[PDF] Uncertainty Reasoning For The Semantic Web eBook

Author : Paulo C. G. Costa
Publisher : Springer Science & Business Media
Page : 416 pages
File Size : 34,36 MB
Release : 2008-12-02
Category : Computers
ISBN : 354089764X

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This book constitutes the thoroughly refereed first three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007. The 22 papers presented are revised and strongly extended versions of selected workshops papers as well as invited contributions from leading experts in the field and closely related areas. The present volume represents the first comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the semantic Web, capturing different models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge.

Author : Fernando Bobillo
Publisher : Springer
Page : 345 pages
File Size : 14,20 MB
Release : 2013-01-09
Category : Computers
ISBN : 3642359752

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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 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

Author : Fernando Bobillo
Publisher : Springer
Page : 346 pages
File Size : 20,95 MB
Release : 2014-11-29
Category : Computers
ISBN : 3319134132

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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.

Author : Stephen C. Dinkel
Publisher :
Page : 370 pages
File Size : 20,81 MB
Release : 2012
Category :
ISBN :

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Accurate situational assessment is key to any decision maker and especially crucial in military command and control, air traffic control, and complex system decision making. Endsley described three dependent levels of situational awareness, (1) perception, (2) understanding, and (3) projection. This research was focused on Endsley's second-level situational awareness (understanding) as it applies to service-oriented information technology environments in the context of the Semantic Web. Specifically, this research addressed the problem of developing accurate situational assessments related to the status or health of information technology (IT) services, especially composite, dynamic IT services, when some of Endsley's first level (perceived) information was inaccurate or incomplete. Research had not adequately addressed the problem of how to work with inaccuracy and situational awareness information in order to produce accurate situational assessments for Semantic Web services. This problem becomes especially important as the current Web moves towards a Semantic Web where information technology is expected to be represented and processed by machines. Costa's probabilistic Web ontology language (PR-OWL), as extended by Carvalho (PR-OWL2), is a framework for storage of and reasoning with uncertainty information as part of the Semantic Web. This study used Costa's PR-OWL framework, as extended by Carvalho, to build an ontology that supports reasoning with service-oriented information in the context of the Semantic Web and then assessed the effectiveness of the developed ontology through the use of competency questions, as described by Gruninger and Fox and verified through the use of an automated reasoner. This research resulted in a Web Ontology Language for Services (OWL-S), PR-OWL2 based ontology, and its associated Multi-Entity Bayesian Network which are flexible and highly effective in calculating situational assessments through the propagation of posterior probabilities using Bayesian logic. Specifically, this research (1) identifies sufficient information required for effective situational awareness reasoning, (2) specifies the predicates and semantics necessary to represent service components and dependencies, (3) applies Multi-Entity Bayesian Network to reason with situational awareness information, (4) ensures the correctness and consistency of the situational awareness ontology, and (5) accurately estimates posterior probabilities consistent with situational awareness information.

Author : R. Zese
Publisher : IOS Press
Page : 193 pages
File Size : 25,19 MB
Release : 2016-12-09
Category : Computers
ISBN : 1614997349

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The management of uncertainty in the Semantic Web is of foremost importance given the nature and origin of the available data. This book presents a probabilistic semantics for knowledge bases, DISPONTE, which is inspired by the distribution semantics of Probabilistic Logic Programming. The book also describes approaches for inference and learning. In particular, it discusses 3 reasoners and 2 learning algorithms. BUNDLE and TRILL are able to find explanations for queries and compute their probability with regard to DISPONTE KBs while TRILLP compactly represents explanations using a Boolean formula and computes the probability of queries. The system EDGE learns the parameters of axioms of DISPONTE KBs. To reduce the computational cost, EDGEMR performs distributed parameter learning. LEAP learns both the structure and parameters of KBs, with LEAPMR using EDGEMR for reducing the computational cost. The algorithms provide effective techniques for dealing with uncertain KBs and have been widely tested on various datasets and compared with state of the art systems.

Author : Claudia d’Amato
Publisher : Springer
Page : 248 pages
File Size : 43,69 MB
Release : 2018-09-14
Category : Computers
ISBN : 3030003388

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This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-sur-Alzette, Luxembourg, in September 2018. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

Author : Fernando Bobillo
Publisher : Springer
Page : 0 pages
File Size : 43,32 MB
Release : 2013-01-09
Category : Computers
ISBN : 9783642359750

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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 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.