Tacit Knowledge Mining John Canny Computer Science Division UC Berkeley

UCB HCC Motivation People often collaborate across geographic, cultural and disciplinary boundaries. Workgroups are more short-lived today. There is less time to learn each other’s skills sets, to develop trust, and a “voice” within the group. On the other hand, there is a wealth of information available in groupware systems about people’s interactions with each other and with data.

UCB HCC An opportunity We know that much of the useful knowledge in a group or organization is “tacit”. Can we recover and use tacit knowledge? Human knowledge and knowhow is difficult and expensive to codify. But the knowledge encoded in activity data may be less so. Examples: expertise discovery, group structure, document dependencies.

UCB HCC Platform: Lotus Notes for now Data sources:  Direct messaging: sender, receiver, time, duration..  Topical discussions or threaded .  Document access (by links or directories)  Document search (by text queries)  Annotations on documents.

UCB HCC Analysis methods: Social Networks. Centrality measures for estimating authority and prestige.

UCB HCC Analysis methods: Clustering. Discovering tacit groups, and related sets of documents. Classification. Use a knowledge hierarchy to classify documents, and compute expertise profiles.

UCB HCC What we want to assist: Expertise discovery. Expertise profiles include authority in each area. Document access. We compute document authorities and use them in ranking. Group dynamics. Does the communication network contain any problematic structures?

UCB HCC What we want to assist: Sensemaking, document context. Record the document creation process. Prioritize records. Attention management. Infer a current task and adjust awareness of documents and other people according to their proximity to the task. Perspectives. Organize annotations (when available) on a document according to the expertise of author and annotator.