GO-FOR: A Goal-Oriented Framework for Ontology Reuse

2019 ◽  
Author(s):  
Cassio Reginato ◽  
Jordana Salamon ◽  
Gabriel Nogueira ◽  
Monalessa Barcellos ◽  
Vitor Souza ◽  
...  
Keyword(s):  
Ontology Reuse

scholarly journals Capturing the Basics of the GDPR in a Well-Founded Legal Domain Modular Ontology

2021 ◽  
Author(s):  
Mirna El Ghosh ◽  
Habib Abdulrab
Keyword(s):  
Data Protection ◽  
Legal Reasoning ◽  
Conceptual Modeling ◽  
Personal Data ◽  
Process Ontology ◽  
General Data Protection Regulation ◽  
Personal Data Protection ◽  
Modular Ontology ◽  
Legal Domain ◽  
Ontology Reuse

The primary goal of the General Data Protection Regulation (GDPR) is to regulate the rights and duties of citizens and organizations over personal data protection. Implementing the GDPR is recently gaining much importance for legal reasoning and compliance checking purposes. In this work, we aim to capture the basics of GDPR in a well-founded legal domain modular ontology named OPPD (Ontology for the Protection of Personal Data). Ontology-Driven Conceptual Modeling (ODCM), ontology layering, modularization, and reuse processes are applied. These processes aim to support the ontology engineer in overcoming the complexity of the legal knowledge and developing an ontology model faithful to reality. ODCM is used for grounding OPPD in the Unified Foundational Ontology (UFO). Ontology modularization and layering aim to simplify the ontology building process. Ontology reuse focuses on selecting and reusing Conceptual Ontology Patterns (COPs) from UFO and the legal core ontology UFO-L. OPPD intends to overcome the lack of a representation of legal procedures that most ontologies encountered. The potential use of OPPD is proposed to formalize the GDPR rules by combining ontological reasoning and Logic Programming.

The metatheory of ontology reuse

2018 ◽  
Vol 13 (3) ◽  
pp. 225-254 ◽  
Author(s):  
Megan Katsumi ◽  
Michael Grüninger
Keyword(s):  
Ontology Reuse

GUIDELINES FOR REUSING ONTOLOGIES ON THE SEMANTIC WEB

2010 ◽  
Vol 04 (02) ◽  
pp. 239-283 ◽  
Author(s):  
ELENA SIMPERL
Keyword(s):  
Semantic Web ◽  
Development Strategy ◽  
Large Scale ◽  
Point Of View ◽  
Ontology Development ◽  
The Poor ◽  
Ontology Reuse ◽  
The Impact ◽  
Do So

The ability to efficiently and effectively reuse ontologies is commonly acknowledged to play a crucial role in the large scale dissemination of ontologies and ontology-driven technology, being thus a pre-requisite for the ongoing realization of the Semantic Web. In this article, we give an account of ontology reuse from a process point of view. We present a methodology that can be utilized to systematize and monitor ontology engineering processes in scenarios reusing available ontological knowledge in the context of a particular application. Notably, and by contrast to existing approaches in this field, our aim is to provide means to overcome the poor reusability of existing resources — rather than to solve the more general issue of building new, more reusable knowledge components. To do so we investigate the impact of the application context of an ontology — in terms of tasks this ontology has been created for and will be utilized in — has on the feasibility of a reuse-oriented ontology development strategy and provide guidelines that take these aspects into account. The applicability of the methodology is demonstrated through a case study performed in collaboration with an international eRecruitment solution provider.

scholarly journals Exploring biomedical ontology mappings with graph theory methods

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2990 ◽  
Author(s):  
Simon Kocbek ◽  
Jin-Dong Kim
Keyword(s):  
Graph Theory ◽  
Biomedical Ontology ◽  
Data Consistency ◽  
Mapping Data ◽  
Time Points ◽  
Modularity Analysis ◽  
Analyse Data ◽  
Ontology Reuse ◽  
Core Ontologies ◽  
Over Time

Background In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies. Methods We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature. Results With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.

Automatic ontology creation using adaptation

2009 ◽  
Vol 24 (1) ◽  
pp. 127-141 ◽  
Author(s):  
Valerie Cross ◽  
Vishal Bathija
Keyword(s):  
Engineering Design ◽  
Language Processing ◽  
Performance Metrics ◽  
General Purpose ◽  
Information Organization ◽  
Domain Specific ◽  
Organization And Management ◽  
Coverage Accuracy ◽  
Ontology Reuse ◽  
Comparable Quality

AbstractOntologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.

scholarly journals Exploring biomedical ontology mappings with graph theory methods

2017 ◽  
Author(s):  
Simon Kocbek ◽  
Jin-Dong Kim
Keyword(s):  
Graph Theory ◽  
Biomedical Ontology ◽  
Data Consistency ◽  
Mapping Data ◽  
Time Points ◽  
Modularity Analysis ◽  
Analyse Data ◽  
Ontology Reuse ◽  
Core Ontologies ◽  
Over Time

Background In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies. Methods We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature. Results With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.

scholarly journals Deductive Module Extraction for Expressive Description Logics

2020 ◽  
Author(s):  
Patrick Koopmann ◽  
Jieying Chen
Keyword(s):  
Description Logics ◽  
Small Subset ◽  
Ontology Reuse ◽  
Uniform Interpolation

In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.

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