An epistemic approach to model uncertainty in data-graphs

ABRIOLA, SERGIO; CIFUENTES, SANTIAGO; MARTÍNEZ, MARÍA VANINA; PARDAL, NINA; PIN, EDWIN

Helsinki

Seminario; Logic Seminar of the Helsinki Logic Group; 2022

University of Helsinki

Graph databases are becoming widely successful as data models that allow to effectively represent and process complex relationships among various types of data. Data-graphs are particular types of graph databases whose representation allows both data values in the paths and in the nodes to be treated as first class citizens by the query language. As with any other type of data repository, data-graphs may suffer from errors and discrepancies with respect to the real-world data they intend to represent. In this talk, we explore the notion of probabilistic unclean data-graphs, in order to capture the idea that the observed (unclean) data-graph is actually the noisy version of a clean one that correctly models the world, but that we know of partially. As the factors that yield to such observation may be the result of different types of clerical errors or unintended transformations of the data, and depend heavily on the application domain, we consider an epistemic probabilistic model that describes the distribution over all possible ways inwhich the clean (uncertain) data-graph could have been polluted. Based on this model we define and study data cleaning and probabilistic query answering for this framework, yielding complexity results when the transformation of the data-graph can be caused by either removing (subset), adding (superset), or modifying (update) nodes and edges.