Incorporating ontology-based semantics into conceptual modelling

La-Ongsri, Somluck; Roddick, John F.

doi:10.1016/j.is.2015.02.003

Cited by 17 publications

(4 citation statements)

References 18 publications

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“…Design science and action research papers are scarce (Table B4). March 2006, 2012;Azevedo et al 2015;Barbier et al 2003;Bera et al 2014;Bera et al 2010;Bowen et al 2006Bowen et al , 2009Briand et al 2004;Burton-Jones et al 2009;Chen and Carlis 2003;Clarke et al 2016;Dobing and Parsons 2008;Dussart et al 2004;Evermann and Fang 2010;Evermann and Wand 2009;Figl et al 2013;Green and Rosemann 2004;Harzallah et al 2012;Irwin and Turk 2005;La-Ongsri and Roddick 2015;Laurier and Poels 2012;Liu et al 2004;Milicev 2007;Milton and Kazmierczak 2004;Milton et al 2012;Opdahl and Henderson-Sellers 2004;Parsons 2011;Recker 2010;Recker et al 2010;Recker et al 2011;Recker et al 2009 Theoretical 27…”

Section: About the Authorsmentioning

confidence: 99%

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in a Digital World

Recker¹,

Lukyanenko²,

Jabbari³

et al. 2020

MISQ

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The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with new theory. We develop a new theoretical framework of conceptual modeling that delivers a fundamental shift in the assumptions that govern research in this area. This move can make traditional knowledge about conceptual modeling consistent with the emerging requirements of a digital world. Our framework draws attention to the role of conceptual modeling scripts as mediators between physical and digital realities. We identify new research questions about grammars, methods, scripts, agents, and contexts that are situated in intertwined physical and digital realities. We discuss several implications for conceptual modeling scholarship that relate to the necessity of developing new methods and grammars for conceptual modeling, broadening the methodological array of conceptual modeling scholarship, and considering new dependent variables.

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Section: About the Authorsmentioning

confidence: 99%

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in a Digital World

Recker¹,

Lukyanenko²,

Jabbari³

et al. 2020

MISQ

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“…After that, diagnostic service stores generated model into a graph database [17][18] and queries it in the diagnostic parameters processing loop.…”

Section: Fig2 Diagnostic Proceduresmentioning

confidence: 99%

Log mining and knowledge-based models in data storage systems diagnostics

Uspenskij

2019

E3S Web Conf.

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Modern data storage systems have a sophisticated hardware and software architecture, including multiple storage processors, storage fabrics, network equipment and storage media and contain information, which can be damaged or lost because of hardware or software fault. Approach to storage software diagnostics, presented in current paper, combines a log mining algorithms for fault detection based on natural language processing text classification methods, and usage of the diagnostic model for a task of fault source detection. Currently existing approaches to computational systems diagnostics are either ignoring system or event log data, using only numeric monitoring parameters, or target only certain log types or use logs to create chains of the structured events. The main advantage of using natural language processing method for log text classification is that no information of log message structure or log message source, or log purpose is required if there is enough data for classificator model training. Developed diagnostic procedure has accuracy score comparable with existing methods and can target all presented in training set faults without prior log structure research.

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“…Li et al [8] designed ontology model describing the space related information of humans, events and indoor space objects as well as their relations to meet the indoor semantic query, and designed a new method based on ontology model which could support complex indoor space semantic query. La-Ongsri and Roddick [9] investigated the incorporation of ontologies into three popular conceptual modeling methodologies, presenting the Ontological Entity-Relationship (OntoER) model, Ontological Object Role Modelling (OntoORM) and the Ontological Unified Modelling Language (OntoUML) class diagram to improve the situation that existing conceptual models were not rich enough to incorporate ontologies in one single conceptual schema. Ji et al [10] used ontology-based semantic similarity to facilitate the article screening process for systematic reviews, which could be easily applied to any SR topics in the biomedical domain with generalizability.…”

Section: Related Researchmentioning

confidence: 99%

Manufacturing Technology Knowledge Retrieval System Based on OSE and UCF

et al. 2019

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Nowadays it is commonly accepted that knowledge, as a resource has become increasingly important in the drive toward knowledge-driven innovation in the manufacturing industry, and which is especially critical for the research and development of complex equipment. In China, there are several fields of manufacturing, such as aviation, ship, and electronics. Although each of them has its own professional technology, they also take advantage of the basic common manufacturing technology. Thus, it is meant for sharing manufacturing technology knowledge. In order to promote innovation, a manufacturing technology knowledge retrieval system is developed to make more efficient use of manufacturing technology knowledge. Manufacturing technology knowledge is a phrase with vast meanings, which may include knowledge of different manufacturing process means, machine, and process capabilities or the latest technology developments. In this system, the manufacturing technology knowledge is defined and organized. A knowledge retrieval method based on the weighted fusion of ontology-based semantic extension (OSE) and user-based collaborative filtering (UCF) is proposed and described detailed. On this basis, the prototype system of manufacturing technology knowledge retrieval system is developed. Its architecture and main functions are elaborated. The results of this system facilitate the sharing of manufacturing knowledge, and it is expected to accelerate the process of knowledge transfer, to improve the level of the overall manufacturing technology. INDEX TERMS Manufacturing technology knowledge, ontology-based semantic extension, user-based collaborative filtering, knowledge sharing.

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Incorporating ontology-based semantics into conceptual modelling

Cited by 17 publications

References 18 publications

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in a Digital World

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in a Digital World

Log mining and knowledge-based models in data storage systems diagnostics

Manufacturing Technology Knowledge Retrieval System Based on OSE and UCF

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