An epistemic approach to model uncertainty in data-graphs
This work addresses data quality issues in graph databases for applications relying on accurate relationship modeling, but it is incremental as it adapts existing probabilistic models from relational databases.
The paper tackles the problem of errors and discrepancies in graph databases by modeling them as probabilistic unclean versions of an underlying clean database, and it defines and studies the computational complexity of data cleaning and probabilistic query answering for transformations involving node/edge removal or addition.
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.