A competency question-oriented approach for the transformation of semi-structured bioinformatics data into linked open data
Bioinformatics data obtained using different molecular biology techniques must be processed through different analysis tools to discover new biological knowledge. Since plain processed data have no explicit semantic value, the extraction of additional knowledge through data exploration would benefit...
Saved in:
Published in: | Engineering applications of artificial intelligence Vol. 90; p. 103495 |
---|---|
Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
01-04-2020
|
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Bioinformatics data obtained using different molecular biology techniques must be processed through different analysis tools to discover new biological knowledge. Since plain processed data have no explicit semantic value, the extraction of additional knowledge through data exploration would benefit from the transformation of bioinformatics data into Linked Open Data (LOD). Different approaches have been proposed to support the transformation of different types of biomedical data into LOD. However, these approaches are not flexible enough so they can be easily adapted for the transformation of semi-structured bioinformatics data into LOD. Thus, this paper proposes a novel approach to support such transformation. According to this approach, a set of competency questions drive not only the definition of transformation rules, but also the data transformation and exploration afterwards. The paper also presents a support toolset and describes the successful application of the proposed approach in the functional genomics domain.
•Transformation of semi-structured data (SSD) into linked open data (LOD).•Stepwise, competency question-oriented approach.•Language for specifying data item to ontology concept semantic equivalence.•Support toolset for the transformation of SSD into LOD. |
---|---|
ISSN: | 0952-1976 1873-6769 |
DOI: | 10.1016/j.engappai.2020.103495 |