At Scaled Research Opportunity Identification: The Marriage of Explicit and Parametric Knowledge

In an era of rapid digital transformation, orga-nizations often grapple with inadequate and reactive strategic planning, leading to potential market capitalization losses and missed opportunities. Traditional methods such as SWOT analysis, though invaluable, largely rely on localized and subjective information, overlooking the vast sea of external events that can herald new opportunities or threats. This position paper elucidates a novel approach that combines explicit and parametric knowledge, aiming to bridge this critical gap. We introduce an innovative framework that augments the capabilities of pretrained language models (PLMs) for enhanced opportunity identification and pertinent information retrieval. Central to this framework is the integration of a Bayesian Network (BN)-based analytical opportunity model, which is complemented by a knowledge-aware prompt tuning model. Together, these modules facilitate efficient identification of emerging opportunities from an expansive array of web resources. Beyond the technical novelty, our model marks a paradigm shift in strategic decision-making, propelling organizations to factor in comprehensive external insights. As a practical application, our objective is to create an artificial intelligence (AI) model that serves as a central entity for research and educational institutions. This model will offer researchers many options such as team building, access to relevant grant opportunities, identification of upcoming research ideas, and more. This effort not only facilitates the development of proactive organizational strategies but also demonstrates the possibilities of integrating traditional and AI-driven approaches in decision-making processes.