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In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured flow and sharing a common vocabulary of tags. Our approach is based on the idea of representing documents as fuzzy bags and, using a measure of comparison, evaluating structural similarities between them. Then we suggest how to organize the extracted knowledge(More)
Semantic Web languages like OWL and RDFS promise to be viable means for representing metadata describing users and resources available over the Internet. Recently, interest has been raised on the use of such languages to represent individual data items contained in Personally Identifiable Information (PII), supporting fine-grained release. To achieve this(More)
—We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically(More)
Organizational risk management should not only rely on protecting data and information but also on protecting knowledge which is underdeveloped in many cases or measures are applied in an uncoordinated, dispersed way. Therefore, we propose a consistent top-down translation from the organizational risk management goals to implemented controls to overcome(More)
Ontology-based modelling is becoming increasingly important in the design of complex knowledge management applications. However , many problems related to large-scale ontology development, deployment and collaborative maintenance of related metadata still remain to be solved. Making online modifications to an ontology whose concepts are simultaneously being(More)
This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types(More)