Reasoning about Explanations for Negative Query Answers in DL-Lite

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for Description Logics, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for instance and conjunctive query answering over DL-Lite ontologies by adopting abductive reasoning; that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning tasks we consider existence and recognition of an explanation, and relevance and necessity of a given assertion for an explanation. We characterize the computational complexity of these problems for arbitrary, subset minimal, and cardinality minimal explanations

Explaining Query Answers under Inconsistency-Tolerant Semantics over Description Logic Knowledge Bases (Extended Abstract)

The problem of querying description logic (DL) knowledge bases (KBs) using database-style queries (in particular, conjunctive queries) has been a major focus of recent DL research. Since scalability is a key concern, much of the work has focused on lightweight DLs for which query answering can be performed in polynomial time w.r.t. the size of the ABox. The DL-Lite family of lightweight DLs [10] is especially popular due to the fact that query answering can be reduced, via query rewriting, to the problem of standard database query evaluation. Since the TBox is usually developed by experts and subject to extensive debugging, it is often reasonable to assume that its contents are correct. By contrast, the ABox is typically substantially larger and subject to frequent modifications, making errors almost inevitable. As such errors may render the KB inconsistent, several inconsistency-tolerant semantics have been introduced in order to provide meaningful answers to queries posed over inconsistent KBs. Arguably the most well-known is the AR semantics [17], inspired by work on consistent query answering in databases (cf. [4] for a survey). Query answering under AR semantics amounts to considering those answers (w.r.t. standard semantics) that can be obtained from every repair, the latter being defined as an inclusion-maximal subset of the ABox that is consistent with the TBox. A more cautious semantics, called IAR semantics The need to equip reasoning systems with explanation services is widely acknowledged by the DL community. Indeed, there have been numerous works on axiom pinpointing, in which the objective is to identify (minimal) subsets of a KB that entail a given TBox axiom (or ABox assertion

Erklärung fehlender Ergebnisse bei der Verarbeitung hierarchischer Daten in Spark

Es existieren einige Algorithmen, die Entwicklern bei der Fehlersuche bei einer Datenbankanfrage helfen. Diese Arbeiten beantworten, wieso bestimmte Daten nicht in der Ergebnismenge für eine Anfrage vorhanden sind oder bestimmte nicht erwartete Daten in der Ergebnismenge erscheinen (Why-not-Frage). Für Anfragesprachen, die hierarchische Daten unterstützen, bestehen bisher aber nur wenige Arbeiten. In dieser Arbeit wird untersucht, welche Besonderheiten es für Why-not-Fragen bei hierarchischen Daten gibt. Dazu wird betrachtet, welche besonderen Fragestellungen dafür möglich sind und wie diese geeignet beantwortet werden können. Dabei wird auch ein konkreter Algorithmus für Python entworfen und implementiert. Anhand von diesem kann mit Hilfe eines Beispiels untersucht werden, ob der Algorithmus effizient und effektiv genug ist Why-not-Fragen zu beantworten