An ontology‐based approach to handling information quality in e‐Science

Preece, Alun; Missier, Paolo; Ernbury, S; Jin, Baocheng; Greenwood, Mark

doi:10.1002/cpe.1195

Cited by 11 publications

(10 citation statements)

References 9 publications

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“…f) Preece et al (2008) propose a framework for managing information quality (IQ) in a e-Science context. The framework allows scientists to define the quality characteristics that are of importance to them in their particular domain.…”

Section: D) Cai and Xumentioning

confidence: 99%

Ontologies for Reusing Data Cleaning Knowledge

Almeida

Oliveira

Braga

et al. 2012

2012 IEEE Sixth International Conference on Semantic Computing

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The emergence of new business models, namely, the establishment of partnerships between organizations, the chance that companies have of adding existing data on the web, especially in the semantic web, to their information, led to the emphasis on some problems existing in databases, particularly related to data quality.Poor data can result in loss of competitiveness of the organizations holding these data, and may even lead to their disappearance, since many of their decision-making processes are based on these data. For this reason, data cleaning is essential. Current approaches to solve these problems are closely linked to database schemas and specific domains. In order that data cleaning can be used in different repositories, it is necessary for computer systems to understand these data, i.e., an associated semantic is needed. The solution presented in this paper includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different sources. With data cleaning operations defined at a conceptual level and existing mappings between domain ontologies and an ontology that results from a database, they may be instantiated and proposed to the expert / specialist to be executed over that database, thus enabling their interoperability.

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Section: D) Cai and Xumentioning

confidence: 99%

Ontologies for Reusing Data Cleaning Knowledge

Almeida

Oliveira

Braga

et al. 2012

2012 IEEE Sixth International Conference on Semantic Computing

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“…As Table 8 represents, the second application is the use of domain ontologies for the assessment of data quality in the querying requirements (Mabotuwana and Warren 2009), extracting knowledge from natural language documents (Valencia-Garcia et al 2008), and data expression (Preece et al 2008). The majority of these studies used precision and recall as metrics to assess the accuracy and validate the ontological approaches (Brank et al 2005;Brewster et al 2004;Euzenat 2007;Gangemi et al 2006;Li 2010;Min et al 2009;Pathak et al 2012aPathak et al , 2012bSpasic and Ananiadou, 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007).…”

Section: %mentioning

confidence: 99%

“…Formal ontological approaches enable the systematic development of automated, valid and reliable methods to assess and manage the DQ and semantic interoperability issues (Lee et al 2009;Valencia-Garcia et al 2008;Verma et al 2009Verma et al , 2008. The expressiveness of ontology based models can facilitate accuracy and precision compared to non-ontology models and approaches (Esposito 2008a(Esposito , 2008bPreece et al 2008). Current ontological approaches have limited evaluation.…”

Section: Moody and Shanks 2003mentioning

confidence: 99%

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

et al. 2014

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This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

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“…Hence, the Semantic Web is regarded by many researchers as a way to solve this issue by providing context awareness and structure to the web content. It is understandable that Semantic Web will not survive without the development and acceptance of ontologies [17,18]. Many researchers argue that the core of the Semantic Web is ontologies [16].…”

Section: Semantic Grid Ontologymentioning

confidence: 99%

A methodology for ontology update in the semantic grid environment

Flahive

Taniar

Rahayu

et al. 2012

Concurrency and Computation

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Ontology as a formal representation of a domain knowledge has played an important role in a distributed environment whereby semantic interoperability is a major factor. In this paper, we particularly focus on a distributed ontology framework that utilizes Semantic Grid resources. The semantic representation in a machine-understandable format (i.e., an ontology) is the backbone that enables interoperability between different user nodes in a semantic grid environment. However, the domain knowledge represented within an ontology is not static. From time to time, its concepts, properties and relationships need to be replaced or updated. Although many existing work have been focusing on how to utilize an ontology to support interoperability within a distributed environment, they often assume a rather static ontology. This paper focuses on formalizing and validating the process of ontology update, whereby sections of one ontology O 2 are replaced by a subset extracted from another ontology O 1 . In the first phase, a subset S 1 is extracted from ontology O 1 . Then in the second phase, the concepts in ontology O 2 are replaced by S 1 . At the end of the process, the resulting ontology O 2 must still be a valid ontology. A semantic completeness checking also needs to be conducted so that the updated ontology O 2 is complete. A case study based on the Unified Medical Language System ontology from the medical informatics domain is presented. We use a semantic grid environment to build a framework for reusing, extracting and updating an ontology using a SOA. These allow the subset extracted from one ontology, to replace sections of another ontology, using shared resources in the semantic grid environment. A prototype of the framework is built using Web Services and a complexity evaluation measure is presented. The results of several simulations show ontology update in the semantic grid is a viable solution and can be further optimized. 783 use in their entirety. Additionally, existing ontologies evolve. Concepts and relationships that exist in the current ontology may be replaced by new concepts and relationships from other ontologies. This paper primarily focuses on ontology update, whereby certain sections of one ontology are replaced by one or more sections from within a second ontology. Figure 1 illustrates a section of ontology 2 being replaced by a section from ontology 1.Many completeness and validation checks are required in this ontology update technique because the subset that is being replaced and the subontology that is replacing it must correspond in many ways. The requirement of consistency and semantic completeness cannot occur before the updating takes place. It is only after the update that inconsistencies and semantic incompleteness can be identified. Because of this restriction, ontology update is difficult and very sensitive to the meaning of the elements being replaced. The most important factor is making sure that every concept that is being replaced in ontology 2 must exist in ontology 1. If these co...

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An ontology‐based approach to handling information quality in e‐Science

Cited by 11 publications

References 9 publications

Ontologies for Reusing Data Cleaning Knowledge

Ontologies for Reusing Data Cleaning Knowledge

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

A methodology for ontology update in the semantic grid environment

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