This SBIR Phase I project aims to develop a working prototype for an interactive search and visualization tool that redefines how social scientists and business practitioners interested in scholarly research findings identify such findings. Instead of reading through numerous, unstructured, and often irrelevant text results (such as those produced by traditional academic search engines), users get to (a) instantly view and easily navigate research findings via intuitively-structured, clickable visual maps, and (b) accurately identify those papers most relevant to them, thanks to semantically intelligent indexing and visual reconciliation. This efficiency-enhancing tool makes research findings substantially easier to understand and explore, while drastically cutting down search times for such findings (to half or even less) which will ultimately enhance the efficiency of research endeavors at U.S. universities. Moreover, various inquiries have shown that practitioners in applied fields such as marketing or management place high value on academic research in the social sciences, yet often find such research difficult to understand. The proposed solution distills social science findings into an easily digestible format, hence facilitating the knowledge transfer between academia and businesses, and enhancing the value of academic research to society. Finally, by being marketed as a subscription-based service to both academics and business practitioners, the proposed tool has the potential to generate substantial commercial value in the long term (up to $50 million in annual revenue). The proposed tool fundamentally alters the existing search paradigm in the social sciences, by changing both the way in which research findings from academic papers are indexed, and how such findings are visually presented. It combines an innovation on the back-end (i.e., using Natural Language Understanding (NLU) to automatically extract concepts and causal relationships from academic research papers, and semantically categorize those concepts against a set of discipline-specific thesauri) with an innovation on the front-end (i.e., using aggregate causal mapping to represent the academic literature in the form of interactive maps that can be visually explored and narrowed down in order to precisely locate relevant papers). The main research objective is to test the feasibility of (1) using NLU for accurately identifying and extracting the underlying concepts/variables and causal structure of the studies described in a large set of social science research papers (approximately 1,000 published papers), and of (2) automatically rendering the extracted information in the form of causal maps that both academic and non-academic users can intuitively understand and navigate. This research objective has been reached if a group of test users employ the proposed tool to successfully identify research papers examining particular concepts and relationships, and do so in about half the time needed when using a traditional academic search engine for the same task.
