We present here an approach for dynamic ontology integration for a multi-agent environment, in which each agent holds the ontologies of its acquaintances (i.e., other agents of its interest) as the integrated partial ...
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We present here an approach for dynamic ontology integration for a multi-agent environment, in which each agent holds the ontologies of its acquaintances (i.e., other agents of its interest) as the integrated partial global ontology, which is essential to interpret the local schemas for inter-agent operations. This integration has to be carried out whenever a new acquaintance is added or when the local ontology of an acquaintance changes. The approach described is general (i.e., independent of any particular thesaurus) and it carries out the integration automatically except for minimal unavoidable human inputs to resolve semantic conflicts if discovered in the process.
When deploying collaborative applications such as instant messaging in ubiquitous computing environments significant enhancements can be afforded by offering additional context information, such as location informatio...
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When deploying collaborative applications such as instant messaging in ubiquitous computing environments significant enhancements can be afforded by offering additional context information, such as location information. However, such environments exert key challenges such as increased diversity of ownership and ad hoc, intermittent network connectivity that suits more decentralized computing architectures. This paper examines how a migration to a more decentralized collaborative architecture can be achieved together with a decentralization of the management of collaborative activities
As context-aware systems become more widespread and mobile there is an increasing need for a common distributed event platform for gathering context information and delivering to context-aware applications. The likely...
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As context-aware systems become more widespread and mobile there is an increasing need for a common distributed event platform for gathering context information and delivering to context-aware applications. The likely heterogeneity across the body of context information can be addressed using runtime reasoning over ontology-based context models. However, existing knowledge-based reasoning is not typically optimised for real-time operation so its inclusion in any context delivery platform needs to be carefully evaluated from a performance perspective. In this paper we propose a benchmark for knowledge-based context delivery platforms and in particular examine suitable knowledge benchmarks for assessing the ability of platforms to deal with semantic interoperability
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific sk...
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Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific sk...
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ISBN:
(纸本)9783885791881
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.
The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding ex...
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The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism
In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for fo...
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In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].
The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of di...
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The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of different soft computing techniques for this purpose. We achieved promising results with regard to classification accuracy and interpretability of rule bases, even though the dataset was rather small, high dimensional and unbalanced. In this paper we reconsider the collection of training examples and their assignment to defect types by the quality experts. We attempt to minimize the uncertainty of the quality experts' subjective and error-prone labelling in order to achieve a higher reliability of the defect detection. We show that refined and more accurate classification models can be built on the basis of a preprocessed training set that is more consistent. Using a partially supervised learning strategy we can report improvements in classification accuracy.
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