Investigating the Use of Data Mining for Knowledge Discovery

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The practice of "lifelogging" involves documenting an increasing amount of one's subjective everyday experience with the intention of using the recordings in the future as a memory aid or the foundation for data-driven self-development. Therefore, the usefulness of the generated lifelogs depends on the lifeloggers' ability to efficiently sift through them. The logs' intrinsic multi-modality and semi-structure allow them to combine data from a variety of sources, including cameras and other wearable physical and virtual sensors. As a result, expressing the data in a graph structure allows for the effective capturing of all created interrelations. Alternative methods must be developed to capture the higher-level semantics because it is impossible to manually or mechanically annotate each entry with a significant amount of semantic context. We describe an Improved Life Graph (ILG), a first method for building a Knowledge Graph-based lifelog representation and retrieval solution, which can capture a lifelog in a graph structure and augment it with external data to help with the connection of higher-level semantic information.