## 2 Analysis of the State of the Art
### [PERSON] and [PERSON]: The AMBI2 ONT Ontology
#### 2.2.2 Context Models Comparison  
The previous section has reviewed several significant context-aware systems and the followed approaches for modeling relevant context parameters of the environment depending on the application domain. In fact, this is one of the main problems in context modeling: the lack of model independence from similar domains and, also, the lack of models to be compared. Despite the fact that sometimes the \"primary\" domains are similar (context-aware computing, pervasive environments and ubiquitous computing) regarding the necessity of managing context knowledge, the concrete applications and approaches domains are different. Here, [PERSON] _et al._[49] realize about the lack of formality and expressiveness of previous context models. However, to avoid this problem [PERSON] _et al_. [52] present an ontology-based solution in which context information is modelled in two separated layers:  
* In the upper layer there is an ontology which describes high-level knowledge about the current context and physical environment. * Under it there is the possibility to add and remove ontologies which model low-level information of the current specific domain. On the one hand, this approach allows developers to consider richer information, as activities, and abstract knowledge about the current global context. On the other hand, it makes possible to model specific knowledge of the current sub-domain. Besides, the possibility to plug and unplug these low-level ontologies makes this solution powerful. The solution provided by [PERSON] _et al_. [51] is significant because of the following reasons:  
* It considers high-level information on top of a more specific and domain dependent sub-model. * Activities are modeled as a relevant concept for context in the upper ontology. * The modeled entities are related. Persons are associated with locations and activities, each location is linked with indoor or outdoor entities, activities can be categorized into scheduled or deduced ones, _etc_. As the work by [PERSON] and [PERSON] [47] is a survey of context-aware computing the remarked context characteristics (location and time) are given more as an advice, since there is no model in their work. Nevertheless, they introduce several novelties like the term \"context history\", which might be useful for future predictions about user's behavior and trends. Modeling high-level information allows to perform deeper computations taking into account behavioral characteristics, trends information, inferred knowledge from small pieces of information combinations, _etc_. As can be seen in Table 2 many authors work with high-level data in their context-aware systems. On the other hand, physical context parameters are frequently modeled in the analyzed literature. Location, time and environment conditions, e.g., temperature, pressure, light, noise,... are usually modeled to achieve final system's goal, e.g., adapting the user interface, recommending items or services, filtering resources,.... Besides, several approaches take user related characteristics to fulfill their purposes. For example, [PERSON] _et al_. [43] consider not only physical environment as context information but also several human factors categorized as follows:  
* User information, which gathers knowledge about user's habits, emotional stated and bio-physiological conditions. * Social environment, made up by others users location, social interactions of the current user and knowledge about the behavior of groups of people. * Task, taking into account spontaneous activities, engaged tasks and general goals. A user modeling approach which considers emotional issues of the user is presented by [PERSON] [26]. As is remarked in Section 2.1.1. the author distinguishes between emotions and perceptions to separate both concepts for modeling processes. These approaches are found useful for recommending systems in which user mood and psychological state are relevant to filter multimedia content [4].