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While contemporary semantic search systems offer to improve classicalkeyword-based search, they are not always adequate for complex domain specificinformation needs. The domain of prescription drug abuse, for example, requiresknowledge of both ontological concepts and 'intelligible constructs' nottypically modeled in ontologies. These intelligible constructs convey essentialinformation that include notions of intensity, frequency, interval, dosage andsentiments, which could be important to the holistic needs of the informationseeker. We present a hybrid approach to domain specific information retrieval(or knowledge-aware search) that integrates ontology-driven queryinterpretation with synonym-based query expansion and domain specific rules, tofacilitate search in social media. Our framework is based on a context-freegrammar (CFG) that defines the query language of constructs interpretable bythe 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 enablesinterpretation of certain specific expressions that belong to such patterns.These low-level expressions occur as concepts from four different categories ofdata: 1) ontological concepts, 2) concepts in lexicons (such as emotions andsentiments), 3) concepts in lexicons with only partial ontology representation,called lexico-ontology concepts (such as side effects and routes ofadministration (ROA)), and 4) domain specific expressions (such as date, time,interval, frequency and dosage) derived solely through rules. Our approach isembodied in a novel Semantic Web platform called PREDOSE developed forprescription drug abuse epidemiology.  Keywords: Knowledge-Aware Search, Ontology, Semantic Search, BackgroundKnowledge, Context-Free Grammar

A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs