# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 1 Introduction  
Nowadays, many smart applications such as smart health, smart home and smart city [1, 2] require efficient situations enrichment and adaptive service delivery through recommendation systems. The recommendation is the most helpful way to assist users in their everyday activities. The proposed solutions for situations enrichment and service delivery in such applications require low execution time and high accuracy level to meet the user's requirements in a large number of available rules [3]. In the smart environment, ontologies are widely standard models and supporting techniques for improving the recommendation of services and quality of life by detecting the relevant situations of a user and easy access to their related services [4]. However, ontology models can be applied to different smart domains by exploiting their capacities to capture and exchange information through unified view of the data. High accuracy and fastness are the most desired targets for any recommendation ontology-based approach. High accuracy means closing gaps between what users want and available situation's services, while fastness ensures that situation's services are recommended in a short response time. In general, the existing ontologies models use a static mechanism to recommend situations' services. This kind of static recommendation of situations' services results in poor consideration of the whole user's context, thus preventing the dynamic recommendation of relevant situations and long-time adaptation of services to users according todevices' heterogeneity. In fact, considering rich contextual factors such as the user's role, user's location, day and time, user's implicit and explicit preferences, user's history and current situation during the recommendation process may much better improve the accuracy of the recommended rule items. Furthermore, we aim to assist the user in different moving scenarios by providing pertinent rules/services considering rich contextual factors (e.g., its user's role and location, day and time, user's implicit and explicit preferences, user's history, user's current situation, etc.). For instance, the system will not recommend cinema services or any entertainment services during the weekday although if the user is near to the cinema. However, the system recommends these services on the weekend. Other approaches have been identified in the literature [4] to recommend the services in the smart environment. Most of them have used content or collaborative approaches, which are based only on user preferences. However, these techniques' performance decreases facing up the complexity of managing a huge number of situations and mobile devices or looking for suitable services while respecting time requirements. For smart environments, we need a roadmap to combine semantic models with uncertain methods to perform the recommendation system according to the accuracy rate and response time. Recently, several surveys and studies in the field of rule-based recommendation [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] have focused exclusively on recommendation process techniques regardless of the following questions: what is the context information to take into consideration for a better understanding and an effective result? Which device is the best to use considering the immediate situation? How to develop an efficient recommendation mechanism to inject new situation rules extracted from users' agendas to rapidly filling up the agendas of newly registered users. Most existing recommendation techniques offer relevant rules and suitable services to persons but introduce considerable challenges when it comes to pervasive computing. Existing techniques do not deal with a huge number of situation rules with several moving scenarios (e.g., at home, at work, at car, etc.) and dynamic context evolution exhibiting varying degrees of precision, accuracy and processing time, nor do they handle multiple devices at the run-time.