The Semantic Shadow : Combining User Interaction with Context Information for Semantic Web-Site Annotation

This thesis develops the concept of the Semantic Shadow (SemS), a model for managing contentual and structural annotations on web page elements and their values. The model supports a contextual weighting of the annotated information, allowing to specify the annotation values in relation to the evaluation context. A procedure is presented, which allows to manage and process this context-dependent meta information on web page elements using a dedicated programming interface. Two distinct implementations for the model have been developed: One based on Java objects, the other using the Resource Description Framework (RDF) as modeling backend. This RDF-based storage allows to integrate the annotations of the Semantic Shadow with other information of the Semantic Web. To demonstrate the application of the Semantic Shadow concept, a procedure to optimize web based user interfaces based on the structural semantics has been developed: Assuming a mobile client, a requested web page is dynamically adapted by a proxy prototype, where the context-awareness of the adaptation can be directly modeled alongside with the structural annotations. To overcome the drawback of missing annotations for existing web pages, this thesis introduces a concept to derive context-dependent meta-information on the web pages from their usage: From the observation of the users' interaction with a web page, certain context-dependent structural information about the concerned web page elements can be derived and stored in the annotation model of the Semantic Shadow concept.In dieser Arbeit wird das Konzept des Semantic Shadow (dt. Semantischer Schatten) entwickelt, ein Programmier-Modell um Webseiten-Elemente mit inhaltsbezogenen und strukturellen Anmerkungen zu versehen. Das Modell unterstützt dabei eine kontextabhängige Gewichtung der Anmerkungen, so dass eine Anmerkung in Bezug zum Auswertungs-Kontext gesetzt werden kann. Zur Verwaltung und Verarbeitung dieser kontextbezogenen Meta-Informationen für Webseiten-Elemente wurde im Rahmen der Arbeit eine Programmierschnittstelle definiert. Dazu wurden zwei Implementierungen der Schnittstelle entwickelt: Eine basiert ausschließlich auf Java-Objekten, die andere baut auf einem RDF-Modell auf. Die RDF-basierte Persistierung erlaubt eine Integration der Semantic-Shadow-Anmerkungen mit anderen Anwendungen des Semantic Webs. Um die Anwendungsmöglichkeiten des Semantic-Shadow-Konzepts darzustellen, wurde eine Vorgehensweise zur Optimierung von webbasierten Benutzerschnittstellen auf Grundlage von semantischen Strukturinformationen entwickelt: Wenn ein mobiler Benutzer eine Webseite anfordert, wird diese dynamisch durch einen Proxy angepasst. Die Kontextabhängigkeit dieser Anpassung wird dabei bereits direkt mit den Struktur-Anmerkungen modelliert. Für bestehende Webseiten liegen zumeist keine Annotationen vor. Daher wird in dieser Arbeit ein Konzept vorgestellt, kontextabhängige Meta-Informationen aus der Benutzung der Webseiten zu bestimmen: Durch Beobachtung der Benutzer-Interaktionen mit den Webseiten-Elementen ist es möglich bestimmte kontextabhängige Strukturinformationen abzuleiten und als Anmerkungen im Modell des Semantic-Shadow-Konzepts zu persistieren

Expressing and Interpreting User Intention in Pervasive Service Environments

International audienceThe introduction of pervasive computing environments enforce new ways of human-machine-interaction. The welldefined interaction interfaces will make place for other, more intuitive ways of interaction. In a pervasive service environment, the system middleware should take care of capturing the users expression of an action intention, solving ambiguousness in this expression, and executing the final pervasive action This article introduces the Pervasive Service Action Query Language (PsaQL), a language to formalize the description of a user intention using composed pervasive services. It presents the next steps of intention treatment in a pervasive service environment: A mathematical model is given, which helps to express the algorithms performing translation of the user intention into an executable action. To implement such algorithms, a suitable object- oriented model representing actions is introduced. In the scope of PERSE, a pervasive service environment developed by our research group, general evaluation metrics for such algorithms are identified, a prototype has been developed and first benchmark results are presented in this article

Modeling User Intention in Pervasive Service Environments

International audienceThe introduction of pervasive computing environments ineveryday life will not just be a big step for users, but also for application designers. The well defined interaction interfaces will make place for other, more intuitive ways of interaction. It is the challenge for a pervasivesystem middleware to capture and model the user intention in asmart way and to solve ambiguousness in the user’s expression of a pervasive action. This paper introduces the Pervasive Service Action Query Language (PsaQL), a language to formalize the description of a user intention using composed pervasive services. The work describes a way of translating the user intention into an executable action and propose algorithmsperforming this translation. Considerations to implement thisprocess are given within the scope of PerSE, a pervasive service environment developed by our research group, together with general evaluation metrics for such algorithms

Expressing and Interpreting User Intention in Pervasive Service Environments

International audienceThe introduction of pervasive computing environments enforce new ways of human-machine-interaction. The welldefined interaction interfaces will make place for other, more intuitive ways of interaction. In a pervasive service environment, the system middleware should take care of capturing the users expression of an action intention, solving ambiguousness in this expression, and executing the final pervasive action This article introduces the Pervasive Service Action Query Language (PsaQL), a language to formalize the description of a user intention using composed pervasive services. It presents the next steps of intention treatment in a pervasive service environment: A mathematical model is given, which helps to express the algorithms performing translation of the user intention into an executable action. To implement such algorithms, a suitable object- oriented model representing actions is introduced. In the scope of PERSE, a pervasive service environment developed by our research group, general evaluation metrics for such algorithms are identified, a prototype has been developed and first benchmark results are presented in this article

Expressing and Interpreting User Intention in Pervasive Service Environments

International audienceExpressing and Interpreting User Intention in Pervasive Service Environment

Localization in sparse networks using sweeps

Determining node positions is essential for many next-generation network functionalities. Previous localization algorithms lack correctness guarantees or require network density higher than required for unique localizability. In this paper, we describe

The Clickable Guard Cell, Version II: Interactive Model of Guard Cell Signal Transduction Mechanisms and Pathways

Guard cells are located in the leaf epidermis and pairs of guard cells surround and form stomatal pores, which regulate CO2 influx from the atmosphere into leaves for photosynthetic carbon fixation. Stomatal guard cells also regulate water loss of plants via transpiration to the atmosphere. Signal transduction mechanisms in guard cells integrate a multitude of different stimuli to modulate stomatal apertures. Stomata open in response to light. Stomata close in response to drought stress, elevated CO2, ozone and low humidity. In response to drought, plants synthesize the hormone abscisic acid (ABA) that triggers closing of stomatal pores. Guard cells have become a highly developed model system for dissecting signal transduction mechanisms in plants and for elucidating how individual signaling mechanisms can interact within a network in a single cell. Many new findings have been made in the last few years. This chapter is an update of an electronic interactive chapter in the previous edition of The Arabidopsis Book (Mäser et al. 2003). Here we focus on mechanisms for which genes and mutations have been characterized, including signaling components for which there is substantial signaling, biochemical and genetic evidence. Ion channels have been shown to represent targets of early signal transduction mechanisms and provide functional signaling and quantitative analysis points to determine where and how mutations affect branches within the guard cell signaling network. Although a substantial number of genes and proteins that function in guard cell signaling have been identified in recent years, there are many more left to be identified and the protein-protein interactions within this network will be an important subject of future research. A fully interactive clickable electronic version of this publication can be accessed at the following web site: http://www-biology.ucsd.edu/labs/schroeder/clickablegc2/. The interactive clickable version includes the following features: Figure 1. Model for the roles of ion channels in ABA signaling.Figure 2. Blue light signaling pathways in guard cells.Figure 3. ABA signaling pathways in guard cells. Figure 1 is linked to explanations that appear upon mouse-over. Figure 2 and Figure 3 are clickable and linked to info boxes, which in turn are linked to TAIR, to relevant abstracts in PubMed, and to updated background explanations from Schroeder et al (2001), used with permission of Annual Reviews of Plant Biology