Recently Twitter, has emerged as one of the primary medium for sharing and seeking of the latest information related to variety of the topics including health information. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, identification of useful information from the deluge of tweets is one of the major challenge. Twitter search is limited to keyword based techniques to retrieve information for a given query and sometimes the results do not contain real-time information. Moreover, Twitter does not utilize semantics to retrieve results. To address these challenges, we developed a system (Social Health Signals) by leveraging rich domain knowledge to extract relevant and reliable health information from Twitter in near real-time. We have used semantics based techniques 1) to retrieve relevant and reliable health information shared on Twitter in real-time 2) to enable question answering 3) to rank results based on relevancy, popularity and reliability 4) to semantically categorize the information.