In this paper we study the diagnosis and repair of incoherent terminologies. We define a number of new nonstandard reasoning services to explain incoherence through pinpointing, and we present algorithms for all of these services. For one of the core tasks of debugging, the calculation of minimal unsatisfiability preserving subterminologies, we developed two different algorithms, one implementing a bottom-up approach using support of an external description logic reasoner, the other implementing a specialized tableau-based calculus. Both algorithms have been prototypically implemented. We study the effectiveness of our algorithms in two ways: we present a realistic case study where we diagnose a terminology used in a practical application, and we perform controlled benchmark experiments to get a better understanding of the computational properties of our algorithms in particular and the debugging problem in general.

Abstract.One of the major problems of large scale, distributed and evolving ontologies is the potential introduction of inconsistencies. In this paper we survey four different approaches to handling inconsistency in DL-based ontologies: consistent ontology evolution, repairing inconsistencies, reasoning in the presence of inconsistencies and multi-version reasoning. We present a common formal basis for all of them, and use this common basis to compare these approaches. We discuss the different requirements for each of these methods, the conditions under which each of them is applicable, the knowledge requirements of the various methods, and the different usage scenarios to which they would apply.

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Abstract. In this paper we propose a framework for reasoning with multi-version ontology, in which a temporal logic is developed to serve as its semantic foundation. We show that the temporal logic approach can provide a solid semantic foundation which can support various requirements on multi-version ontology reasoning. We have implemented the prototype of MORE (Multi-version Ontology REasoner), which is based on the proposed framework. We have tested MORE with several realistic ontologies. In this paper, we also discuss the implementation issues and report the experiments with MORE.

Objectives: Use of corticosteroids is common in the treatment of coronavirus disease 2019, but clinical effectiveness is controversial. We aimed to investigate the association of corticosteroids therapy with clinical outcomes of hospitalized COVID-19 patients. Methods: In this single-centre, retrospective cohort study, adult patients with confirmed coronavirus disease 2019 and dead or discharged between 29 December 2019 and 15 February 2020 were studied; 1:1 propensity score matchings were performed between patients with or without corticosteroid treatment. A multivariable COX proportional hazards model was used to estimate the association between corticosteroid treatment and in-hospital mortality by taking corticosteroids as a time-varying covariate. Results: Among 646 patients, the in-hospital death rate was higher in 158 patients with corticosteroid administration (72/158, 45.6% vs. 56/488, 11.5%, p < 0.0001). After propensity score matching analysis, no significant differences were observed in in-hospital death between patients with and without corticosteroid treatment (47/124, 37.9% vs. 47/124, 37.9%, p 1.000). When patients received corticosteroids before they required nasal high-flow oxygen therapy or mechanical ventilation, the in-hospital death rate was lower than that in patients who were not administered corticosteroids (17/86, 19.8% vs. 26/86, 30.2%, log rank p 0.0102), whereas the time from admission to clinical improvement was longer (13 (IQR 10e17) days vs. 10 (IQR 8e13) days; p < 0.001). Using the Cox proportional hazards regression model accounting for time varying exposures in matched pairs, corticosteroid therapy was not associated with mortality difference (HR 0.98, 95% CI 0.93e1.03, p 0.4694). Discussion: Corticosteroids use in COVID-19 patients may not be associated with in-hospital mortality.

Prolog is an excellent tool for representing and manipulating data written in formal languages as well as natural language. Its safe semantics and automatic memory management make it a prime candidate for programming robust Web services.Where Prolog is commonly seen as a component in a Web application that is either embedded or communicates using a proprietary protocol, we propose an architecture where Prolog communicates to other components in a Web application using the standard HTTP protocol. By avoiding embedding in external Web servers development and deployment become much easier. To support this architecture, in addition to the transfer protocol, we must also support parsing, representing and generating the key Web document types such as HTML, XML and RDF. This paper motivates the design decisions in the libraries and extensions to Prolog for handling Web documents and protocols. The design has been guided by the requirement to handle large documents efficiently. The described libraries support a wide range of Web applications ranging from HTML and XML documents to Semantic Web RDF processing.The benefits of using Prolog for Web related tasks is illustrated using three case studies.

Abstract. Revision of a description logic-based ontology deals with the problem of incorporating newly received information consistently. In this paper, we propose a general operator for revising terminologies in description logic-based ontologies. Our revision operator relies on a reformulation of the kernel contraction operator in belief revision. We first define our revision operator for terminologies and show that it satisfies some desirable logical properties. Second, two algorithms are developed to instantiate the revision operator. Since in general, these two algorithms are computationally too hard, we propose a third algorithm as a more efficient alternative. We implemented the algorithms and provide evaluation results on their efficiency, effectiveness and meaningfulness in the context of two application scenarios: Incremental ontology learning and mapping revision.