Overview
The management of personal data is becoming increasingly important for value-added knowledge work for all economic actors in the 4th sector, as the flood of data grows exponentially and the tool landscape becomes increasingly fragmented. Since no standard solution for personal data management fits all users and every team, there are many isolated solutions.
The goal of Vision X-MAS (cross-mas) is to contextualize business-, public-, shared- and personal data across silo platforms (cross-platform, cross = X) in a meta-service as simple and intuitive as possible by automatically generating context (mediation), liniking (association) and filtering (search). To do so, we investigate automated approaches using machine learning, and social media approaches such as tagging and linking.
The following research questions are answered:
1. Automatic Tagging: How Can the Keyword Extraction Algorithm of Kaufmann et al. (2014) to be extended to keyphrase extraction?
2. Automatic networking: How can relationships between documents be recognized automaticall with relationship extraction techiques?
3. Knowledge sharing: How can distributedly stored knowledge graphs be elaborated collaboratively?
4. User Interaction: How can these algorithms be integrated into the value chain of knowledge work so that the increase in efficiency can be measured quantitatively?
