Deriving Conceptual Data Models from Domain Ontologies for Bioinformatics

El-Ghalayini, Haya; Odeh, Mohammed; McClatchey, Richard; Arnold, Doug

doi:10.1109/ictta.2006.1684992

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…Many other works [29,16,25,10,15,18,21] present conceptual models for explaining biological entities and their interactions in terms of conceptual data structures. With our approach, similar to DeepBlue [2], we instead use conceptual modeling for driving the continuous process of metadata integration and for offering high-level query interfaces on metadata for locating relevant datasets, under the assumption that users will then manage these datasets for solving biological or clinical questions.…”

Section: Related Workmentioning

confidence: 99%

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Bernasconi

Ceri

Campi

et al. 2017

Lecture Notes in Computer Science

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Many repositories of open data for genomics, collected by worldwide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus.

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Section: Related Workmentioning

confidence: 99%

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Bernasconi

Ceri

Campi

et al. 2017

Lecture Notes in Computer Science

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“…Moreover, only if the data is stored and managed correctly can one achieve the most comprehensive and reliable discovery of biological knowledge. An ontology also can be used to generate multiple conceptual data models [20,32,61], which improves their quality and ensures interoperability between them. Both scenarios require a formal, logic-based, foundation of a conceptual data modeling language to foster precision, accuracy, adequate coverage of the subject domain semantics, and implementability.…”

Section: Focus On Automated Reasoningmentioning

confidence: 99%

Ontology‐Driven Formal Conceptual Data Modeling for Biological Data Analysis

Keet

2013

Biological Knowledge Discovery Handbook

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Biological data modeling serves many purposes, and many approaches exist that are used in this endeavor. The main topics of advanced conceptual data modeling for database and Object-Oriented software development to support biological data analysis are included in Figure 1.1, which extend the traditional 'waterfall' software development methodology as depicted in bold in Figure 1.2. The scope of this chapter is to provide an overview of the ontological and logical aspects of conceptual data modeling tailored to molecular biology and biological knowledge discovery.Many databases and software applications have been and are being developed in bioinformatics, which, following good computing methodologies, are-or should have been-developed in stages, going from requirements analysis ('what should the envisioned software do?') and conceptual analysis ('what data should it be able to manage?') to design-level code and then to the actual implementation. It is well-known that omitting the conceptual analysis stage by going straight to coding or scripting just adds to the pile of one-off (bioinformatics) tools that have more bugs and are much less, or not at all, maintainable and interoperable. Conversely, availing of a proper software development methodology with a representitle, edition.

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“…They wish to enrich it with semantic integrity constraints derived from some large existing general ontology. El‐Ghalayini et al (2006) study the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed to generate a conceptual data model from a given domain ontology.…”

Section: Related Workmentioning

confidence: 99%

Maintaining web application: an ontology‐based reverse engineering approach

Benslimane¹,

Malki²,

Bouchiha³

2009

International Journal of Web Information Systems

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PurposeWeb applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.Design/methodology/approachThe proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.FindingsThe advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.Originality/valueThe strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.

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Deriving Conceptual Data Models from Domain Ontologies for Bioinformatics

Cited by 7 publications

References 10 publications

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Ontology‐Driven Formal Conceptual Data Modeling for Biological Data Analysis

Maintaining web application: an ontology‐based reverse engineering approach

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