A hybrid approach to finding relevant social media content for complex domain specific information needs

While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and "intelligible constructs'' not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage, and sentiments, which could be important to the holistic needs of the information seeker. In this paper, we present a hybrid approach to domain specific information retrieval (or knowledge-aware search) that integrates ontology-driven query interpretation with synonym-based query expansion, and domain specific rules, to facilitate search. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: (1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and (2) a low-level CFG that enables interpretation of specific expressions that belong to such patterns. These low-level expressions occur as concepts from four different categories of data: (1) ontological concepts, (2) concepts in lexicons (such as emotions and sentiments), (3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and (4) domain specific expressions (such as date, time, interval, frequency, and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE, which provides search support for complex domain specific information needs in prescription drug abuse epidemiology. When applied to a corpus of over 1 million drug abuse-related web forum posts, our search framework proved effective in retrieving relevant documents when compared with three existing search systems. Published by Elsevier B.V.