scholarly journals Peer Review #1 of "Uncertainty modeling process for semantic technology (v0.1)"

2016 ◽  
Keyword(s):  
Peer Review ◽  
Uncertainty Modeling ◽  
Semantic Technology ◽  
Modeling Process

scholarly journals Uncertainty modeling process for semantic technology

2016 ◽  
Vol 2 ◽  
pp. e77 ◽  
Author(s):  
Rommel N. Carvalho ◽  
Kathryn B. Laskey ◽  
Paulo C.G. Da Costa
Keyword(s):  
Knowledge Engineering ◽  
Uncertainty Modeling ◽  
Domain Model ◽  
Proof Of Concept ◽  
Semantic Technology ◽  
Modeling Process ◽  
Ontology Language ◽  
Domain Semantics ◽  
Probabilistic Ontology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

scholarly journals Uncertainty modeling process for semantic technology

2016 ◽  
Author(s):  
Rommel N Carvalho ◽  
Kathryn B Laskey ◽  
Paulo C. G. Da Costa
Keyword(s):  
Knowledge Engineering ◽  
Uncertainty Modeling ◽  
Domain Model ◽  
Proof Of Concept ◽  
Semantic Technology ◽  
Modeling Process ◽  
Ontology Language ◽  
Domain Semantics ◽  
Probabilistic Ontology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model is intended to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

scholarly journals Uncertainty modeling process for semantic technology

2016 ◽  
Author(s):  
Rommel N Carvalho ◽  
Kathryn B Laskey ◽  
Paulo C. G. Da Costa
Keyword(s):  
Knowledge Engineering ◽  
Uncertainty Modeling ◽  
Domain Model ◽  
Proof Of Concept ◽  
Semantic Technology ◽  
Modeling Process ◽  
Ontology Language ◽  
Domain Semantics ◽  
Probabilistic Ontology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model is intended to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

Peer review of a dental curriculum: how one school did it

1976 ◽  
Vol 40 (11) ◽  
pp. 761-762
Author(s):  
PK Morse ◽  
TR Dirksen
Keyword(s):  
Peer Review ◽  
Dental Curriculum

An empirical review of gambling expansion and gambling-related harm

2018 ◽  
Vol 64 (5-6) ◽  
pp. 295-306 ◽  
Author(s):  
Debi A. LaPlante ◽  
Heather M. Gray ◽  
Pat M. Williams ◽  
Sarah E. Nelson
Keyword(s):  
Literature Review ◽  
Peer Review ◽  
Empirical Data ◽  
Empirical Studies ◽  
Expansion Method ◽  
Responsible Gambling ◽  
Review Literature ◽  
Evidence Based ◽  
Empirical Comparison ◽  
Related Harm

Abstract. Aims: To discuss and review the latest research related to gambling expansion. Method: We completed a literature review and empirical comparison of peer reviewed findings related to gambling expansion and subsequent gambling-related changes among the population. Results: Although gambling expansion is associated with changes in gambling and gambling-related problems, empirical studies suggest that these effects are mixed and the available literature is limited. For example, the peer review literature suggests that most post-expansion gambling outcomes (i. e., 22 of 34 possible expansion outcomes; 64.7 %) indicate no observable change or a decrease in gambling outcomes, and a minority (i. e., 12 of 34 possible expansion outcomes; 35.3 %) indicate an increase in gambling outcomes. Conclusions: Empirical data related to gambling expansion suggests that its effects are more complex than frequently considered; however, evidence-based intervention might help prepare jurisdictions to deal with potential consequences. Jurisdictions can develop and evaluate responsible gambling programs to try to mitigate the impacts of expanded gambling.

A retrospective analysis of peer review at Physiologia Plantarum

1994 ◽  
Vol 92 (4) ◽  
pp. 535-542 ◽  
Author(s):  
Terence M. Murphy ◽  
Jessica M. Utts
Keyword(s):  
Peer Review ◽  
Retrospective Analysis
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