Social Choice Methods for Database Aggregation
This work addresses database aggregation for knowledge representation, but it is incremental as it applies existing social choice techniques to a new context.
The paper tackles the problem of aggregating information from multiple sources, each represented as a first-order relational database, by identifying aggregators that preserve integrity constraints and characterizing query languages where aggregated query answers match individual answers. It applies social choice theory to database knowledge representation as a foundational step.
Knowledge can be represented compactly in multiple ways, from a set of propositional formulas, to a Kripke model, to a database. In this paper we study the aggregation of information coming from multiple sources, each source submitting a database modelled as a first-order relational structure. In the presence of integrity constraints, we identify classes of aggregators that respect them in the aggregated database, provided these are satisfied in all individual databases. We also characterise languages for first-order queries on which the answer to a query on the aggregated database coincides with the aggregation of the answers to the query obtained on each individual database. This contribution is meant to be a first step on the application of techniques from social choice theory to knowledge representation in databases.