ExpO: Towards Explaining Ontology-Driven Conceptual Models

Elena Romanenko; Diego Calvanese; Giancarlo Guizzardi

doi:10.1007/978-3-031-59468-7_3

ExpO: Towards Explaining Ontology-Driven Conceptual Models

Elena Romanenko^*
, Diego Calvanese
, Giancarlo Guizzardi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

Ontology-driven conceptual models play an explanatory role in complex and critical domains. However, since those models may consist of a large number of elements, including concepts, relations and sub-diagrams, their reuse or adaptation requires significant efforts. While conceptual model engineers tend to be biased against the removal of information from the models, general users struggle to fully understand them. The paper describes ExpO—a prototype that addresses this trade-off by providing three components: (1) an API that implements model transformations, (2) a software plugin aimed at modelers working with the language OntoUML, and (3) a web application for model exploration mostly designed for domain experts. We describe characteristics of every component and specify scenarios of possible usages.

Original language	English
Title of host publication	Research Challenges in Information Science
Subtitle of host publication	18th International Conference, RCIS 2024, Proceedings
Editors	João Araújo, Jose Luis de la Vara, Maribel Yasmina Santos, Saïd Assar
Place of Publication	Cham
Publisher	Springer
Pages	20-28
Number of pages	9
Edition	1
ISBN (Electronic)	978-3-031-59468-7
ISBN (Print)	978-3-031-59467-0
DOIs	https://doi.org/10.1007/978-3-031-59468-7_3
Publication status	Published - 4 May 2024
Event	18th International Conference on Research Challenges in Information Science, RCIS 2024 - Centro Cultural Vila Flor (CCVF) , Guimarães, Portugal Duration: 14 May 2024 → 17 May 2024 Conference number: 18 https://www.rcis-conf.com/rcis2024/

Publication series

Name	Lecture Notes in Business Information Processing
Publisher	Springer
Volume	514 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	18th International Conference on Research Challenges in Information Science, RCIS 2024
Abbreviated title	RCIS 2024
Country/Territory	Portugal
City	Guimarães
Period	14/05/24 → 17/05/24
Internet address	https://www.rcis-conf.com/rcis2024/

Keywords

NLA
OntoUML
Pragmatic explanation
Software Tools
Ontology-Driven Conceptual Models

Access to Document

10.1007/978-3-031-59468-7_3

Cite this

Romanenko, E., Calvanese, D., & Guizzardi, G. (2024). ExpO: Towards Explaining Ontology-Driven Conceptual Models. In J. Araújo, J. L. de la Vara, M. Y. Santos, & S. Assar (Eds.), Research Challenges in Information Science: 18th International Conference, RCIS 2024, Proceedings (1 ed., pp. 20-28). (Lecture Notes in Business Information Processing; Vol. 514 LNBIP). Springer. https://doi.org/10.1007/978-3-031-59468-7_3

Romanenko, Elena ; Calvanese, Diego ; Guizzardi, Giancarlo. / ExpO : Towards Explaining Ontology-Driven Conceptual Models. Research Challenges in Information Science: 18th International Conference, RCIS 2024, Proceedings. editor / João Araújo ; Jose Luis de la Vara ; Maribel Yasmina Santos ; Saïd Assar. 1. ed. Cham : Springer, 2024. pp. 20-28 (Lecture Notes in Business Information Processing).

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abstract = "Ontology-driven conceptual models play an explanatory role in complex and critical domains. However, since those models may consist of a large number of elements, including concepts, relations and sub-diagrams, their reuse or adaptation requires significant efforts. While conceptual model engineers tend to be biased against the removal of information from the models, general users struggle to fully understand them. The paper describes ExpO—a prototype that addresses this trade-off by providing three components: (1) an API that implements model transformations, (2) a software plugin aimed at modelers working with the language OntoUML, and (3) a web application for model exploration mostly designed for domain experts. We describe characteristics of every component and specify scenarios of possible usages.",

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author = "Elena Romanenko and Diego Calvanese and Giancarlo Guizzardi",

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doi = "10.1007/978-3-031-59468-7\_3",

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Romanenko, E, Calvanese, D & Guizzardi, G 2024, ExpO: Towards Explaining Ontology-Driven Conceptual Models. in J Araújo, JL de la Vara, MY Santos & S Assar (eds), Research Challenges in Information Science: 18th International Conference, RCIS 2024, Proceedings. 1 edn, Lecture Notes in Business Information Processing, vol. 514 LNBIP, Springer, Cham, pp. 20-28, 18th International Conference on Research Challenges in Information Science, RCIS 2024, Guimarães, Portugal, 14/05/24. https://doi.org/10.1007/978-3-031-59468-7_3

ExpO: Towards Explaining Ontology-Driven Conceptual Models. / Romanenko, Elena; Calvanese, Diego; Guizzardi, Giancarlo.
Research Challenges in Information Science: 18th International Conference, RCIS 2024, Proceedings. ed. / João Araújo; Jose Luis de la Vara; Maribel Yasmina Santos; Saïd Assar. 1. ed. Cham: Springer, 2024. p. 20-28 (Lecture Notes in Business Information Processing; Vol. 514 LNBIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - ExpO

T2 - 18th International Conference on Research Challenges in Information Science, RCIS 2024

AU - Romanenko, Elena

AU - Calvanese, Diego

AU - Guizzardi, Giancarlo

N1 - Conference code: 18

PY - 2024/5/4

Y1 - 2024/5/4

N2 - Ontology-driven conceptual models play an explanatory role in complex and critical domains. However, since those models may consist of a large number of elements, including concepts, relations and sub-diagrams, their reuse or adaptation requires significant efforts. While conceptual model engineers tend to be biased against the removal of information from the models, general users struggle to fully understand them. The paper describes ExpO—a prototype that addresses this trade-off by providing three components: (1) an API that implements model transformations, (2) a software plugin aimed at modelers working with the language OntoUML, and (3) a web application for model exploration mostly designed for domain experts. We describe characteristics of every component and specify scenarios of possible usages.

AB - Ontology-driven conceptual models play an explanatory role in complex and critical domains. However, since those models may consist of a large number of elements, including concepts, relations and sub-diagrams, their reuse or adaptation requires significant efforts. While conceptual model engineers tend to be biased against the removal of information from the models, general users struggle to fully understand them. The paper describes ExpO—a prototype that addresses this trade-off by providing three components: (1) an API that implements model transformations, (2) a software plugin aimed at modelers working with the language OntoUML, and (3) a web application for model exploration mostly designed for domain experts. We describe characteristics of every component and specify scenarios of possible usages.

KW - NLA

KW - OntoUML

KW - Pragmatic explanation

KW - Software Tools

KW - Ontology-Driven Conceptual Models

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DO - 10.1007/978-3-031-59468-7_3

M3 - Conference contribution

AN - SCOPUS:85193607972

SN - 978-3-031-59467-0

T3 - Lecture Notes in Business Information Processing

SP - 20

EP - 28

BT - Research Challenges in Information Science

A2 - Araújo, João

A2 - de la Vara, Jose Luis

A2 - Santos, Maribel Yasmina

A2 - Assar, Saïd

PB - Springer

CY - Cham

Y2 - 14 May 2024 through 17 May 2024

ER -

Romanenko E, Calvanese D, Guizzardi G. ExpO: Towards Explaining Ontology-Driven Conceptual Models. In Araújo J, de la Vara JL, Santos MY, Assar S, editors, Research Challenges in Information Science: 18th International Conference, RCIS 2024, Proceedings. 1 ed. Cham: Springer. 2024. p. 20-28. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-59468-7_3

ExpO: Towards Explaining Ontology-Driven Conceptual Models

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Keywords

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