The data produced by efforts such as life logging is commonly multi modal and can have manifold interrelations with itself as well as external information. Representing this data in such a way that these rich relations as well as all the different sources can be leveraged is a non-trivial undertaking. In this paper, we present the first iteration of LifeGraph, a Knowledge Graph for lifelogging data. Life-Graph aims at not only capturing all aspects of the data contained in a lifelog but also linking them to external, static knowledge bases in order to put the log as a whole as well as its individual entries into a broader context. In the Lifelog Search Challenge 2020, we show a first proof-of-concept implementation of LifeGraph as well as a retrieval system prototype which utilizes it to search the log for specific events.

Video is a very expressive medium, able to capture a wide variety of information in different ways. While there have been many advances in the recent past, which enable the annotation of semantic concepts as well as individual objects within video, their larger context has so far not extensively been used for the purpose of retrieval. In this paper, we introduce the first iteration of VideoGraph, a knowledge graph-based video retrieval system. VideoGraph combines information extracted from multiple video modalities with external knowledge bases to produce a semantically enriched representation of the content in a video collection, which can then be retrieved using graph traversal. For the 2021 Video Browser Showdown, we show the first proof-of-concept of such a graph-based video retrieval approach. Keywords Interactive video retrieval Knowledge-graphs Multi-modal graphs

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