EarthQA is a question answering engine that accepts questions in natural language (English) that ask for satellite images satisfying certain criteria and returns links to such datasets, that can be then downloaded from the CREODIAS cloud platform. The questions can refer to image metadata (e.g., satellite platform, sensing period, cloud cover etc.) but also to entities from the knowledge graph DB-pedia (e.g., Mount Etna or the city of Munich). In this way, the users can ask questions like "Give me Sentinel-2 satellite images that show Mount Etna, have been taken in February 2021 and have cloud cover less than 10%."

Query rewriting is an important technique for answering queries over data described using ontologies. In query rewriting the input, a conjunctive query (CQ) |$q$| and an ontology |$\mathcal {O}$|⁠, is transformed into a new datalog query that captures all answers of |$q$| over |$\mathcal {O}$| and any dataset |$D$|⁠. This process can be time-consuming as it is of high computational complexity. In many real-world applications, this can be particularly problematic as they involve frequent and relatively small modifications on quite large ontologies. Hence, a drawback of most of modern query rewriting systems is that every time the initial ontology is modified, e.g. when new axioms are added or existing ones removed, they compute a new rewriting from scratch. In this paper, we study the problem of computing a rewriting for a CQ over an ontology that has been modified. We do this by reusing the information obtained by the extraction of some previous rewriting with the goal of performing the least possible computations. We study the problem theoretically, present detailed algorithms for both ontology revision and ontology contraction and finally, present an extensive experimental evaluation using the well-known query rewriting systems Requiem and Rapid.