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International audience; The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) LOVs for IoT (LOV4IoT). To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey-based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.

The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools.In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic $\mathcal {SROIQ}4$, showing that it is both sound relative to classical $\mathcal {SROIQ}$ and that its embedding into $\mathcal {SROIQ}$ is consequence preserving. We also examine paraconsistent varieties of $\mathcal{EL}^{++}$, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models by inserting additional axioms into knowledge bases. If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.

International audience; Measuring inconsistency in knowledge bases has been recog- nized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: 1) They are mostly restricted to propositional knowledge bases; 2) Very few of them discuss computational aspects of computing inconsistency measures. In this paper, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an inconsistency measure of a first-order knowledge base, which is a sequence of inconsistency degrees. We then propose a precise algorithm to compute our inconsistency measure. We show that this algorithm reduces the computation of the inconsistency measure to classical satisfiability checking. This is done by introducing a new semantics, named S[n]-4 semantics, which can be calculated by invoking a classical SAT solver. Moreover, we show that this auxiliary semantics also gives a direct way to compute upper and lower bounds of inconsistency degrees. That is, it can be easily revised to compute approximating inconsistency measures. The approximating inconsistency measures converge to the precise values if enough resources are available. Finally, by some nice properties of the S[n]-4 semantics, we show that some upper and lower bounds can be computed in P-time, which says that the problem of computing these approximating inconsistency measures is tractable.

International audience; Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm.

A four-valued description logic has been proposed to reason with description logic based inconsistent knowledge bases. This approach has a distinct advantage that it can be implemented by invoking classical reasoners to keep the same complexity as under the classical semantics. However, this approach has so far only been studied for the basic description logic $\mathcal{ALC}$. In this paper, we further study how to extend the four-valued semantics to the more expressive description logic $\mathcal{SROIQ}$ which underlies the forthcoming revision of the Web Ontology Language, OWL 2, and also investigate how it fares when adapted to tractable description logics including $\mathcal{EL++}$, DL-Lite, and Horn-DLs. We define the four-valued semantics along the same lines as for $\mathcal{ALC}$ and show that we can retain most of the desired properties.