Abstract
We consider graphs to model uncertain facts as edges, linking involved entities, with weights reflecting uncertainty degree. Rules are used to create new edges from the existing ones, and methods to propagate uncertainty measures are defined using a suitable theoretical framework. We also consider new rules, mined from graphs containing uncertain information and answer sets obtained using such rules. We then use Argument Graphs and Possibility Networks to evaluate the conclusions that can be drawn from the facts, taking into account their uncertainty. Finally, information revision is discussed for cases when a new piece of information is added to the graph.
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Notes
- 1.
We received the data from the investigators to produce research and proof of concepts, with permission of publishing general research results, but not to share the data. All the files we received were carefully anonymized, discarding all unnecessary information (e.g. addresses) and changing names to numerical ids, phone numbers to random digits and so on. In this way no real persons, events or places could be recognized.
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Bellandi, V., Frati, F., Siccardi, S., Zuccotti, F. (2022). Management of Uncertain Data in Event Graphs. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_47
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DOI: https://doi.org/10.1007/978-3-031-08971-8_47
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