WebProtégé: A collaborative ontology editor and knowledge acquisition tool for the Web

Tudorache, Tania; Nyulas, Csongor; Noy, Natalya F.; Musen, Mark A.

doi:10.3233/sw-2012-0057

Cited by 174 publications

(91 citation statements)

References 15 publications

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“…Protégé, and its extensions for collaborative development, such as WebProtégé and iCAT [10] (see Figure 1 for a screenshot of the iCAT ontology-editor interface) are prominent standalone tools that are used by a large community worldwide to develop ontologies in a variety of different projects. Both Web-Protégé and Collaborative Protégé provide a robust and scalable environment for collaboration and are used in several largescale projects, including the development of ICD-11 [11].…”

Section: Collaborative Ontology Engineeringmentioning

confidence: 99%

“…A high entropy can be observed for BRO (0.81), which indicates that it is a demographically edited ontology -even though there are only five users. 10 Interpretation & practical implications: The transition probabilities for a first-order Markov chain unveil the roles of certain users and can help to identify users or even groups of users who frequently change the same classes. Users that frequently change classes after other users (i.e., exhibit high transition probabilities in their columns) were identified by us as actual gardeners, curators and administrators of the corresponding projects.…”

Section: Path and Model Descriptionmentioning

confidence: 99%

“…If certain users always change the same classes after specific other users, it could be worthwhile for project administrators to investigate if these users are actually collaborating, for example by looking at the changed properties and property 9 Additionally, we calculated the Gini Coefficient for each distribution confirming the results presented here. 10 Note that we do not necessarily know whether the differences between these distributions are statistically significant as we are mainly interested in the behavior of single distributions. values, or if a single user is always cleaning up after the other user.…”

Section: Path and Model Descriptionmentioning

confidence: 99%

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Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Walk

Singer

Strohmaier

et al. 2014

Journal of Biomedical Informatics

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Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50, 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.

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Section: Collaborative Ontology Engineeringmentioning

confidence: 99%

Section: Path and Model Descriptionmentioning

confidence: 99%

Section: Path and Model Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Walk

Singer

Strohmaier

et al. 2014

Journal of Biomedical Informatics

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“…Functionalities, scenarios and security knowledge items (attacks, weakness, and practices) were collected under the defined context. We then implemented all the designed classes and corresponding relationships using OWL (https://www.w3.org/OWL/) (Web Ontology Language), a markup language based on RDF/XML (https://www.w3.org/RDF/) (Resource Description Framework/Extensible Markup Language), and we used the Protégé OWL tool to create and maintain the ontology because of its simplicity and popularity [20]. Figure 7 depicts the ontology design in Protégé editor whereas Figure 8 presents the maintenance of object properties and data properties for contextualized knowledge (Security Attack).…”

Section: Evaluation Of the Ontologymentioning

confidence: 99%

Managing Software Security Knowledge in Context: An Ontology Based Approach

Wen

Katt

2019

Information

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Knowledge of software security is highly complex since it is quite context-specific and can be applied in diverse ways. To secure software development, software developers require not only knowledge about general security concepts but also about the context for which the software is being developed. With traditional security-centric knowledge formats, it is difficult for developers or knowledge users to retrieve their required security information based on the requirements of software products and development technologies. In order to effectively regulate the operation of security knowledge and be an essential part of practical software development practices, we argue that security knowledge must first incorporate features that specify what contextual characteristics are to be handled, and represent the security knowledge in a format that is understandable and acceptable to the individuals. This study introduces a novel ontology approach for modeling security knowledge with a context-based approach, by which security knowledge can be retrieved, taking the context of the software application at hand into consideration. In this paper, we present our security ontology with the design concepts and the corresponding evaluation process.Information 2019, 10, 216 2 of 12 security to methodically classify various security concepts, such as security attacks and vulnerabilities as well as related security prevention mechanisms [5,6]. The knowledge representation of ontology not only integrates knowledge resources at both abstract and semantic levels, but can also be adopted by knowledge sharing services such as advanced knowledge search, knowledge visualization, and therefore, supports the learning process of software security.This paper is part of ongoing research developing a contextual learning environment for software security, in which ontology is used as the kernel knowledge repository in managing contextualized security knowledge. The objective of this research is to support software developers and knowledge users to define and use security knowledge appropriately, adapting to their working context. The ontology we designed integrates application context, security domain knowledge, and contextualized knowledge, allowing contextual inquiry through software scenarios that users would be interested in or familiar with. In this paper, we present our security ontology with the design concepts and the evaluation process.The rest of this paper is organized as follows. Section 2 introduces the background knowledge about the context and knowledge. In Section 3, we describe the design of our ontology. Section 4 presents the evaluation process of the ontology, followed by a discussion in Section 5. We discuss related works in Section 6. Lastly, Section 7 presents the conclusion and our future works. Context and Knowledge Management

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“…The terminological sources include freely available materials, such as classical textbooks, Wikipedia articles, and real scholarly papers, along with personal experience of the experts. During the development, we used a collaborative tool WebProtege 9 [31].…”

Section: Modeling Principlesmentioning

confidence: 99%

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Nevzorova

Zhiltsov

Kirillovich

et al. 2014

Knowledge Engineering and the Semantic Web

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Abstract. In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.

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WebProtégé: A collaborative ontology editor and knowledge acquisition tool for the Web

Cited by 174 publications

References 15 publications

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Managing Software Security Knowledge in Context: An Ontology Based Approach

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

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