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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Notes
- 1.
We immediately acknowledge the sensitive nature of this information, and the very important privacy issues that they raise.
- 2.
The case study in [9] showed that 194 out of 316 episodes of eating out (61%) had a single PDD, corresponding to a single action in the plan associated with them.
- 3.
- 4.
Low-end scores were due to factors such as absence of NLP and a couple sharing credit cards.
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Kalokyri, V., Borgida, A., Marian, A., Vianna, D. (2017). Semantic Modeling and Inference with Episodic Organization for Managing Personal Digital Traces. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_19
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DOI: https://doi.org/10.1007/978-3-319-69459-7_19
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