We address the problem of handling inconsistency in uncertain knowledge bases that are specified in the lightweight fragments of description logics DL-Lite. More specifically, we assume that the TBox component is coherent, stable, and fully reliable. However, the ABox component may be inconsistent with respect to the TBox, partially preordered and uncertain. Uncertainty is encoded in the framework of possibility theory. In this context, we propose an extension of standard possibilistic DL-Lite. We represent the ABox as a symbolic weighted base, where the weights attached to the assertions are ordered according to a strict partial order. We define a tractable method for computing a single possibilistic repair for a partially preordered weighted ABox. The idea is to consider the possibilistic compatible bases of such an ABox, which intuitively encode all the possible extensions of a partial order, and compute the possibilistic repair of each compatible base. We then compute the intersection of all these possibilistic repairs to obtain a single repair for the initial ABox. We also provide an equivalent characterization by introducing the notion of$\pi$-accepted assertions. This ensures that the computation of the partially preordered possibilistic repair can be achieved in polynomial time in DL-Lite.

Handling inconsistency in formal ontologies is crucial for facilitating meaningful query answering. Arguably the most popular approach for resolving inconsistency amounts to repairing the dataset in terms of the semantic knowledge encoded in the ontology. This has given rise to many inconsistency- tolerant semantics, like the well-known IAR (Intersection of ABox Repair) semantics, which produces a single consistent subset of the dataset, and that can be queried. Several frameworks additionally consider a preference relation over the data pieces (called assertions), such as the Elect method, which generalizes the IAR semantics to capture a partial preorder. Elect also computes a single consistent subset of the dataset using the notion of elected assertions. Basically, an assertion is elected if it is strictly preferred to all the assertions that conflict with it. However, Elect requires the prior computation of all the conflicts between the assertions. In this paper, we propose a new algorithm for computing the set of elected assertions, without exhibiting all the conflicts. Our algorithm is based on a new characterization of the set of elected assertions using the IAR semantics.

Abstract We focus on the problem of handling inconsistency in lightweight ontologies. We assume that the terminological knowledge base (TBox) is specified in DL-Lite and that the set of assertional facts (ABox) is partially preordered and may be inconsistent with respect to the TBox. One of the main contributions of this paper is the provision of an efficient and safe method, called Elect, to restore the consistency of the ABox with respect to the TBox. In the case where the assertional base is flat (i.e. no priorities are associated with the ABox) or totally preordered, we show that our method collapses with the well-known intersection ABox repair semantics and the non-defeated semantics, respectively. The semantic justification of the Elect method is obtained by first viewing a partially preordered ABox as a family of totally preordered ABoxes and then applying non-defeated inference to each of the totally preordered ABoxes. We introduce the notion of elected assertions which allows us to provide an equivalent characterization of the Elect method without explicitly generating all the totally preordered ABoxes. We show that computing the set of elected assertions is done in polynomial time with respect to the size of the ABox. The second part of the paper discusses how to go beyond the Elect method. In particular, we discuss to what extent the Elect method can be generalized to description logics that are more expressive than DL-Lite.

International audience

The aim of this paper is to highlight two important issues related to the annotation and querying of Intangible Cultural Heritage video datasets. First, we focus on ontology completion by annotating dance videos. In order to build video training sets and to enrich the proposed ontology, manual video annotation is performed based on background knowledge formalized in an ontology, representing a semantics of a traditional dance. The paper provides a case study on Malaysian Zapin dances. Second, we address the question of how can end-users efficiently query the datasets of annotated videos that are built.