Evaluation of Quality of Context Information in U-Health Smart Homes from the DatatypeProperty and ObjectProperty OWL language constructs, as well as from a self- defined class QualityExtension, which models the QoC indicators. Tang, et al. (2007) also proposed an ontology- based approach for modeling quality of infor- mation. Unlike Preuveneers, et al. (2006), they proposed an independent OWL-DL ontology for modeling QoC information. In this model, property class replaces the function of OWL property. Qual- ity class is used by context information services to represent quality information with different QoC parameters. Then, Parameters classes are associated with the current situation. Bu, et al. (2006) proposed a similar ontology- based model that represents only QoC indicators. In order to provide means of representing QoC indicators, QoC parameters, QoC aggregating methods, and QoC measurement methods, Filho, et al. (2010) proposed a new extended ontology- · · Filtering context data: it is necessary to verify the quality of context information used to derive/infer new higher-level con- text information. In this case, QoC indica- tors associated with the set of context raw data may do not reach the required quality levels imposed by QoC threshold defined at system level; Improving context-aware decisions: each context element is associated with one or more QoC indicators that should meet QoC thresholds defined in the decision- making layer in order to control the quality of context. We analyzed the set of QoC indicators pre- sented in Table 4 and quality indicators from the data quality domain (Batini, et al., 2009) in order to indentify similarities between them and to pro- pose a consistent set of QoC indicators. Table 4