1,721,134 research outputs found
Semantic Web Technologies for the Adaptive Web
No full textOntologies and reasoning are the key terms brought into focus by the semantic web community. Formal representation of ontologies in a common data model on the web can be taken as a foundation for adaptive web technologies as well. This chapter describes how ontologies shared on the semantic web provide conceptualization for the links which are a main vehicle to access information on the web. The subject domain ontologies serve as constraints for generating only those links which are relevant for the domain a user is currently interested in. Furthermore, user model ontologies provide additional means for deciding which links to show, annotate, hide, generate, and reorder. The semantic web technologies provide means to formalize the domain ontologies and metadata created from them. The formalization enables reasoning for personalization decisions. This chapter describes which components are crucial to be formalized by the semantic web ontologies for adaptive web. We use examples from an eLearning domain to illustrate the principles which are broadly applicable to any information domain on the web
Completing LOM-how additional axioms increase the utility of learning object metadata
No full textLearning Objects Metadata aims at describing educational resources in order to allow better reusability and retrieval. Unfortunately, annotating complete courses thoroughly with LOM metadata can be a tedious task. In this poster we show how additional inference rules can make this task easier, and allow us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM fields, thus taking an important step towards a complete axiomatization of LOM metadata (with the goal of transforming the LOM definitions from a simple syntactical description into a complete ontology). We used RDF metadata descriptions and an inference language explicitly developed for RDF (TRIPLE) to represent metadata and axioms. We show how these rules can be applied for the extensions of course metadata, the creation of views onto the metadata or metadata consistency checking
Stuck around the Stadium? An Approach to Identify Road Segments Affected by Planned Special Events
No full textThe recent availability of large amount of mobility data has fostered many research efforts to improve mobility prediction. Lots of these studies are focused on learning the impact of influencing factors on traffic, such as rush hour or accidents. Nevertheless, only very few have investigated the impact of Planned Special Events (PSEs), such as concerts, soccer games, etc., despite their well-known influence on traffic. In this paper we present an automatic solution to model the impact of PSEs on traffic around the venue of the events. In particular, we answer the question of "which road segments are affected by PSEs?" by identifying which roads show an event specific behavior that can identify the happening of a PSE reliably. For that, we propose a solution based on an Artificial Neural Network (ANN) classifier that is trained on traffic data on event and non-event days for each road. The proposed approach has been evaluated on two different venues in Germany with a leave-one-out cross-validation performed on all the soccer matches played in those locations during the season 2013/14 of the German First League. Results show that the approach can reliably identify road segments affected by PSEs, with an F-Measure up to 0.97
Distributed Bayesian Networks for User Modeling
No full textThe World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms
Towards Exploiting Social Networks for Detecting Epidemic Outbreaks
No full textSocial networks are becoming a valuable source of information for applications in many domains. In particular, many studies have highlighted the potential of social networks for early detection of epidemic outbreaks,
due to their capability to transmit information faster than traditional channels, thus leading to quicker reactions of public health officials. Anyhow, the most of these studies have investigated only one or two diseases, and consequently to date there is no study in the literature trying to investigate if and how different kinds of outbreaks may lead to different temporal dynamics of the messages exchanged over social networks. Furthermore, in case of a wide variability, it is not clear if it would be possible to define a single generic solution able to detect multiple epidemic outbreaks, or if specifically tailored approaches should be implemented for each disease. To get an insight into these open points, we collected a massive dataset, containing more than one hundred million Twitter messages from different countries, looking for those relevant for an early outbreak detection of multiple disease. The collected results highlight that there is a significant variability in the temporal patterns of Twitter messages among different diseases. In this paper, we report on the main findings of this analysis, and we propose a set of steps to exploit social networks for early epidemic outbreaks, including a proper document model for the outbreaks, a Graphical User Interface for the public health officials, and the identification of suitable sources of information useful as ground truth for the assessment of outbreak detection algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Architecture to Process Massive Vehicular Traffic Data
No full textFostered by the big data hype in mobility, many research efforts have been aimed at improving techniques to model vehicular traffic patterns for mobility prediction. Nevertheless, from a practical stance, the industry still faces many technological challenges in bringing solutions on the market.
Especially the scalability and performance of such systems raise major concerns, given the amount of spatio-temporal data to be processed. The common approach in dealing with these issues is to introduce constraints and/or simplifications on both the spatial component of the data and on the employed algorithms, leading to results that are somehow limited. To overcome these issues, in this paper we report on our experiences and our approaches in providing a solution that meets industrial needs with the aim to leverage the computational and storage capabilities of the Cloud to handle massive dataset for providing vehicular traffic predictions. In particular, we present an approach to deal with
real-world datasets to facilitate the knowledge discovery process from this data while matching the business constraints given by the industrial use case
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