Petrova, Iliana Mineva (2017) OPTIMIZATION OF NONSTANDARD REASONING SERVICES. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: OPTIMIZATION OF NONSTANDARD REASONING SERVICES
Creators:
CreatorsEmail
Petrova, Iliana Minevailianamineva.petrova@unina.it
Date: 1 October 2017
Number of Pages: 195
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Information technology and electrical engineering
Ciclo di dottorato: 29
Coordinatore del Corso di dottorato:
nomeemail
Riccio, Danieledariccio@unina.it
Tutor:
nomeemail
Bonatti, Piero AndreaUNSPECIFIED
Date: 1 October 2017
Number of Pages: 195
Uncontrolled Keywords: non monotonic reasoning, access controll, optimizations
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Date Deposited: 22 Nov 2017 11:43
Last Modified: 08 Mar 2018 13:33
URI: http://www.fedoa.unina.it/id/eprint/11873
DOI: 10.6093/UNINA/FEDOA/11873

Abstract

The increasing adoption of semantic technologies and the corresponding increasing complexity of application requirements are motivating extensions to the standard reasoning paradigms and services supported by such technologies. This thesis focuses on two of such extensions: nonmonotonic reasoning and inference-proof access control. Expressing knowledge via general rules that admit exceptions is an approach that has been commonly adopted for centuries in areas such as law and science, and more recently in object-oriented programming and computer security. The experiences in developing complex biomedical knowledge bases reported in the literature show that a direct support to defeasible properties and exceptions would be of great help. On the other hand, there is ample evidence of the need for knowledge confidentiality measures. Ontology languages and Linked Open Data are increasingly being used to encode the private knowledge of companies and public organizations. Semantic Web techniques facilitate merging different sources of knowledge and extract implicit information, thereby putting at risk security and the privacy of individuals. But the same reasoning capabilities can be exploited to protect the confidentiality of knowledge. Both nonmonotonic inference and secure knowledge base access rely on nonstandard reasoning procedures. The design and realization of these algorithms in a scalable way (appropriate to the ever-increasing size of ontologies and knowledge bases) is carried out by means of a diversified range of optimization techniques such as appropriate module extraction and incremental reasoning. Extensive experimental evaluation shows the efficiency of the developed optimization techniques: (i) for the first time performance compatible with real-time reasoning is obtained for large nonmonotonic ontologies, while (ii) the secure ontology access control proves to be already compatible with practical use in the e-health application scenario. �

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