# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 1 Introduction  
Due to the proliferation of Artificial Intelligence (AI), the Internet of Things (IoT), Social Network Services (SNS), and e-health, a huge amount of data is generated, integrated and aggregated for the decision making process [1]; however, the quality of the data is questionable, along with associated risks [2; 3]. It has been estimated that 40% of the expected value from business targets could not be achieved due to the bad data quality, and it affects 20% on the productivity of laborers [4]. In the article [5], it is mentioned that 70% of participants reveal that their business faced hardships due to poor data quality. The data produced and used in the organizations contain 1-5% of poor data quality [6]. This may be one of the reasons that organizations face hardship and could not be able to sustain the business for a longer duration. According to the estimation done by the International Data Corporation (IDC) that the big data market will increase to 136 billion per year and the bad data cost can also be increased with the same speed [7]. The 2016 Harvard business review report indicates that the knowledge workers waste 50% time in data hunting and fixing of errors. Similarly, data scientist spends 60% of their time on the cleaning and organization of data [8]. Therefore, the qualityof data is important for the organizations and it should be checked and measured before using it in the applications and services. Data quality is more important in the healthcare domain. The magnificent assessment of data quality leads to provide better services and efficient resource management. The data used in the services should be checked in terms of quality for accurate planning and decisions. On the other hand, unchecked data from unreliable resources may cause serious damages to the reputation and loss of businesses. In the healthcare domain, a large quantity of data is collected such as electronic health records, laboratory results, patients' health status monitoring data through wearable and non-wearable sensors, etc. However, the potential of that data could not be exploited because of poor quality and low confidence [9; 10; 11]. There are many reasons for poor quality healthcare data such as wear and tear of healthcare equipment, untrainable staff, and different formats, etc. By using machine learning or deep learning models, we can estimate the quality of healthcare data by eliminating the deficiencies of subjective measures. So that the healthcare data could be fully utilized without any risk for better patient care and delivery of high-quality services. In the healthcare domain, there are various governmental, non-profit organization and business stakeholders who can get benefits from well-defined contextual data quality assessment mechanisms. The advantages of contextual data quality analytics for various stakeholders in the healthcare environment have been summarized in Figure 1.  
Quality of the same data may be different for the various applications and users; because each application has its context and requirements to use that data. Data quality refers that the data is fit for use in a specific context and it can be defined as \"the standard of something as measured against other  
Figure 1: Contextual data quality environment in the healthcare domain. things of a similar kind\" [12; 13]. The International Organization for Standardization (ISO) defines data quality as \"characteristics of data that relate to their ability to satisfy stated requirements\". Moreover, data quality can be specified as the \"degree to which the characteristics of data satisfy stated and implied need when used under specified conditions\". To assess data quality various metrics have been used such as completeness, accuracy, timeliness, uniqueness, reliability, consistency, reputation, interpretability, and relevancy, etc. [10; 11; 14]. [PERSON] et al.