Semantic modeling and inference with episodic organization for managing personal digital traces: (Short paper)

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Abstract

Many individuals generate a flood of personal digital traces (e.g., emails, social media posts, web searches, calendars) as a byproduct of their daily activities. To facilitate querying and to support natural retrospective and prospective memory of these, a key problem is to integrate them in some sensible manner. For this purpose, based on research in the cognitive sciences, we propose a conceptual modeling language whose novel features include (i) the super-properties “who, what, when, where, why, how” applied uniformly to both documents and autobiographic events; and (ii) the ability to describe prototypical plans (“scripts”) for common everyday events, which in fact generate personal digital documents as traces. The scripts and wh-questions support the hierarchical organization and abstraction of the original data, thus helping end-users query it. We illustrate the use of our language through examples, provide formal semantics, and present an algorithm to recognize script instances.

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APA

Kalokyri, V., Borgida, A., Marian, A., & Vianna, D. (2017). Semantic modeling and inference with episodic organization for managing personal digital traces: (Short paper). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10574 LNCS, pp. 273–280). Springer Verlag. https://doi.org/10.1007/978-3-319-69459-7_19

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