BEKsee

BEKsee is a term used in information science and artificial intelligence to describe a framework for designing and evaluating automated or semi-automated agents that seek knowledge through iterative exploration. It encompasses methodologies for modeling goals, selecting informative queries, and integrating retrieved information into decision-making processes.

The term BEKsee appeared in academic and industry discourse in the early 2020s and is used with varying expansions of the acronym. There is no universally accepted definition, and different groups have proposed different architectures under the BEKsee umbrella. As a result, BEKsee is often treated as a family of approaches rather than a single standardized model.

Core components commonly cited include an agent model of user or task, an exploration strategy that balances depth and breadth of search, a retrieval or knowledge base interface, and an evaluation module that measures discovery effectiveness and user satisfaction. BEKsee-inspired designs may combine techniques from information retrieval, reinforcement learning, and knowledge graphs to facilitate targeted, iterative information discovery.

Applications for BEKsee concepts include intelligent tutoring systems, customer support automation, research assistants, and discovery tools for large document collections. The overarching goal is to improve efficiency in information seeking and to tailor results to user state and intent, sometimes in real time.

In absence of a standardized definition, BEKsee remains a topic of ongoing scholarly and industry discussion. Adoption and implementation vary by domain, with debates focusing on transparency, reproducibility, and potential biases in automated exploration. See also information retrieval, reinforcement learning, knowledge graphs, and user modeling.