Representing temporal vagueness on the
semantic web for historical datasets

[This post is based on the Master Information Sciences project of Fabian Witeczek and reuses text from his thesis. The research is part of VU’s effort in the Intavia project and was co-supervised by Go Sugimoto]

To represent properly temporal data on the Semantic Web, there is a need for an ontology to represent vague or imprecise dates. In the context of his research, Fabian Witeczek developed an ontology that can be used to represent various forms of such vague dates. The engineering process of the ontology started with a requirements analysis that contained the collection of data records from existing Digital Humanities Linked Data sets containing temporally vague dates: Biographynet and Europeana. The occurrences of vagueness were evaluated, and categories of vagueness were defined.

The categories were evaluated through a survey conducted with domain experts in the digital humanities domain. The experts were also questioned about their problems when working with temporally vague dates. The survey results confirmed the meaningfulness of the ontology requirements and the categories of vagueness which were: 1) Unknown deviation, 2) within a time span, 3) before or after a
specific date, 4) date options, and 5) complete vagueness.

Visualization of the vague date ontology

Based on the findings, the ontology was designed and implemented, scoping to year-granularity only. Lastly, the ontology was tested and evaluated by linking its instances to instances of a historical dataset. This research concludes that the presented vague date ontology offers a clear way to specify how vague dates are and in which regard they are vague. However, the ontology requires much effort to make it work in practice for researchers in digital humanities. This is due to precision and deviation values that need to be set for every record within the datasets.

Example SPARQL query using concepts from the vague dates ontology

More information can be found in the Master Thesis, linked below.

The ontology itself is found in Fabian’s github account

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Comparing Synthetic Data Generation Tools for IoT Data

[This post is based on the Bachelor Information Sciences project of Darin Pavlov and reuses text from his thesis. The research is part of VU’s effort in the InterConnect project and was supervised by Roderick van der Weerdt]

The concepts and technologies behind the Internet of Things (IoT) make it possible to establish networks of interconnected smart devices. Such networks can produce large volumes of data transmitted through sensors and actuators. Machine Learning can play a key role in processing this data towards several use cases in specific domains automotive, healthcare, manufacturing, etc. However, access to data for developing and testing Machine Learning is often hindered due to sensitivity of data, privacy issues etc.

One solution for this problem is to use synthetic data, resembling as much as possible real data. In his study, Darin Pavlov conducted a set of experiments, investigating the effectiveness of synthetic IoT data generation by three different tools:

This table shows the results of one of the two Machine Learning detection tests showing how difficult it is to differentiate the synthetic data from the real one with a Machine Learning model. For two datasets, the result is calculated as 1 minus the average ROC AUC score

Darin compared the tools on various distinguishability metrics. He observed that Mostly AI outperforms the other two generators, although Gretel.ai shows similar satisfactory results on the statistical metrics. The output of SDV on the other hand is poor on all metrics. Through this study we aim to encourage future research within the quickly developing area of synthetic data generation in the context of IoT technology.

More details can be found in Darin’s thesis.

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