A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases
This work addresses data inconsistency and entity ambiguity in knowledge bases, offering a combined approach that is incremental in improving query reliability for database and AI applications.
The paper tackles the problem of integrating entity resolution and query answering in knowledge bases with dependencies, proposing a framework that uses equivalence classes and value sets to handle inconsistencies. The result is a chase procedure that never fails and produces a universal solution for certain answers to conjunctive queries when it terminates.
We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules. We define the semantics of the KB in terms of special instances that involve equivalence classes of entities and sets of values. Intuitively, the former collect all entities denoting the same real-world object, while the latter collect all alternative values for an attribute. This approach allows us to both resolve entities and bypass possible inconsistencies in the data. We then design a chase procedure that is tailored to this new framework and has the feature that it never fails; moreover, when the chase procedure terminates, it produces a universal solution, which in turn can be used to obtain the certain answers to conjunctive queries. We finally discuss challenges arising when the chase does not terminate.