| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910484495803321 |
|
|
Titolo |
Uncertainty Reasoning for the Semantic Web III : ISWC International Workshops, URSW 2011-2013, Revised Selected Papers / / edited by Fernando Bobillo, Rommel N. Carvalho, Paulo C.G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Michael Pool |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2014.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XVIII, 329 p. 56 illus.) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Artificial Intelligence ; ; 8816 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Mathematical logic |
Mathematical statistics |
Data mining |
Artificial Intelligence |
Mathematical Logic and Formal Languages |
Probability and Statistics in Computer Science |
Data Mining and Knowledge Discovery |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Bibliographic Level Mode of Issuance: Monograph |
|
|
|
|
|
|
Nota di contenuto |
|
UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework -- Computing Inferences for Relational Bayesian Networks Based on ALC Constructs -- Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+/– -- Learning Probabilistic Description Logics -- Semantics and Inference for Probabilistic Description Logics -- A Metaontology for Annotating Ontology Entities with Vagueness Descriptions -- Finite Lattices Do Not Make Reasoning in ALCOI Harder -- Fuzzy and Cross-Lingual Ontology Matching Mediated by Background Knowledge -- Semantic Knowledge Discovery and Data-Driven Logical Reasoning from Heterogeneous Data Sources -- Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness -- |
|
|
|
|
|
|
|
|
|
|
|
Graph-Based Regularization for Transductive Class-Membership Prediction -- Analyzing User Demographics and User Behavior for Trust Assessment -- Bridging Gaps Between Subjective Logic and Semantic Web -- Uncertainty Estimation and Analysis of Categorical Web Data -- Ontological CP-Nets -- Uncertainty Handling in Named Entity Extraction and Disambiguation for Informal Text -- UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework -- Computing Inferences for Relational Bayesian Networks Based on ALC Constructs -- Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+/– -- Learning Probabilistic Description Logics -- Semantics and Inference for Probabilistic Description Logics -- A Metaontology for Annotating Ontology Entities with Vagueness Descriptions -- Finite Lattices Do Not Make Reasoning in ALCOI Harder -- Fuzzy and Cross-Lingual Ontology Matching Mediated by Background Knowledge -- Semantic Knowledge Discovery and Data-Driven Logical Reasoning from Heterogeneous Data Sources -- Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness -- Graph-Based Regularization for Transductive Class-Membership Prediction -- Analyzing User Demographics and User Behavior for Trust Assessment -- Bridging Gaps Between Subjective Logic and Semantic Web -- Uncertainty Estimation and Analysis of Categorical Web Data -- Ontological CP-Nets -- Uncertainty Handling in Named Entity Extraction and Disambiguation for Informal Text. |
|
|
|
|
|
|
Sommario/riassunto |
|
This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2011, 2012, and 2013. The 16 papers presented were carefully reviewed and selected from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches. |
|
|
|
|
|
|
|
| |