Abstract: Knowledge management (KM) typically includes processes for representing, acquiring, storing, accessing, and disseminating knowledge. In today’s economy, the creation and access to new forms of knowledge are recognized as a firm’s competitive source of advantage, innovation, and growth. In search for innovating approaches, analytics offers opportunities for extended KM capabilities that include the uncovering of hidden layers of organizational knowledge. While many KM frameworks consider document collections, or corpora, as sources of intelligent information, processes for systematic transformation of individuals’ tacit knowledge into community tacit knowledge (collective tacit knowledge) are understudied. In a typical data warehouse design, activity and transaction events are recorded in a fact table that is surrounded by dimensions that represent entities such as user, product, time, etc. Inspired by this data warehouse “star schema”, we propose a dimensional model that includes hidden aspects of a corpus, such as topic or opinion, as derived star schema dimensions. In a case study that focuses on Twitter as a knowledge community, we demonstrate that the derived dimensions can be combined with other transactional facts, such as the username, or the tweet text, and derived facts, such as a tweet’s topic, or a tweet’s sentiment, to uncover the collective tacit knowledge in Twitter communities.
