SDF: A Sign Description Framework for Cross-Context Information Resource Representation and Interchange

Dynamic context management for pervasive applications

The Knowledge Engineering Review ◽

10.1017/s0269888907001269 ◽

2008 ◽

Vol 23 (1) ◽

pp. 21-49 ◽

Cited By ~ 30

Author(s):

JÉRÔME EUZENAT ◽

JÉRÔME PIERSON ◽

FANO RAMPARANY

Keyword(s):

Pervasive Computing ◽

Context Information ◽

Human Beings ◽

Semantic Web Technologies ◽

Web Technologies ◽

Related Information ◽

Simple Protocol ◽

Description Framework ◽

Description Languages ◽

New Applications

AbstractPervasive computing aims at providing services for human beings that interact with their environment (encompassing objects and people who reside in it). Pervasive computing applications must be able to take into account the context in which users evolve, for example, physical location, social or hierarchical position, current tasks as well as related information. These applications have to deal with the dynamic integration in the environment of new, and sometimes unexpected, elements (users or devices). In turn, the environment has to provide context information to newly designed applications. This requires a framework which is open, dynamic and minimal. We describe an architecture in which context information is distributed in the environment and context managers use semantic Web technologies in order to identify and characterize available resources. The components in the environment maintain their own context expressed in RDF (Resource Description Framework) and described through OWL ontologies. They may communicate this information to other components, obeying a simple protocol for identifying them and determining the information they can provide. We show how this architecture allows introducing new devices and new applications without interrupting what is working. In particular, the openness of ontology description languages makes possible the extension of context descriptions and ontology matching helps dealing with independently developed ontologies.

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Automatic Stereotype Activation Is Context Dependent

Social Psychology ◽

10.1027/1864-9335/a000019 ◽

2010 ◽

Vol 41 (3) ◽

pp. 131-136 ◽

Cited By ~ 35

Author(s):

Catharina Casper ◽

Klaus Rothermund ◽

Dirk Wentura

Keyword(s):

Lexical Decision ◽

Context Information ◽

Decision Task ◽

Social Categories ◽

Priming Effects ◽

Stereotype Activation ◽

Social Stereotypes ◽

Priming Paradigm ◽

Automatic Activation ◽

Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

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Impact of Context Information on Metaphor Elaboration

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000422 ◽

2018 ◽

Vol 65 (6) ◽

pp. 370-384 ◽

Cited By ~ 5

Author(s):

Veronika Lerche ◽

Ursula Christmann ◽

Andreas Voss

Keyword(s):

Diffusion Model ◽

Processing Speed ◽

A Priori ◽

Model Fit ◽

Verbal Task ◽

Context Information ◽

Speed Of Processing ◽

Good Account ◽

Dependent Variables ◽

Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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