AIDBOct 1, 2019

Distance-Based Approaches to Repair Semantics in Ontology-based Data Access

arXiv:1910.00293v1
Originality Incremental advance
AI Analysis

This work addresses inconsistency issues in ontology-based data access, offering an incremental improvement for personalized query processing.

The paper tackles the problem of few query answers in inconsistent ontology-based data access by using syntactic distance to cluster repairs, proposing a generic framework for more personalized query answering.

In the presence of inconsistencies, repair techniques thrive to restore consistency by reasoning with several repairs. However, since the number of repairs can be large, standard inconsistent tolerant semantics usually yield few answers. In this paper, we use the notion of syntactic distance between repairs following the intuition that it can allow us to cluster some repairs "close" to each other. In this way, we propose a generic framework to answer queries in a more personalise fashion.

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