Lessons Learned — The Case of CROCUS: Cluster-Based Ontology Data Cleansing

Cherix, Didier; Usbeck, Ricardo; Both, Andreas; Lehmann, Jens

doi:10.1007/978-3-319-11955-7_2

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…Patient information collection, investigation data, and treatment record has been managed and analyzed by a health informatics system called health ontology (14) . In software engineering, data visualization ontology (15) is implemented in a procedural approach; pattern matching ontology is used for information searching (16) and data cleansing ontology (17) to remove irrelevant data. Our advanced works modeled graphical ontology for requirement specification (18) ; descriptive logical ontology for integrated quality factors (18) ; relational ontology to integrate green computational criteria (19) and ontological design architecture for the internet of things based application (20) .…”

Section: Ontologymentioning

confidence: 99%

Explicit specification framework to manage software project effectively

Sarker¹

2020

IJST

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Objectives: This research addresses the causal relationship among processes, tasks, resources, and stakeholders of the software projects to improve the success rate that is 31% only now (CHAOS report 2019). Methods: A software project management framework is developed based on the findings of an empirical study on recent commonly used methodologies and root cause analysis on the reasons for project failure. The causeeffect relationship is prioritized by Structural Equation Modeling (SEM) to develop the Explicit Specification Framework (ESF). The framework is tied to the pinpointing with ontology for formal presentation, descriptive logic for unambiguous information specification, and ikigai for decision-making.Findings: The proposed framework is evaluated by a group of experts and their average scope is evaluated by a group comparison mechanism called Grey Rational Analysis (GRA). The evaluation process considered 6 parameters and compared it with 4 commonly used project management approaches. The highest GRA rank is found for the average of six distinct parameters though it is comparatively more complex for explicit documentation. The three-layer architecture of ESF remarks on roles, functions, and respective stakeholders in each layer presented by a comprehensive comparison study with literature and found that it enhances formal practice, sustainability, and management capability. ESF is easy to implement for distributed project management and improving automation facilities in the software industries.Novelty/Application: Artificial intelligence, the Internet of things, and big data would be common elements of a software project and ESF would meet the demand for project information specification, management, and control. Keywords: Software Project Management; Software Quality Control; methodology; multi- criteria decision making; structural educational modeling; ontology

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Section: Ontologymentioning

confidence: 99%

Explicit specification framework to manage software project effectively

Sarker¹

2020

IJST

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“…This means that the actual work is executed by a high number of contributors in a decentralized fashion. 8 This not only leads to significant improvements in terms of response time, but also offers a means to cross-check the accuracy of answers (as each task is typically assigned to more than one person). Collecting answers from different contributors (or 'workers') allows for automatically identifying accurate responses using techniques such as majority voting (or other aggregation methods).…”

Section: Microtask Crowdsourcingmentioning

confidence: 99%

“…Existing frameworks for quality assessment of the Web of Data, including Linked Data, can be broadly classified as automated [13,18,19,32,37,38], semiautomated [8,15,29,53] and manual [5,36].…”

Section: Web Data Quality Assessmentmentioning

confidence: 99%

“…However, in this case, the results are difficult to interpret and require the user to specify different weights for different quality metrics at each step of the assessment, which makes it challenging especially when users may not know the dataset in much detail. CRO-CUS [8] is a clustering-based framework that identify outliers at ontologies' instance-level to detect inconsistencies in LD sets. Outliers are then assessed by non-experts denominated quality raters.…”

Section: Web Data Quality Assessmentmentioning

confidence: 99%

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Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Acosta

Zaveri²,

Simperl

et al. 2018

Self Cite

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In this paper we examine the use of crowdsourcing as a means to detect Linked Data quality problems that are difficult to uncover automatically. We base our approach on the analysis of the most common errors encountered in the DBpedia dataset, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and study different crowdsourcing approaches to identify these Linked Data quality issues, employing DBpedia as our use case: (i) a contest targeting the Linked Data expert community, and (ii) paid microtasks published on Amazon Mechanical Turk. We secondly focus on adapting the Find-Fix-Verify crowdsourcing pattern to exploit the strengths of experts and lay workers. By testing two distinct Find-Verify workflows (lay users only and experts verified by lay users) we reveal how to best combine different crowds' complementary aptitudes in Linked Data quality issue detection. Empirical results show that a combination of the two styles of crowdsourcing is likely to achieve more effective results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of DBpedia.

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“…The work of [5] uses clustering based ontology where its work in a semiautomatic environment. Dimitris et.…”

Section: Related Workmentioning

confidence: 99%