An epistemic approach to model uncertainty in data-graphs

TL;DR

This paper introduces a probabilistic framework for modeling and addressing uncertainties and errors in graph databases, focusing on data cleaning and query answering under noisy observations.

Contribution

It extends the concept of probabilistic unclean databases to graph data, defining new computational problems and analyzing their complexity.

Findings

Defined probabilistic models for unclean graph databases.

Formulated data cleaning and query answering problems.

Analyzed computational complexity for different error types.

Abstract

Graph databases are becoming widely successful as data models that allow to effectively represent and process complex relationships among various types of data. As with any other type of data repository, graph databases may suffer from errors and discrepancies with respect to the real-world data they intend to represent. In this work we explore the notion of probabilistic unclean graph databases, previously proposed for relational databases, in order to capture the idea that the observed (unclean) graph database is actually the noisy version of a clean one that correctly models the world but that we know partially. As the factors that may be involved in the observation can be many, e.g, all different types of clerical errors or unintended transformations of the data, we assume a probabilistic model that describes the distribution over all possible ways in which the clean (uncertain) database could have been polluted. Based on this model we define two computational problems: data cleaning and probabilistic query answering and study for both of them their corresponding complexity when considering that the transformation of the database can be caused by either removing (subset) or adding (superset) nodes and edges.