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3rd workshop on semantic models for adaptive interactive systems (SEMAIS): (SEMAIS)
Abstract
The International Workshop on Semantic Models for Adaptive Interactive Systems (SEMAIS 2012) aims to identify emerging trends in interactive system design 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.
Fostering creativity in User Interface (UI) design is challenging for innovation. This paper explores the combination of model-based approaches and interactive genetic algorithms to foster the exploration of the design space. A user task model is given in input. Magellan produces sketches of UIs that aim at inspiring the designer. Later on, appropriate tools may be used to tune the right design into the design right. Magellan is a proof of concept that deserves further exploration. Currently it is implemented using COMETs but it is not dependent of this technology.
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.
In this paper we introduce SPREADR, a model-based tech-nique for creating context-adaptive web applications. In this approach, the domain knowledge as well as context factors are represented by an ontology. Context factors depend on the application domain and may include location, time, user role, weather or any other relevant context information, of-ten information which can automatically be sensed. Both domain objects and context factors are treated as concepts or instances in the ontology, which can be linked to each other, thus forming a semantic network. In addition to this repre-sentation, we introduce scalar activation levels for concepts as well as weights for relations. Concept activation repre-sents the level of user interest whereas activation of con-text factors can be seen as the level of fulfilment based on some measurement. The structure of the semantic network remains the same for each user while activation levels may differ. Recognition of context factors or user actions trigger an activation flow through the network, thus increasing the activation of contextually important nodes. Relations are as-sociated with different weights resulting in different amounts of activation spreading along different relations. The result-ing spreading activation network can be used both for gen-erating a web site as well as for controlling the adaptive be-haviour of the system. Adaptation may include effects such as sorting navigation items by relevance, showing or hiding information depending on the activation value and highlight-ing or recommending important items. The system is also able to learn user preferences through implicit feedback. We present a demonstrator application as well as initial evalua-tion results.
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.
Collaborative work is characterized by frequently changing situations and corresponding demands for tool support and interaction behavior provided by the collaboration environment. Current approaches to address these changing demands include manual tailoring by end-users and automatic adaptation of single user tools or for individual users. Few systems use context as a basis for adapting collaborative work environments, mostly focusing on document recommendation and awareness provision. In this paper, we present, firstly, a generic four layer framework for modeling and exploiting context. Secondly, a generic adaptation process translating user activity into state, deriving context for a given focus, and executing adaptation rules on this context. Thirdly, a collaboration domain model for describing collaboration environments and collaborative situations. Fourthly, examples of exploiting our approach to support context-based adaptation in four typical collaboration situations: co-location, co-access, co-recommendation, and co-dependency.
Ontologies are a key component for building open and dynamic distributed pervasive computing systems in which agents and devices share contextual information. We describe our use of the Web Ontology Language OWL and other tools for building the foundation ontology for the Context Broker Architecture (CoBrA), a new context-aware pervasive computing framework. The current version of the CoBrA ontology models the basic concepts of people, agents, places, and presentation events in an intelligent meeting room environment. It provides a vocabulary of terms for classes and properties suitable for building practical systems that model context in pervasive computing environments. We also describe our ongoing research in developing an OWL inference engine using Flora-2 and in extending the present CoBrA ontology to use the DAML spatial and temporal ontologies.