Reasoning on the semantic web: beyond ontology languages and reasoners

Bry, François; Marchiori, Massimo

doi:10.1049/ic.2005.0749

Cited by 5 publications

(3 citation statements)

References 15 publications

(15 reference statements)

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“…The time model in AROM-ST [17] offers several time types including instant, interval, multiInstant, and multiInterval types. The importance of time considerations in ontologies was initiated by the semantic web community [18]. A variety of approaches have been proposed to represent temporal information in RDF [19] and OWL [20].…”

Section: Prosopographical Concepts: a State Of The Artmentioning

confidence: 99%

Modeling Historical Social Networks Databases

Akoka¹,

Comyn-Wattiau²,

Lamassé³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Historical social networks are analyzed using prosopographical methods. Prosopography is a branch of historical research that focuses on the identification of social networks that appear in historical sources. It aims to represent and to interpret historical data, sourced from texts. Conceptual modeling imparts the capability to process these large data sets. This paper outlines a conceptual approach to designing a prosopographical database encompassing uncertainty. Our contribution is threefold: i) a generic certaintybased prosopographical conceptual model; ii) two meta-models with a mapping between them; iii) an illustrative example generating a customized prosopographical relational model. Unlike past approaches, our design process helps us to integrate disparate points of view as expressed in the prosopography community. We apply our approach to the prosopographical database Studium Parisiense dedicated to members of Paris schools and university between the twelfth and sixteenth centuries. This instantiation validates the usefulness of our approach.

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Section: Prosopographical Concepts: a State Of The Artmentioning

confidence: 99%

Modeling Historical Social Networks Databases

Akoka¹,

Comyn-Wattiau²,

Lamassé³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Fran et al provided a discussion on reasoning framework for the semantic languages [5]. They believe that deductive languages and reactive rules are prerequisite for a semantic reasoning.…”

Section: Ontology Implementationmentioning

confidence: 99%

An ontological engineering approach for automating inspection and quarantine at airports

Zang

Xie

et al. 2008

Journal of Computer and System Sciences

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Customs and quarantine departments are applying information systems to automate their inspection processes and improve their inspection efficiency and accuracy. The product codes from the Harmonized System (HS codes) are the essential elements of the system's integration, automation and intelligence. The identified HS codes are well-accepted and precise product references used by customs authorities, to match applicable policies to the products being inspected and taxed. Domain ontology for importing and exporting industry can be used to acquire HS codes for given products, and is a prerequisite for an integrated and intelligent automated inspection system. The authors have proposed and implemented an importing and exporting domain ontology. The ontology is composed of an integrated and comprehensive knowledge base derived from static dictionaries and the HS specification, and dynamic processing data. Based on this ontology, a reasoning engine is developed to generate HS codes intelligently for the given product names. Information systems can use the engine to get HS codes for submitted products and find applicable policies automatically. The ontology and the engine have been implemented in a Java-based platform and published as a HS Web service. In this paper, knowledge structure, reasoning mechanism and implementation details for the domain ontology and reasoning engine are presented. A test bed in the application environment has been conducted and experimental results have been obtained. The ontology and the service have the potential to be widely used by authorities and international traders of importing and exporting industry around the world.

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“…Machine reasoning has the ability to uncover implicit relationships in the data, rather than simply retrieving explicitly represented data, as is the case of querying a database. However, machine reasoning over large and complex data sets requires the use of appropriate and meaningful knowledge representations of the domain area combined with presentation of the data in machinereadable format [6]. One technique for implementing knowledge representation that has been readily adopted in biology has been the construction of bio-ontologies to establish a precisely (if not formally) defined way to model and express the knowledge of a domain in terms of defined concepts: the classes of "things", the relationships that exist between these classes, and the rules or axioms that apply to these concepts in the domain.…”

Section: Introductionmentioning

confidence: 99%

Integrating Hierarchical Controlled Vocabularies With Owl Ontology: A Case Study From the Domain of Molecular Interactions

Davis¹,

Newman²,

Khan³

et al. 2007

Proceedings of the 6th Asia-Pacific Bioinformatics Conference

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Many efforts at standardising terminologies within the biological domain have resulted in the construction of hierarchical controlled vocabularies that capture domain knowledge. Vocabularies, such as the PSI-MI vocabulary, capture both deep and extensive domain knowledge, in the OBO (Open Biomedical Ontologies) format. However hierarchical vocabularies, such as PSI-MI which are represented in OBO, only represent simple parent-child relationships between terms. By contrast, ontologies constructed using the Web Ontology Language (OWL), such as BioPax, define many richer types of relationships between terms. OWL provides a semantically rich structured language for describing classes and sub-classes of entities and properties, relationships between them and expressing domain-specific rules or axioms that can be applied to extract new information through semantic inference. In order to fully exploit the domain knowledge inherent in domain-specific controlled vocabularies, they need to be represented as OWL-DL ontologies, rather than in formats such as OBO. In this paper, we describe a method for converting OBO vocabularies into OWL and * This work has been performed as part of a collaborative research and development project with Pfizer Global R&D and is partially supported by ARC grand CEO348221. 2 class instances represented as OWL-RDF triples. This approach preserves the hierarchical arrangement of the domain knowledge whilst also making the underlying parent-child relationships available to inference engines. This approach also has clear advantages over existing methods which incorporate terms from external controlled vocabularies as literals stripped of the context associated with their place in the hierarchy. By preserving this context, we enable machine inference over the ordered domain knowledge captured in OBO controlled vocabularies.

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Reasoning on the semantic web: beyond ontology languages and reasoners

Cited by 5 publications

References 15 publications

Modeling Historical Social Networks Databases

Modeling Historical Social Networks Databases

An ontological engineering approach for automating inspection and quarantine at airports

Integrating Hierarchical Controlled Vocabularies With Owl Ontology: A Case Study From the Domain of Molecular Interactions

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