A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a "theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose "axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a \theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose \axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning

Peer reviewedPreprin

Provenance-based validation of E-science experiments

E-Science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards. Scientists may also want to review and verify experiments performed by their colleagues. There are no existing frameworks for validating such experiments in today's e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented provenance of experiment results and semantic descriptions of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis

Link Before You Share: Managing Privacy Policies through Blockchain

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE International Conference on Big Data (IEEE BigData 2017) December 14, 2017, Boston, MA, US

OWL-POLAR : semantic policies for agent reasoning

The original publication is available at www.springerlink.comPostprin

Supporting public decision making in policy deliberations: An ontological approach

This is the post-print version of the Paper. The official published version can be accessed from the link below - Copyright @ 2011 SpringerSupporting public decision making in policy deliberations has been a key objective of eParticipation which is an emerging area of eGovernment. EParticipation aims to enhance citizen involvement in public governance activities through the use of information and communication technologies. An innovative approach towards this objective is exploiting the potentials of semantic web technologies centred on conceptual knowledge models in the form of ontologies. Ontologies are generally defined as explicit human and computer shared views on the world of particular domains. In this paper, the potentials and benefits of using ontologies for policy deliberation processes are discussed. Previous work is then extended and synthesised to develop a deliberation ontology. The ontology aims to define the necessary semantics in order to structure and interrelate the stages and various activities of deliberation processes with legal information, participant stakeholders and their associated arguments. The practical implications of the proposed framework are illustrated.This work is funded by the European Commission under the 2006/1 eParticipation call

Ontology population for open-source intelligence: A GATE-based solution

Open-Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results