One of the greatest challenges of an enterprise's service center is to ensure that their engineers and customers are provided with the right information in a timely fashion. For this purpose, modern organizations operate a wide range of information support systems to assist customers with critical service requests and to provide proactive monitoring, where possible, to prevent service requests from occurring in the first place. It is often the case that relevant information is scattered over the Internet and/or maintained on disparate systems, buried in large amount of noisy data, and in heterogeneous formats, thereby complicating the access to reusable knowledge and extending the response time to reach a resolution. To address these challenges, in this paper we propose an effective knowledge mining solution to improve the quality of service request resolution. We model the service resolution problem as an online search and classification problem, and use domain knowledge in the form of ontology to guide effective machine learning. Our proposed solution has been extensively evaluated with experiments and has been used in a real enterprise customer center.

Online social networks have enabled communication, collaboration and information sharing in the healthcare domain. Despite the benefits offered by new healthcare social networking applications, there are many challenges. The unique trust and privacy requirements of healthcare make trust management an utmost important issue. In this paper, we propose a personalized self-managed trust model, MedTrust, to establish trust relationships among participating healthcare social network users. The model identifies key trust factors in a healthcare social networking environment, and uses fuzzy logic to represent and evaluate trust. Experiments were conducted and demonstrated that our approach can generate accurate and realistic outcomes in assessing trust and predicting the scope and impact of different trust factors.

Social media is rapidly changing the nature and speed of healthcare interaction. As more and more people go online to search for their health-related issues, providing them with appropriate information would save them from being overwhelmed by mountains of information. For this purpose, in this paper we propose a personalized healthcare recommending system to recommend highly relevant and trustworthy healthcare-related information to users. The system identifies key factors impacting the recommendation in a healthcare social networking environment, and uses semantic web technology and fuzzy logic to represent and evaluate the recommendation. Experiments were conducted and demonstrated that our approach can generate good outcomes in making recommendation and predicting the scope and impact of different factors.

Nowadays more and more people are going online to seek for information, service and products to address their health concerns. Given the mountains of online health-related data, it is time and energy consuming for people to locate the right information directly related to their health concerns. Therefore, effective recommendation is very important to save people's time and energy by providing them with the appropriate information. In this paper, we propose a recommendation system which utilizes semantic web technology and healthcare social networking to provide personalized recommendation to speed patient recovery and improve healthcare outcomes. Extensive experiments have been performed to evaluate the performance of the system. The results demonstrated the effectiveness of the proposed strategy.