Domain and user knowledge in a web-based courseware engineering course, knowlegde-based software engineering.

AIMS is a knowledge-based system for learning and teaching support within the context of distance education. It is aimed not only at enhancing learner's conceptual knowledge in a specific subject area but also at providing knowledge verification tools for the teacher. The system can be used to aid learning and teaching in different subject areas and to provide user-oriented support in searching courserelated information, concept teaching and learning, and conceptual and task-oriented domain structuring

OntoAIMS: ontological approach to courseware authoring

In this paper we discuss how current ontology concepts can be beneficial for more flexible and semantic rich description of the authoring process and for the provision of authoring support of Intelligent Educational Systems (IES) with respect to the three main authoring modules: domain editing, course composition and resource management. We take a semantic perspective on the knowledge representation within such systems and explore the interoperability between the various ontological structures for domain, instructional and resource modeling and the modeling of the entire authoring process. We build upon our research on Authoring Task Ontology and exemplify it within OntoAIMS system. We present authoring scenarios and show their mapping with authoring task ontology. Further we discuss the OntoAIMS framework for management of electronic learning objects (resources) and their usage in the automatic generation of course templates for the authors. Finally, we describe our architecture, based on the ontological specification of the authoring process

Crowdsourced Evaluation of Semantic Patterns for Recommendations

Abstract. In this paper we explore the use of semantics to improve diversity in recommendations. We use semantic patterns extracted from Linked Data sources to surface new connections between items to provide diverse recommendations to the end users. We evaluate this methodology by adopting a bottom-up approach, i.e. we ask users of a crowdsourcing platform to choose a movie recommendation from among five options. We evaluate the results in terms of a diversity measure based on the semantic distance of topics and genres of the result list. The results of the experiment indicate that there are features of semantic patterns that can be used as an indicator of its suitability for the recommendation process.

Interactive user modeling for personalized access to museum collections : the Rijksmuseum case study

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections

Semantics-driven recommendations in cross-media museum applications

In this paper we present the CHIP demonstrator aimed at helping users to explore the Rijksmuseum Amsterdam collection both online and inside the museum. Cultural heritage data from various external sources is integrated to provide an enriched semantic knowledge structure. The resulting RDF/OWL graph is the basis for CHIP main functionality for recommendations, search and personalized interaction

Scoring and Classifying Implicit Positive Interpretations:A Challenge of Class Imbalance

This paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according to their likelihood. We convert the scores to classes and report our results on both the regression and classification tasks. We show that a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset. Our error analysis indicates that an approach that takes the information structure into account (i.e. which information is new or contrastive) may be promising, which requires looking beyond the syntactic and semantic characteristics of negated statements