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2 nd international workshop on semantic models for adaptive interactive systems (SEMAIS 2011)
Abstract
The International Workshop on Semantic Models for Adaptive Interactive Systems (SEMAIS 2011) aims to identify emerging trends in interactive system design and execution using semantic models.
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Machine learning (ML) is a complex process that can hardly be carried out by non-expert users. Especially when using adaptive systems that interpret and exploit observations of the user to modify their behavior according to the user's perceived preferences, even naïve users may be confronted with learning systems. This paper presents an approach to make non-expert users understand and influence an ML system such as to improve trust and acceptance of the overall system behavior.
Application integration can be carried out on three different levels: the data source level, the business logic level, and
the user interface level. With ontologies-based integration on the data source level dating back to the 1990s and semantic
web services for integrating on the business logic level coming of age, it is time for the next logical step: employing ontologies
for integration on the user interface level. Such an approach supports both the developer (in terms of reduced development
times) and the user (in terms of better usability) of integrated applications. In this paper, we introduce a framework employing
ontologies for integrating applications on the user interface level.
In this paper, we present an ontology-based utterance interpretation mechanism for intelligent conversational interfaces. We describe how this mechanism was embedded in a conversational interface applied to personal assistant agents. The main goal of such approach is to offer a system capable of performing tasks through an intuitive interface, allowing experienced and less experienced users to interact with it in an easy and comfortable way. In this mechanism, ontologies are used for syntactic and semantic interpretation. We present how to design an ontology for semantic interpretation and how the interpretation process use it for semantic analysis.
Ontologies have been increasingly used in software systems in the past years. However, in many of those systems, the ontologies are hidden "under the hood". While a lot of useful applications of ontologies on the database and business logic layer have been proposed, the employment of ontologies in user interfaces has been gaining comparatively little attention so far. For providing a deeper understanding of that field as well as assisting developers of ontology-enhanced user interfaces, the authors give an overview of such applications and introduce a schema for characterizing the requirements of ontology-enhanced user interfaces. With this article, a state of the art survey of approaches is presented along with promising research directions.
The paper presents a system architecture for the automatic generation of interface specifications from ontologies.
This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Conceptual navigation realizes the independence between resources and links to facilitate interoperability and reusability. An engine builds dynamic links, assembles resources under an argumentative scheme and allows optimization with a possible constraint, such as the user's available time. Among several strategies, two are discussed in detail with examples of applications. On the one hand, conceptual specifications for linking and assembling are embedded in the resource meta-description with the support of the ontology of the domain to facilitate meta-communication. Resources are like agents looking for conceptual acquaintances with intention. On the other hand, the domain ontology and an argumentative ontology drive the linking and assembling strategies.
Knowledge workers solve complex problems. Their work seems not to be routinisable because of the unique results and constellation of actors involved. For distributed collaboration knowledge workers need many different tools, which leads to knowledge dispersed over different locations, artifacts, and systems. Context-based adaptation can be used to support teams by shared workspace environments best meeting their needs. We propose an ontology representing context in a shared workspace environment, and a conceptual architecture for context sensing, reasoning, and adaptation. We report on first experiences demonstrating the applicability of our approach and give an outlook on directions of future work.
Strang, T. and Linnhoff-Popien, C. A Context Modeling Survey
- T Strang
- C Linnhoff-Popien
- Strang T.