The COLIBRI Platform: Tools, Features and Working Examples

Recio-Garcí­a, Juan A.; Dí­az-Agudo, Belén; González-Calero, Pedro A.

doi:10.1007/978-3-642-38736-4_5

Cited by 9 publications

(12 citation statements)

References 51 publications

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“…• We introduce an efficient way to develop the case-base fuzzy ontology, which is the backbone of the proposed system. This ontology is built based on our previously published crisp ontology [31] and the top-level CBR crisp ontology namely CBROnto proposed by [52]. The step-by-step tutorial on the fuzzy ontology development process can be helpful for interested readers to conduct experiments.…”

Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

“…9, CASE INDEX subsumes all of the case features, CBRCASE subsumes case instances, and HAS-COMPONENT subsumes the two parts of the case. This way, we utilize OntoBridge API of JCOLIBRI2 to address ontology storage, retrieval, and manipulation in a straightforward way [52]. In ontology-based CBR, cases are represented as concept instances and their attributes are represented as ontology relations or properties.…”

Section: Crisp Ontology Customizationmentioning

confidence: 99%

“…Second, to quantify similarity for concept features, the commonalities and differences between concepts must be considered [55]. JCOLIBRI API 9 uses four semantic similarity measures: path-based such as fdeep basic and fdeep, and feature-based such as cosine, and detail [52]. These measures have been tested in the API's tutorial; however, path-based measures do not take in to account the depth of concepts from their LCA, and feature-based measures depend only on the commonalities between compared concepts.…”

Section: Similarity Calculation's Second Stagementioning

confidence: 99%

“…A CBR system was developed in Java by extending the APIs of the JCOLIBRI2 CBR framework [52]. As shown in Fig.…”

Section: System Implementationmentioning

confidence: 99%

“…Before starting the fuzzification process, our previously created crisp case-base ontology [31] is customized according to our casebase's fuzzy database contents and the CBROnto standard ontology of JCOLIBRI 2 API [52]. Fig.…”

Section: Crisp Ontology Customizationmentioning

confidence: 99%

See 4 more Smart Citations

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

Section: Crisp Ontology Customizationmentioning

confidence: 99%

Section: Similarity Calculation's Second Stagementioning

confidence: 99%

“…A CBR system was developed in Java by extending the APIs of the JCOLIBRI2 CBR framework [52]. As shown in Fig.…”

Section: System Implementationmentioning

confidence: 99%

Section: Crisp Ontology Customizationmentioning

confidence: 99%

See 3 more Smart Citations

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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Clood CBR: Towards Microservices Oriented Case-Based Reasoning

Nkisi-Orji

Wiratunga

Palihawadana

et al. 2020

Case-Based Reasoning Research and Development

Self Cite

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CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic architectures which impose size and complexity constraints. As such these applications have a barrier to adopting new technologies and remain prohibitively expensive in both time and cost because changes in frameworks or languages affect the application directly. To address this challenge, we introduce a distributed and highly scalable generic CBR system, Clood, which is based on a microservices architecture. This splits the application into a set of smaller, interconnected services that scale to meet varying demands. Microservices are cloud-native architectures and with the rapid increase in cloud-computing adoption, it is timely for the CBR community to have access to such a framework.

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An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh

Elmogy

2016

Journal of King Saud University - Computer and Information Scie

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Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semanticallyintelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of $75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases. Ó 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

show abstract

The COLIBRI Platform: Tools, Features and Working Examples

Cited by 9 publications

References 51 publications

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

Clood CBR: Towards Microservices Oriented Case-Based Reasoning

An encoding methodology for medical knowledge using SNOMED CT ontology

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