Evolution of biomedical ontologies and mappings: Overview of recent approaches

Groß, Anika; Pruski, Cédric; Rahm, Erhard

doi:10.1016/j.csbj.2016.08.002

Cited by 32 publications

(19 citation statements)

References 48 publications

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“…Despite maintenance and standard policies for adding terms, ontological organization is still subject to human error and disagreement, necessitating quality assurance and revising, especially as ontologies evolve or merge. A recent review of current methods for biomedical ontology mapping highlights the importance in developing semi-automatic methods to aid in ontology evolution efforts and reiterates the aforementioned concept of semantic correspondence in terms of scoping between terms [20]. Methods incorporating such correspondences have been published elsewhere, but these deal with issues of ontology evolution and merging, and not with categorizing terms into user-defined subsets [21,22].…”

Section: Maintenance Of Ontologiesmentioning

confidence: 99%

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Flight

Moseley

2018

Preprint

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results)relations are included in the analysis. Therefore, we developed a custom graph parsing scheme that allows these relations to be incorporated without resulting in erroneous term categorizations. This enables a more robust semantic scoping utilization of Gene Ontology, facilitating summarization of annotations in large data sets. We also demonstrate that GOcats is well suited for comparing results from separate annotation sources due to its ability to allow adjustment of categories to the appropriate annotation term granularity. GOcats thus facilitates more robust interpretation and comparison of experimental and knowledgebase annotation sources and provides new tools for semantic scoping utilization and development of Gene Ontology. Furthermore, when used alongside annotation enrichment tools such as categoryCompare2, GOcats' unique method for inferring category membership results in both improved enrichment statistics and the identification of enriched terms otherwise impossible-to-identify with statistical significance when compared to using conventional ontology inference rules. 8 has_part describe part-whole (mereological) correspondence. Therefore, we consider scoping relations to be comprised of is_a, part_of, and has_part, and mereological relations to be comprised of part_of and has_part.There are three versions of the GO database, each containing aspects of the CV with varying complexity: go-basic is filtered to exclude relations that span across multiple sub-ontologies and to include only relations that point toward the root of the ontology; go or go-core contains additional relations, such as has_part that may span sub-ontologies and which point both toward and away from the root of the ontology; and go-plus contains yet more relations in addition to cross-references to entries in external databases like the Chemical Entities of Biological Interest (ChEBI) ontology (Munoz-Torres & Carbon, 2017). The first and second versions are available in the Open Biomedical Ontology (OBO) flat text file formatting, while the third is available only in the Web Ontology Language (OWL) RDF/XML format. Categorization-relevant issues in GOOntological graphs are typically designed as directed graphs, meaning that every edge has directionality or directed acyclic graphs (DAGs), meaning that no path exists that leads back to a node already visited if one were to traverse the graph stepwise.This allows the graph to form a complex semantic model of biology containing both general concepts and more-specific (fine-grained) concepts. The "parent-child" relation hierarchy allows biological entities to be annotated at any level of specificity (granularity) with a single term code, as fine-grained terms intrinsically capture the meaning of every one of its parent and ancestor terms through the linking of relation-defining is_a edges in the graph. However, it is deceptively non-trivial to reverse the logic and organize 1 0 Author Contributions E.W.H. worked on the design of GOcats, implemented GOcats, performed all analyses,...

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Section: Maintenance Of Ontologiesmentioning

confidence: 99%

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Flight

Moseley

2018

Preprint

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“…Ontologi dapat didefinisikan dalam banyak makna diantaranya dikemukakan oleh Neches bahwa ontologi merupakan pengertian dan relasi kosakata dalam sebuah bidang tertentu menggunakan aturan yang mengkomb inasikan antara pengertian istilah yang berkaitan dengan bidang tersebut serta relasi untuk mendefin isikan kosakata tertentu [2] dan menurut Gruber ontologi adalah spesifikasi secara nyata dan detail dari sebuah istilah untuk domain tertentu dan relasi yang terdapat di dalamnya [3].…”

Section: Pendahuluanunclassified

Knowledge Base Program Studi Berbasis Ontologi

Eriya

Septiriana

2017

JURNAL MULTIMEDIA NETWORKING INFORMATICS

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“…First, the use of standards is facilitated. Examples of such standards are the ABCD schema (Access to Biological Collection Data, [1]), the SEEK observation ontology [4], and biomedical ontologies [5]. A common pitfall of standards lies in the standardization process itself.…”

Section: Introductionmentioning

confidence: 99%

“…It is widely recognized that ontologies play a central role in modern scientific applications [5] [6] since their use is considered a possible solution for semantic based integration [2] [7]. Nevertheless a clear methodology for setting up the data integration task (cf.…”

Section: Introductionmentioning

confidence: 99%

Semantic based Data Integration in Scientific Workflows

Rehman¹,

Ahmed²,

Waqas³

2017

ijacsa

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Abstract-Data Integration has become the most prominent aspect of data management applications, especially in scientific domains like ecology, biology, and geosciences. Today's complex scientific applications and the rise of diverse data generating devices in scientific domains (e.g. sensors) have made data integration a challenging task. In response to these types of challenges, data management applications are providing groundbreaking functionalities which come at the price of high complexity. This paper presents a semantic data integration framework which is based on the exploitation of ontologies. Exploiting a Description Logics formalism and associated reasoning procedures, the framework is able the handle heterogeneous formats and different semantics. Besides an indepth discussion of the ontology-based integration capability, the paper also discusses a brief overview of the system architecture and its application in a real world scenario taken from ecological research.

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Evolution of biomedical ontologies and mappings: Overview of recent approaches

Cited by 32 publications

References 48 publications

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Knowledge Base Program Studi Berbasis Ontologi

Semantic based Data Integration in Scientific Workflows

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