The protégé project

Musen, Mark A.

doi:10.1145/2757001.2757003

Cited by 1,011 publications

(315 citation statements)

References 30 publications

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“…To implement our ontology and to graft it on CIDOC CRM and compatible extensions, we used Protégé, a "free, open-source ontology editor and framework for building intelligent systems" (Musen, 2015).…”

Section: Methodsmentioning

confidence: 99%

Implementation of multiple interpretation data model concepts in CIDOC CRM and compatible models

Ruymbeke

Hallot

Nys

et al. 2018

Virtual archaeol. rev.

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Highlights: Cultural Heritage modelling involves two different ontological concepts: reality and information held about it. Historical Objects existence is a sequence made by events, stability periods and changes affecting it. Multiple Interpretation Data Model mapping to CIDOC CRM and its extension proposal take into account difference between reality and information. They also manage sequence concept. Abstract:Modelling cultural heritage is a research topic shared by a broad scientific community. Although this subject has been widely studied, it seems that some aspects still have to be tackled. This paper describes two CIDOC (ICOM's International Committee for Documentation) Conceptual Reference Model (CRM) extension proposals (A & B) dedicated to structuring knowledge concerning historical objects and historical events. It focuses on multiple interpretations and sequential reality, this last being a concept which does not exist in CIDOC CRM but was originally developed in another conceptual model, the Multiple Interpretation Data Model (MIDM). To begin, an extensive description of MIDM concepts is given as well as a recall of its main peculiarities. It is followed by a mapping proposed to translate MIDM concepts into ontologies devoted to describing cultural heritage entities and activities, the CIDOC CRM and compatible models. Unfortunately, some MIDM concepts are not covered by this mapping because they do not match with existing CRM entities and properties, and this paper explains why an extension is necessary. It describes how the two versions of the extension proposal cover the missing MIDM concepts. One of these two versions, the proposal A, has been implemented as ontology in Protégé and has been tested through an instantiation phase using a real example. This instantiation phase is fully detailed. It shows that proposal A works coherently with CRM ontologies. On another hand, instantiation phase highlights improvements needs such as recording chronology in a structured way.Keywords: ontology; sequential reality; reasoning process; documentation; virtual archaeology; cultural heritage Resumen:El modelado del patrimonio cultural es un tema de investigación ampliamente compartido por la comunidad científica. A pesar de que este tema ha sido ampliamente estudiado, parece que algunos aspectos aún tienen que ser abordados. Este artículo describe dos propuestas de extensión (A y B) del Modelo de Referencia Conceptual (CRM) CIDOC (ICOM's International Committee for Documentation) dedicado a estructurar el conocimiento concerniente a objetos históricos y eventos históricos. Se centra en múltiples interpretaciones y en la realidad secuencial, siendo este último un concepto que no existe en CIDOC CRM pero que fue originalmente desarrollado en otro modelo conceptual, el Modelo de Datos de Interpretación Múltiple (MDIM). Para empezar, se da una extensa descripción de los conceptos MDIM, así como un recuerdo de sus peculiaridades principales. Continúa con un mapeo propuesto para traducir conceptos MDIM en ontolo...

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Section: Methodsmentioning

confidence: 99%

Implementation of multiple interpretation data model concepts in CIDOC CRM and compatible models

Ruymbeke

Hallot

Nys

et al. 2018

Virtual archaeol. rev.

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“…The main structure of the proposed ontology is modelled in Protégé [100] software. The class Criteria takes a central place in the ontology (Figure 2).…”

Section: Ontology Construction Processmentioning

confidence: 99%

“…Finally, expanding the extracted concepts to a set of criteria prosecutes to a taxonomy construction. The ontology was built using the Protégé (Stanford University, Stanford, USA) [100] application. The applied technology standard is OWL (Ontology Web Language).…”

Section: Ontology Construction Processmentioning

confidence: 99%

An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain

Konys

2018

Sustainability

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Sustainability assessment has received more and more attention from researchers and it offers a large number of opportunities to measure and evaluate the level of its accomplishment. However, proper selection of a particular sustainability assessment approach, reflecting problem properties and the evaluator's preferences, is a complex and important issue. Due to an existing number of different approaches dedicated to assessing, supporting, or measuring the level of sustainability and their structure oriented on the particular domain usage, problems with accurate matching frequently occur. On the other hand, the efficiency of sustainability assessment depends on the available knowledge of the ongoing capabilities. Additionally, actual research trends confirm that knowledge engineering gives a method to handle domain knowledge practically and effectively. Unfortunately, literature studies confirm that there is a lack of knowledge systematization in the sustainability assessment domain, however. The practical application of knowledge-based mechanisms may cover this gap. In this paper, we provide formal, practical and technological guidance to a knowledge management-based approach to sustainability assessment. We propose ontology as a form of knowledge conceptualization and using knowledge engineering, we make gathered knowledge publicly available and reusable, especially in terms of interoperability of collected knowledge.

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“…Utilizing owl:imports enables the import of content “on the fly” when an ontology is opened. Most ontology tools (e.g., Protege [20]) open the full contents of the source ontology in the owl:imports statement when the importing ontology is opened. Alternatively, one can use Protégé to merge the contents of two ontologies into a single ontology.…”

Section: Introductionmentioning

confidence: 99%

An empirical analysis of ontology reuse in BioPortal

Ochs

Perl

Geller

et al. 2017

Journal of Biomedical Informatics

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Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies. Content reuse enables a consistent representation of a domain and reusing content can save an ontology author significant time and effort. Prior studies have investigated the existence of reused terms among the ontologies in the NCBO BioPortal, but as of yet there has not been a study investigating how the ontologies in BioPortal utilize reused content in the modeling of their own content. In this study we investigate how 355 ontologies hosted in the NCBO BioPortal reuse content from other ontologies for the purposes of creating new ontology content. We identified 197 ontologies that reuse content. Among these ontologies, 108 utilize reused classes in the modeling of their own classes and 116 utilize reused properties in class restrictions. Current utilization of reuse and quality issues related to reuse are discussed.

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The protégé project

Cited by 1,011 publications

References 30 publications

Implementation of multiple interpretation data model concepts in CIDOC CRM and compatible models

Implementation of multiple interpretation data model concepts in CIDOC CRM and compatible models

An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain

An empirical analysis of ontology reuse in BioPortal

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