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Graph-Based Representation and Reasoning [[electronic resource] ] : 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings / / edited by Dominik Endres, Mehwish Alam, Diana Şotropa
| Graph-Based Representation and Reasoning [[electronic resource] ] : 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings / / edited by Dominik Endres, Mehwish Alam, Diana Şotropa |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXIV, 271 p. 128 illus., 59 illus. in color.) |
| Disciplina | 003.54 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Database management Application software Mathematical logic Artificial Intelligence Database Management Information Systems Applications (incl. Internet) Mathematical Logic and Formal Languages Computer Applications |
| ISBN | 3-030-23182-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talk -- Visualization, Reasoning, and Rationality -- Regular Papers -- Introducing Contextual Reasoning to the Semantic Web with OWLC -- Graph-based variability modelling: towards a classification of existing Formalisms -- Publishing Uncertainty on the Semantic Web: Blurring the LOD bubbles -- Formal Context Generation using Dirichlet Distributions -- Lifted Temporal Most Probable Explanation -- Temporal Relations between Imprecise Time Intervals: Representation and Reasoning -- Relevant Attributes in Formal Contexts -- Adaptive Collaborative Filtering for Recommender System -- Exploring and Conceptualizing Attestation -- Enhancing Layered Enterprise Architecture Development through Conceptual Structures -- Ontology-informed Lattice Reduction Using the Discrimination Power Index -- Redescription mining for learning definitions and disjointness axioms in Linked Open Data -- Covering Concept Lattices with Concept Chains -- The compositional rule of inference under the composition max-product -- Short Papers -- Mining Social Networks from Linked Open Data -- Navigate and Refine: IR Chatbot based on Conceptual Models -- Semiotic-Conceptual Analysis of a Lexical Field -- Applying Semiotic-Conceptual Analysis to Mathematical Language -- Mathematical similarity models: Do we need incomparability to be precise? -- Conceptual Graphs Based Modeling of MongoDB Data Structure and Query. |
| Record Nr. | UNISA-996466320903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Graph-Based Representation and Reasoning : 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings / / edited by Dominik Endres, Mehwish Alam, Diana Şotropa
| Graph-Based Representation and Reasoning : 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings / / edited by Dominik Endres, Mehwish Alam, Diana Şotropa |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXIV, 271 p. 128 illus., 59 illus. in color.) |
| Disciplina |
003.54
006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Database management Application software Mathematical logic Artificial Intelligence Database Management Information Systems Applications (incl. Internet) Mathematical Logic and Formal Languages Computer Applications |
| ISBN | 3-030-23182-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talk -- Visualization, Reasoning, and Rationality -- Regular Papers -- Introducing Contextual Reasoning to the Semantic Web with OWLC -- Graph-based variability modelling: towards a classification of existing Formalisms -- Publishing Uncertainty on the Semantic Web: Blurring the LOD bubbles -- Formal Context Generation using Dirichlet Distributions -- Lifted Temporal Most Probable Explanation -- Temporal Relations between Imprecise Time Intervals: Representation and Reasoning -- Relevant Attributes in Formal Contexts -- Adaptive Collaborative Filtering for Recommender System -- Exploring and Conceptualizing Attestation -- Enhancing Layered Enterprise Architecture Development through Conceptual Structures -- Ontology-informed Lattice Reduction Using the Discrimination Power Index -- Redescription mining for learning definitions and disjointness axioms in Linked Open Data -- Covering Concept Lattices with Concept Chains -- The compositional rule of inference under the composition max-product -- Short Papers -- Mining Social Networks from Linked Open Data -- Navigate and Refine: IR Chatbot based on Conceptual Models -- Semiotic-Conceptual Analysis of a Lexical Field -- Applying Semiotic-Conceptual Analysis to Mathematical Language -- Mathematical similarity models: Do we need incomparability to be precise? -- Conceptual Graphs Based Modeling of MongoDB Data Structure and Query. |
| Record Nr. | UNINA-9910337841403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Ontologies and concepts in mind and machine : 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18-20, 2020, Proceedings / / Mehwish Alam, Tanya Braun, Bruno Yun (Eds.)
| Ontologies and concepts in mind and machine : 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18-20, 2020, Proceedings / / Mehwish Alam, Tanya Braun, Bruno Yun (Eds.) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XXIV, 209 p. 170 illus., 16 illus. in color.) |
| Disciplina | 003.54 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Conceptual structures (Information theory) |
| ISBN | 3-030-57855-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Knowledge Bases -- Conceptual Structures -- Reasoning Models. |
| Record Nr. | UNINA-9910427717603321 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Ontologies and concepts in mind and machine : 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18-20, 2020, Proceedings / / Mehwish Alam, Tanya Braun, Bruno Yun (Eds.)
| Ontologies and concepts in mind and machine : 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18-20, 2020, Proceedings / / Mehwish Alam, Tanya Braun, Bruno Yun (Eds.) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XXIV, 209 p. 170 illus., 16 illus. in color.) |
| Disciplina | 003.54 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Conceptual structures (Information theory) |
| ISBN | 3-030-57855-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Knowledge Bases -- Conceptual Structures -- Reasoning Models. |
| Record Nr. | UNISA-996418296403316 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Semantic Systems. In the Era of Knowledge Graphs [[electronic resource] ] : 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings / / edited by Eva Blomqvist, Paul Groth, Victor de Boer, Tassilo Pellegrini, Mehwish Alam, Tobias Käfer, Peter Kieseberg, Sabrina Kirrane, Albert Meroño-Peñuela, Harshvardhan J. Pandit
| Semantic Systems. In the Era of Knowledge Graphs [[electronic resource] ] : 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings / / edited by Eva Blomqvist, Paul Groth, Victor de Boer, Tassilo Pellegrini, Mehwish Alam, Tobias Käfer, Peter Kieseberg, Sabrina Kirrane, Albert Meroño-Peñuela, Harshvardhan J. Pandit |
| Autore | Blomqvist Eva |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIII, 127 p. 102 illus., 30 illus. in color.) |
| Disciplina | 006.33 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Knowledge representation (Information theory)
Application software Computers Knowledge based Systems Computer Applications Information Systems and Communication Service |
| Soggetto non controllato |
Knowledge based Systems
Computer Applications Information Systems and Communication Service Computer and Information Systems Applications Computer Engineering and Networks artificial intelligence computer science computer systems data handling databases dbpedia internet knowledge-based system linguistics linked data Natural Language Processing (NLP) natural languages ontologies query languages query processing resource description framework search engines semantic web semantics World Wide Web Expert systems / knowledge-based systems Information technology: general issues Computer networking & communications |
| ISBN | 3-030-59833-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows -- DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects,. A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge -- Semantic Annotation, Representation and Linking of Survey Data -- QueDI: from Knowledge Graph Querying to Data Visualization -- EcoDaLo: Federating advertisement targeting with Linked Data -- MINDS: a translator to embed mathematical expressions inside SPARQL queries -- Integrating Historical Person Registers as Linked Open Data in the WarSampo Knowledge Graph. |
| Record Nr. | UNISA-996418218103316 |
Blomqvist Eva
|
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Semantic Systems. In the Era of Knowledge Graphs : 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings / / edited by Eva Blomqvist, Paul Groth, Victor de Boer, Tassilo Pellegrini, Mehwish Alam, Tobias Käfer, Peter Kieseberg, Sabrina Kirrane, Albert Meroño-Peñuela, Harshvardhan J. Pandit
| Semantic Systems. In the Era of Knowledge Graphs : 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings / / edited by Eva Blomqvist, Paul Groth, Victor de Boer, Tassilo Pellegrini, Mehwish Alam, Tobias Käfer, Peter Kieseberg, Sabrina Kirrane, Albert Meroño-Peñuela, Harshvardhan J. Pandit |
| Autore | Blomqvist Eva |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIII, 127 p. 102 illus., 30 illus. in color.) |
| Disciplina | 006.33 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Knowledge representation (Information theory)
Application software Computers Knowledge based Systems Computer Applications Information Systems and Communication Service |
| Soggetto non controllato |
Knowledge based Systems
Computer Applications Information Systems and Communication Service Computer and Information Systems Applications Computer Engineering and Networks artificial intelligence computer science computer systems data handling databases dbpedia internet knowledge-based system linguistics linked data Natural Language Processing (NLP) natural languages ontologies query languages query processing resource description framework search engines semantic web semantics World Wide Web Expert systems / knowledge-based systems Information technology: general issues Computer networking & communications |
| ISBN | 3-030-59833-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows -- DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects,. A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge -- Semantic Annotation, Representation and Linking of Survey Data -- QueDI: from Knowledge Graph Querying to Data Visualization -- EcoDaLo: Federating advertisement targeting with Linked Data -- MINDS: a translator to embed mathematical expressions inside SPARQL queries -- Integrating Historical Person Registers as Linked Open Data in the WarSampo Knowledge Graph. |
| Record Nr. | UNINA-9910424947703321 |
|
Blomqvist Eva
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
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Meroño Peñuela Albert
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| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Meroño Peñuela Albert
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||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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. | UNISA-996601563103316 |
|
Meroño Peñuela Albert
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||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
