TY - GEN
T1 - A knowledge graph-based system for retrieval of lifelog data
AU - Rossetto, L.
AU - Baumgartner, M.
AU - Ashena, N.
AU - Ruosch, F.
AU - Pernisch, R.
AU - Bernstein, A.
PY - 2020
Y1 - 2020
N2 - © 2020 CEUR-WS. All rights reserved.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.
AB - © 2020 CEUR-WS. All rights reserved.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.
M3 - Conference contribution
VL - 2721
T3 - CEUR Workshop Proceedings
SP - 224
EP - 228
BT - ISWC-Posters 2020 - Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice, co-located with 19th International Semantic Web Conference, ISWC 2020
A2 - Taylor, K.
A2 - Goncalves, R.
A2 - Lecue, F.
A2 - Yan, J.
PB - CEUR-WS
T2 - 19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020
Y2 - 1 November 2020 through 6 November 2020
ER -