ONTOCOM: A reliable cost estimation method for ontology development projects

Simperl, Elena; Bürger, Tobias; Hangl, Simon; Wörgl, Stephan; Popov, Igor

doi:10.1016/j.websem.2012.07.001

Cited by 23 publications

(10 citation statements)

References 22 publications

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“…All materials and components are analysed and formulated into Cost Estimating Relationship (CER). The CERs are mathematical representation of the relationship between cost drivers and total cost [13,14,22,23]. The complete development of a parametric model undergoes scope determination, data collection, data normalization, data analysis, data application, testing and documentation [24].…”

Section: Parametric Modelmentioning

confidence: 99%

Cost estimation methods for internet infrastructure deployment in Rural Sarawak: a review

Hao

Lai

et al. 2020

IJEECS

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<p>In rural Sarawak, the internet accessibility is low due to unreliable power grids to support telecommunication network and large geographical area. The risk for network infrastructure implementation is high for Internet Service Provider (ISP), thus more practical and accurate cost estimation methods should be used [1-3]. This paper reviews different types of cost estimation methods and the accuracy and feasibility of each methods are discussed and compared for network infrastructure implementation in rural Sarawak. The unique characteristics of rural Sarawak are considered in this work, including the topography, development of rural areas and acceptance of new technologies. Different cost estimation methods are identified for different senarios and availability of data.</p>

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Section: Parametric Modelmentioning

confidence: 99%

Cost estimation methods for internet infrastructure deployment in Rural Sarawak: a review

Hao

Lai

et al. 2020

IJEECS

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“…OKMSs, thus, are computer‐based systems highly relevant for business organizations (Del Giudice & Della‐Peruta, 2016; Di Vaio et al, 2020). Nevertheless, similar to generic KMS, despite the utilization of OKMS was proposed about three decades ago (Fensel, 2002; Maedche et al, 2003; O'Leary, 1998a, 1998b), the development of OKMSs has been scarce and few systematically guided (Chalmeta & Grangel, 2008; Corcho et al, 2003; Dehghani & Ramsin, 2015; Feilmayr & Wöß, 2016; Fernandez‐López & Gomez‐Pérez, 2002; Iqbal et al, 2013; Simperl et al, 2012; Simperl & Tempich, 2006). Fernandez‐López & Gomez‐Pérez, 2002; p. 153) concluded that ‘none of the methodologies are fully mature if we compare them with the IEEE standard’.…”

Section: Introductionmentioning

confidence: 99%

“…Chalmeta and Grangel (2008; p. 743) stated that ‘one of the chief reasons for the large number of failures in implementing a KMS is the lack of an ISDM (Information Systems Development Methodology) that is specifically oriented towards the development of a KMS’. Simperl et al (2012, p. 15) concluded that ‘the adoption of ontology‐based technologies in commercial settings depends on the availability of appropriate methodologies and software assisting the ontology engineering process, and on methods for an effective cost/benefit management’. Iqbal et al (2013) identified insufficient maturity level and poor documentation guidelines in the development methodologies for applications with ontology components.…”

Section: Introductionmentioning

confidence: 99%

“…This research, thus, pursues to advance similar research efforts on the convergence and need of counting with comprehensive and systematic development methodologies (Chalmeta & Grangel, 2008; Corcho et al, 2003; Dehghani & Ramsin, 2015; Feilmayr & Wöß, 2016; Fernandez‐López & Gomez‐Pérez, 2002; Iqbal et al, 2013; Simperl & Tempich, 2006) in order to OKMS can be developed and implemented usually in business organizations like other types of computer‐based systems nowadays (Feilmayr & Wöß, 2016). A comprehensive and systematic system development process, in this research, is defined as a process that includes: a life cycle process; project management and technical systems engineering processes; workflows with roles, activities, and artefacts; and it is used in a standard and consistent way (Dehghani & Ramsin, 2015; Feilmayr & Wöß, 2016; Iqbal et al, 2013; Simperl et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Development methodologies for ontology‐based knowledge management systems: A review

et al. 2021

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Knowledge management systems (KMS) are computer‐based systems highly valued in business organizations because they support knowledge management (KM) processes. Most KMS have been developed using non‐intelligent computer technology—that is, DMS, CMS, DBMS, and CIS—, and thus, they cannot provide advanced capabilities. Consequently, enhanced KMS using intelligent technologies of ontologies with inference engines—called ontology‐based knowledge management systems (OKMS)—have been proposed in the last three decades. Nowadays, however, the implementation of OKMS in real‐world settings is still scarce. Lack of comprehensive and systematic development methodologies including Project Management and Technical Systems Engineering processes—as the Systems and Software Systems Engineering standards propose—have been suggested as a factor that inhibits OKMS implementations. In this study, we review the OKMS literature (1990–2021 period)—from six seminal studies located using a research search engine—to assess OKMS development methodologies that can be considered comprehensive and systematic. Five methodologies were identified and assessed using an evaluation subset from the ISO/IEC/IEEE 15288:2015 Systems and Software Engineering standard. Two of them—CommonKADS and NeON—were found with a high comprehensive and systematic level and both are suggested for organizations interested in OKMS implementations, but none of them qualified as agile, which is a current development approach for systems and software systems. Hence, further empirical research toward the realization of comprehensive and systematic OKMSs development methodologies, including agile versions, is suggested for fostering the implementation of OKMS in real‐world settings.

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“…In general, ontologies are created manually and require availability of domain experts and ontology engineers familiar with the theory and practice of ontology construction. Once the ontology has been constructed, evolving knowledge and application requirements demand continuous maintenance efforts [ 30 ]. In addition, the dramatic increase in the volume of data over the last decade has made it virtually impossible to transform all existing data manually into knowledge under reasonable time constraints [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Linked open data-based framework for automatic biomedical ontology generation

2018

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BackgroundFulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.ResultsOur evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called “OntoGain” which shows that LOD-ABOG performs 14.76% better in terms of relation extraction.ConclusionThis paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.

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ONTOCOM: A reliable cost estimation method for ontology development projects

Cited by 23 publications

References 22 publications

Cost estimation methods for internet infrastructure deployment in Rural Sarawak: a review

Cost estimation methods for internet infrastructure deployment in Rural Sarawak: a review

Development methodologies for ontology‐based knowledge management systems: A review

Linked open data-based framework for automatic biomedical ontology generation

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