Inconsistency-Tolerant Querying of Description Logic Knowledge Bases

Abstract

Abstract. An important issue that arises when querying description logic (DL) knowledge bases is how to handle the case in which the knowledge base is inconsistent. Indeed, while it may be reasonable to assume that the TBox (ontology) has been properly debugged, the ABox (data) will typically be very large and subject to frequent modifications, both of which make errors likely. As standard DL semantics is useless in such circumstances (everything is entailed from a contradiction), several alternative inconsistency-tolerant semantics have been proposed with the aim of providing meaningful answers to queries in the presence of such data inconsistencies. In the first part of this chapter, we present and compare these inconsistency-tolerant semantics, which can be applied to any DL (or ontology language). The second half of the chapter summarizes what is known about the computational properties of these semantics and gives an overview of the main algorithmic techniques and existing systems, focusing on DLs of the DL-Lite family.

Ontology-mediated query answering over temporal and inconsistent data

“…It is sometimes convenient to represent a sequence of ABoxes as a set of assertions associated with timestamps, which we call timed assertions: [29,33] and EL ⊥ [28,35] A rigid concept is a concept which only uses rigid predicates. A rigid assertion is of the form A(a) with A ∈ N RC or R(a, b) with R ∈ N RR .…”

“…There are several directions of research to cope with this problem. While some employ non-monotonic reasoning techniques [25,26], others try to resolve the inconsistencies directly [27] or perform reasoning with respect to inconsistency-tolerant semantics (see [28] for a recent overview). We follow the latter road, since the techniques developed there are tailored to ontologymediated queries and often of lower complexity than the other approaches for resolving inconsistencies.…”

“…For the two DLs to be investigated in this paper, answering of (atemporal) conjunctive queries under these inconsistency-tolerant semantics has already been investigated for DL-Lite R in [29,30,33,34] and for EL ⊥ in [28,35]. Attention has then turned to the problem of designing algorithms and implementing these alternative semantics.…”

“…Most work has focused on the IAR semantics and dialects of DL-Lite, due to the aforementioned tractability result [30,36,37]. A notable exception is the CQAPri system, which implements all three mentioned semantics-AR, IAR and brave-for DL-Lite R knowledge bases [38,39].…”

Providing Definitive Learning Direction for Relation Classification System

“…Relation classification [4] is one of the key technologies in this procedure. It has wide applications in the field of information retrieval [5], question answering [6], and knowledge base completion [7]. Given a sentence annotated with two entities, the relation classification system is to recognize the correct relation type from a predefined relation set.…”