Luca Rossetto, Matthias Baumgartner, Narges Ashena, Florian Ruosch, Romana Pernischová, A. Bernstein
{"title":"A Knowledge Graph-based System for Retrieval of Lifelog Data","authors":"Luca Rossetto, Matthias Baumgartner, Narges Ashena, Florian Ruosch, Romana Pernischová, A. Bernstein","doi":"10.5167/UZH-195148","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":342971,"journal":{"name":"International Workshop on the Semantic Web","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on the Semantic Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5167/UZH-195148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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.