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Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair Semantics

arXiv.org Artificial Intelligence

In this paper, we address the problem of handling inconsistent data in Temporal Description Logic (TDL) knowledge bases. Considering the data part of the knowledge base as the source of inconsistency over time, we propose an ABox repair approach. This is the first work handling the repair in TDL Knowledge bases. To do so, our goal is twofold: 1) detect temporal inconsistencies and 2) propose a data temporal reparation. For the inconsistency detection, we propose a reduction approach from TDL to DL which allows to provide a tight NP-complete upper bound for TDL concept satisfiability and to use highly optimised DL reasoners that can bring precise explanation (the set of inconsistent data assertions). Thereafter, from the obtained explanation, we propose a method for automatically computing the best repair in the temporal setting based on the allowed rigid predicates and the time order of assertions.


The DL-Lite Family and Relations

Journal of Artificial Intelligence Research

The recently introduced series of description logics under the common moniker `DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique same assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.


The DL-Lite Family and Relations

arXiv.org Artificial Intelligence

The recently introduced series of description logics under the common moniker DL-Lite has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique same assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.


Past and Future of DL-Lite

AAAI Conferences

Temporal conceptual data models (TCMs) can be encoded Conceptual data modelling formalisms such as the Entity-in various temporal description logics (TDLs), which Relationship model (ER) and Unified Modelling Language have been designed and investigated since the seminal paper (UML) have become a de facto standard in database design (Schild 1993) with the aim of understanding the computational by providing visual means to describe application domains price of introducing a temporal dimension in DLs; in a declarative and reusable way. On the other hand, both see (Lutz, Wolter, & Zakharyaschev 2008) for a recent survey. ER and UML turned out to be closely connected with description A general conclusion one can draw from the obtained logics (DLs) developed in the area of knowledge results is that--as far as there is nontrivial interaction between representation, underpinned by formal semantics and thus the temporal and DL components--TDLs based on capable of providing services for effective reasoning over full-fledged DLs like ALC turn out to be too complex for conceptual models; see, e.g., (Berardi, Calvanese, & De Giacomo effective reasoning (see the end of this section for details).


Automated Reasoning in Temporal DL-Lite

arXiv.org Artificial Intelligence

This paper investigates the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs). We test the usage of off-the-shelf LTL reasoners to check satisfiability of TDL-Lite KBs. In particular, we test the robustness and the scalability of reasoners when dealing with TDL-Lite TBoxes paired with a temporal ABox. We conduct various experiments to analyse the performance of different reasoners by randomly generating TDL-Lite KBs and then measuring the running time and the size of the translations. Furthermore, in an effort to make the usage of TDL-Lite KBs a reality, we present a fully fledged tool with a graphical interface to design them. Our interface is based on conceptual modelling principles and it is integrated with our translation tool and a temporal reasoner.