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In pervasive computing environment the contexts are usually imprecise and incomplete due to unreliable connectivity, user mobility, and resource constraints. In this paper we present an approach based on the Dempster-Shafer Theory (DST) for the reasoning with imprecise context. To solve the two fundamental issues of the DST, computation intensiveness and the Zadeh paradox, we filer out excrescent...
We propose the Tag Allocation Model (TAM) to model social annotation data. TAM is a probabilistic generative model, its key feature is finding the latent reason for each tag. A latent reason can be any discrete features of the document (such as words) or a global noise variable. Inferring the reason for each tag helps TAM reduce the ambiguity of a document with multiple tags. By introducing noise...
Significant increase in collected data for investigative tasks and the increased complexity of the reasoning process itself have made investigative analytical tasks more challenging. These tasks are time critical and typically involve identifying and tracking multiple hypotheses; gathering evidence to validate the correct hypotheses and eliminating the incorrect ones. In this paper we specifically...
This paper describes the design and implementation of backward chained clustered RDFS reasoning in 4store. The system presented, called “4s-reasoner”, adds no overhead to the import phase and yet performs reasonably well at the query phase. We also demonstrate that our solution scales over clusters of commodity servers providing an optimal solution that balances infrastructure cost and performance...
This paper presents the coauthor network topic (CNT) model constructed based on Markov random fields (MRFs) with higher-order cliques. Regularized by the complex coauthor network structures, the CNT can simultaneously learn topic distributions as well as expertise of authors from large document collections. Besides modeling the pairwise relations, we model also higher-order coauthor relations and...
Our goal is to understand limitations of simplicity of knowledge structures and reasoning processes in Multiagent Systems. Therefore, we propose a framework that integrates external storage media and a capacity-constrained Multiagent System. Agents can store knowledge internally and on external storage media located in an environment. In some cases, agents either have to forget or to store knowledge...
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