 Ablation Study -- 5 Related Work -- 5.1 Representation Learning -- 5.2 Contrastive Learning -- 6 Conclusion -- References -- In-Use.
Automation of Electronic Invoice Validation Using Knowledge Graph Technologies -- 1 Introduction -- 2 Preliminaries -- 3 Our Approach -- 3.1 EDIFACT Ontology -- 3.2 The EDIFACT-VAL Tool -- 4 Evaluation -- 4.1 Experimental Set up -- 4.2 Results of the Ontology Evaluation -- 4.3 Completeness of the Invoice KG Generated by EDIFACT-Val -- 4.4 Results of Invoice Validation with EDIFACT-Val -- 4.5 Runtime Performance of EDIFACT-Val -- 4.6 In-Use Evaluation -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Towards Cyber Mapping the German Financial System with Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Knowledge Graph Construction -- 3.1 Cyber Mapping Ontology -- 3.2 Structured Data: Financial Domain -- 3.3 Unstructured Data: Fund Prospectus -- 3.4 Provenance Information -- 4 Knowledge Graph Application -- 5 User Study -- 5.1 Setup -- 5.2 Results -- 6 Conclusion and Outlook -- References -- Integrating Domain Knowledge for Enhanced Concept Model Explainability in Plant Disease Classification -- 1 Introduction -- 2 Use Cases -- 3 Related Work -- 4 Methods -- 4.1 Ontology Development (Ontology Based Explanation) -- 4.2 Synthetic Concepts Images Generation -- 4.3 Testing with Concept Activation Vectors (TCAV) -- 5 Experiments and Results -- 5.1 Dataset and Trained Model -- 5.2 Experimental Setup -- 5.3 Findings and Analyses -- 6 Conclusion -- References -- Generative Expression Constrained Knowledge-Based Decoding for Open Data -- 1 Introduction -- 2 Related Work -- 3 Application -- 3.1 Generative Models -- 4 Evaluation -- 4.1 Quantitative Results -- 4.2 Qualitative User Testing -- 5 Impact and Deployment -- 6 Conclusions and Future Work -- A S-expression functions considered in GECKO -- References -- OntoEditor: Real-Time Collaboration via Distributed Version Control for Ontology Development -- 1 Introduction -- 2 Motivation.
3 Related Work.
Record Nr. UNINA-9910861091703321
Meroño Peñuela Albert  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part II
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part II
Autore Meroño Peñuela Albert
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (277 pages)
Altri autori (Persone) DimouAnastasia
TroncyRaphaël
HartigOlaf
AcostaMaribel
AlamMehwish
PaulheimHeiko
LisenaPasquale
Collana Lecture Notes in Computer Science Series
ISBN 3-031-60635-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Work -- References -- IICONGRAPH: Improved Iconographic and Iconological Statements in Knowledge Graphs -- 1 Introduction.
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.
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 Analysis -- 6 Conclusion -- References -- MLSea: A Semantic Layer for Discoverable Machine Learning.
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.
Record Nr. UNISA-996601563103316
Meroño Peñuela Albert  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part I
The Semantic Web : 21st International Conference, ESWC 2024, Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part I
Autore Meroño Peñuela Albert
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (0 pages)
Altri autori (Persone) DimouAnastasia
TroncyRaphaël
HartigOlaf
AcostaMaribel
AlamMehwish
PaulheimHeiko
LisenaPasquale
Collana Lecture Notes in Computer Science Series
ISBN 3-031-60626-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Research -- Do Similar Entities Have Similar Embeddings? -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Towards a Graph-Based Notion of Similarity -- 3.2 Embedding vs. Graph: A Different Notion of Similarity? -- 4 Experiments -- 4.1 Knowledge Graph Embedding Models -- 4.2 Datasets -- 5 Results and Discussion -- 5.1 Different Notions of Similarity (RQ1) -- 5.2 Correlation Between Rank-Based Metrics and RBO (RQ2) -- 5.3 Analyzing Predicate Importance (RQ3) -- 6 Conclusion and Outlook -- References -- Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Semantic-Enhanced Approaches -- 2.2 Loss Functions for the Link Prediction Task -- 3 Signature-Driven Loss Functions -- 4 Experimental Setting -- 4.1 Evaluation Metrics -- 4.2 Datasets and Models -- 4.3 Implementation Details -- 5 Results -- 5.1 Global Performance -- 5.2 Ablation Study -- 6 Discussion -- 6.1 Treating Different Negatives Differently (RQ1) -- 6.2 Impact of Signature-Driven Losses on Performance (RQ2) -- 6.3 On the Evaluation of KGEM Predictions for LP -- 7 Conclusion -- A Modified Versions of LSBCEL and LSPLL -- B Bucket Analysis -- References -- QAGCN: Answering Multi-relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 QAGCN - A Question-Aware GCN-Based Question Answering Model -- 3.1 Task Definition -- 3.2 The Question-Aware GCN -- 4 Experiments -- 4.1 Effectiveness Evaluation -- 4.2 Comparison with SOTA Reasoning-Based Method - NSM -- 4.3 Efficiency Evaluation -- 4.4 Ablation Study -- 4.5 Case Study with Embedding Visualization -- 5 Conclusion and Outlook -- References.
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs -- 1 Introduction -- 2 Preliminaries -- 3 The Framework -- 4 Experiments -- 4.1 The Datasets -- 4.2 Evaluation Metrics -- 4.3 Experimental Setup -- 4.4 Transductive Time Interval Prediction -- 4.5 Inductive Time Interval Prediction -- 4.6 Fine-Grained Analysis -- 5 Related Work -- 6 Conclusion -- 7 Appendix -- 7.1 Effect of Number of Negative Samples -- 7.2 Evaluation Terminology -- 7.3 Variance Analysis -- References -- A Language Model Based Framework for New Concept Placement in Ontologies -- 1 Introduction -- 2 Related Work -- 2.1 Ontology Concept Placement -- 2.2 Pre-trained Language Models for Ontology Concept Placement -- 3 Problem Statement -- 4 Methodology -- 4.1 Edge Search: Searching Seed Concepts or Edges -- 4.2 Edge Formation and Enrichment -- 4.3 Edge Selection -- 5 Experiments -- 5.1 Data Construction -- 5.2 Metrics -- 5.3 Experimental Settings and Baseline Methods -- 5.4 Results -- 5.5 Ablation Studies -- 5.6 Conclusion and Future Studies -- References -- Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 3.1 Knowledge Graph Embedding (KGE) -- 3.2 Riemannian Geometry -- 3.3 Extrapolation -- 3.4 Composition Patterns -- 4 Method: HolmE -- 4.1 HolmE: Holomorphic KG Embedding -- 4.2 Model Analysis -- 5 Experiments -- 5.1 Overall Experiment Design -- 5.2 Extrapolation to Unseen Triples -- 5.3 Extrapolation to Under-Represented Entities -- 5.4 Extrapolation to Under-Represented Relations -- 6 Conclusion -- A Result Details -- References -- SC-Block: Supervised Contrastive Blocking Within Entity Resolution Pipelines -- 1 Introduction -- 2 WDC-Block: A Large Product Blocking Benchmark.
3 SC-Block: Supervised Contrastive Blocking -- 4 Blocking-Only Evaluation -- 4.1 Baseline Blocking Methods -- 4.2 Implementation -- 4.3 Results for Fixed K -- 4.4 Results for 99.5% Recall on Validation Set -- 5 Evaluation Within Entity Resolution Pipelines -- 5.1 F1 Score and Runtime -- 5.2 Impact of Training Time -- 6 Related Work -- 7 Conclusion -- A Appendix - Additional Experiments -- References -- Navigating Ontology Development with Large Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Ontology Engineering -- 2.2 Ontology Learning and Generation -- 2.3 LLMs for Code Generation -- 3 Methodology -- 3.1 Large Language Models -- 3.2 Prompting Methods -- 3.3 Experimental Setup -- 4 Experimental Results -- 4.1 Initial Experiment Results -- 4.2 Main Experiment Results -- 5 Discussion -- 6 Conclusions and Future Work -- References -- ESLM: Improving Entity Summarization by Leveraging Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Entity Summarization -- 2.2 Contextual Language Models -- 3 Approach -- 3.1 Problem Statement -- 3.2 ESLM Model -- 3.3 ESLM Model Enrichment -- 3.4 Entity Summarization -- 4 Experimental Setup -- 4.1 Baselines -- 4.2 Datasets -- 4.3 Experimental Settings -- 5 Results and Analysis -- 5.1 Comparison with State-of-the-Art Approaches -- 5.2 Example Findings -- 5.3 Ablation Study -- 5.4 Computational Requirements and Efficiency of ESLM -- 6 Conclusion and Future Work -- References -- Explanation of Link Predictions on Knowledge Graphs via Levelwise Filtering and Graph Summarization -- 1 Introduction -- 2 Fundamentals -- 2.1 Knowledge Graphs and Embedding Models -- 2.2 Quotient Graph -- 3 Related Works -- 4 The Proposed Approach -- 4.1 Kelpie -- 4.2 Injecting Semantics in the Pre-Filter -- 4.3 Injecting Semantics in the Explanation Builder -- 5 Experimental Evaluation -- 5.1 Experimental Setting.
5.2 Quantitative Evaluation -- 5.3 Qualitative Evaluation -- 6 Conclusions -- A Appendix: Repair of Inconsistent and Unsatisfiable Ontologies -- B Appendix: Hyper-parameters -- References -- Large Language Models for Scientific Question Answering: An Extensive Analysis of the SciQA Benchmark -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Dataset -- 3.2 Approaches -- 4 Methodology -- 4.1 Experiments -- 4.2 Experimental Setup -- 5 Results and Discussion -- 6 Error Analysis -- 7 Limitations -- 8 Conclusions -- A Appendix - Examples -- References -- Efficient Evaluation of Conjunctive Regular Path Queries Using Multi-way Joins -- 1 Introduction -- 2 Preliminaries -- 2.1 RDF and SPARQL -- 2.2 Worst-Case Optimal Multi-way Joins -- 2.3 GenericJoinCRPQ -- 3 Related Work -- 4 Evaluating C2RPQs with Multi-way Joins -- 4.1 Evaluation of RPQs -- 4.2 Evaluation of C2RPQs -- 4.3 Query Planning and Optimizations -- 5 Experimental Results -- 5.1 Systems and Experimental Setup -- 5.2 Datasets and Queries -- 5.3 Evaluation of Execution Strategies -- 5.4 Comparison with Existing Solutions -- 6 Conclusion and Future Work -- References -- Can Contrastive Learning Refine Embeddings -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 2.1 Contrastive Learning -- 2.2 Problem Statement -- 3 Method -- 3.1 Contrastive Learning Limitation -- 3.2 SimSkip Details -- 3.3 Data Augmentation -- 3.4 Theoretical Proof -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 SimSkip for Federated Knowledge Graph Embedding -- 4.3 SimSkip for Image Embedding -- 4.4 SimSkip for Node embedding learned by Supervised Learning -- 4.5 SimSkip for Transformer-based Text Embedding -- 4.6 Ablation Study -- 5 Related Work -- 5.1 Representation Learning -- 5.2 Contrastive Learning -- 6 Conclusion -- References -- In-Use.
Automation of Electronic Invoice Validation Using Knowledge Graph Technologies -- 1 Introduction -- 2 Preliminaries -- 3 Our Approach -- 3.1 EDIFACT Ontology -- 3.2 The EDIFACT-VAL Tool -- 4 Evaluation -- 4.1 Experimental Set up -- 4.2 Results of the Ontology Evaluation -- 4.3 Completeness of the Invoice KG Generated by EDIFACT-Val -- 4.4 Results of Invoice Validation with EDIFACT-Val -- 4.5 Runtime Performance of EDIFACT-Val -- 4.6 In-Use Evaluation -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Towards Cyber Mapping the German Financial System with Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 3 Knowledge Graph Construction -- 3.1 Cyber Mapping Ontology -- 3.2 Structured Data: Financial Domain -- 3.3 Unstructured Data: Fund Prospectus -- 3.4 Provenance Information -- 4 Knowledge Graph Application -- 5 User Study -- 5.1 Setup -- 5.2 Results -- 6 Conclusion and Outlook -- References -- Integrating Domain Knowledge for Enhanced Concept Model Explainability in Plant Disease Classification -- 1 Introduction -- 2 Use Cases -- 3 Related Work -- 4 Methods -- 4.1 Ontology Development (Ontology Based Explanation) -- 4.2 Synthetic Concepts Images Generation -- 4.3 Testing with Concept Activation Vectors (TCAV) -- 5 Experiments and Results -- 5.1 Dataset and Trained Model -- 5.2 Experimental Setup -- 5.3 Findings and Analyses -- 6 Conclusion -- References -- Generative Expression Constrained Knowledge-Based Decoding for Open Data -- 1 Introduction -- 2 Related Work -- 3 Application -- 3.1 Generative Models -- 4 Evaluation -- 4.1 Quantitative Results -- 4.2 Qualitative User Testing -- 5 Impact and Deployment -- 6 Conclusions and Future Work -- A S-expression functions considered in GECKO -- References -- OntoEditor: Real-Time Collaboration via Distributed Version Control for Ontology Development -- 1 Introduction -- 2 Motivation.
3 Related Work.
Record Nr. UNISA-996601563003316
Meroño Peñuela Albert  
Cham : , : Springer, , 2024
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