[u' @article{almeida_assessing_2012, title = {Assessing {Ambiguity} of {Context} {Data} in {Intelligent} {Environments}: {Towards} a {More} {Reliable} {Context} {Managing} {System}}, volume = {12}, copyright = {http://creativecommons.org/licenses/by/3.0/}, shorttitle = {Assessing {Ambiguity} of {Context} {Data} in {Intelligent} {Environments}}, url = {http://www.mdpi.com/1424-8220/12/4/4934}, doi = {10.3390/s120404934}, abstract = {Sensors, an international, peer-reviewed Open Access journal., Modeling and managing correctly the user context in Smart Environments is important to achieve robust and reliable systems. When modeling reality we must take into account its ambiguous nature. Considering the uncertainty and vagueness in context data information it is possible to attain a more precise picture of the environment, thus leading to a more accurate inference process. To achieve these goals we present an ontology that models the ambiguity in intelligent environments and a data fusion and inference process that takes advantage of that extra information to provide better results. Our system can assess the certainty of the captured measurements, discarding the unreliable ones and combining the rest into a unified vision of the current user context. It also models the vagueness of the system, combining it with the uncertainty to obtain a richer inference process.}, language = {en}, number = {4}, urldate = {2013-01-16}, journal = {Sensors}, author = {Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, month = apr, year = {2012}, note = {Modeling and managing correctly the user context in Smart Environments is important to achieve robust and reliable systems. When modeling reality we must take into account its ambiguous nature. Considering the uncertainty and vagueness in context data information it is possible to attain a more precise picture of the environment, thus leading to a more accurate inference process. To achieve these goals we present an ontology that models the ambiguity in intelligent environments and a data fusion and inference process that takes advantage of that extra information to provide better results. Our system can assess the certainty of the captured measurements, discarding the unreliable ones and combining the rest into a unified vision of the current user context. It also models the vagueness of the system, combining it with the uncertainty to obtain a richer inference process.}, keywords = {Artificial Intelligence, Context-Aware Computing, Data analysis, Fuzzy logic, Q1, Vagueness, ambient intelligence, data fusion, inference, jcr1.739, ontologies, phd, semantic reasoning, uncertainty}, pages = {4934--4951}, } ']