Doctor of Philosophy (PhD), Wright State University, 2016, Computer Science and Engineering PhD

Understanding users' latent intents behind search queries is essential for satisfying a user's search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Hence, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and forms one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd-sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been eva (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Michael Raymer Ph.D. (Committee Member); Jyotishman Pathak Ph.D. (Committee Member) Subjects: Computer Science