A Knowledge Graph-based System for Retrieval of Lifelog Data
Luca Rossetto, Matthias Baumgartner, Narges Ashena, Florian Ruosch, Romana Pernisch, Abraham Bernstein · Zurich Open Repository and Archive (University of Zurich) · 2020
Lifelogging is a phenomenon where practitioners record an increasing part of their subjective daily experience with the aim of later being able to use these recordings as a memory aid or basis for datadriven self improvement.The resulting lifelogs are, therefore, only useful if the lifeloggers have efficient ways to search through them.The logs are inherently multi-modal and semi structured, combining data from several sources, such as cameras and other wearable physical as well as virtual sensors, so representing the data in a graph structure can effectively capture all produced interrelations.Since annotating each entry with a sufficiently large semantic context is infeasible, either manually or automatically, alternatives must be found to capture the higher level semantics.In this paper, we demonstrate LifeGraph, a first approach of creating a Knowledge Graph-based lifelog representation and retrieval solution, able of capturing a lifelog in a graph structure and augmenting it with external information to aid with the association of higher-level semantic information.