Reasoning-Driven Contextual Retrieval
Keywords:
Enterprise Intelligence; Knowledge Management; Autonomous Reasoning; Contextual Information Retrieval; Ontological Foundations; Data Layer and Knowledge Layer Separation; Integration Methodologies Knowledge-Centric Enterprise.Abstract
Knowledge-Centric Enterprise Intelligence combines autonomous reasoning with contextual information retrieval to support decision-making that is transparent and evidence-based. Autonomous reasoning automates portions of enterprise decision-making, particularly for problems that recur, must be resolved in real time or at short intervals, or follow predictable patterns. Realizing its predictive potential requires reducing reliance on manual configuration, so that decisions are made in ways that account for uncertainty in the underlying causal models while remaining interpretable within the context of the system that produced them. While autonomous decision support shares similarities with case-based reasoning, its distinguishing focus is on managing the decision-making process itself, rather than merely generating a final recommendation or organizing support for human decision-makers. This work examines the goals, capabilities, limitations, and evaluation metrics of autonomous reasoning in business settings, comparing three paradigms: rule-based, probabilistic, and hybrid approaches.
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