The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

Malone, James; Brown, Andy; Lister, Allyson L.; Ison, Jon C.; Hull, Duncan; Parkinson, Helen; Stevens, Robert

doi:10.1186/2041-1480-5-25

Cited by 67 publications

(49 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did find the Software Ontology, which supports annotation of software tools by type, manufacturer, inputs, outputs, and uses. Software Ontology does not provide enough detail to model a KO and its parts …”

Section: Background and Significancementioning

confidence: 99%

The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems

Flynn

Friedman

Boisvert

et al. 2018

Learning Health Systems

View full text Add to dashboard Cite

Introduction Health systems are challenged by care underutilization, overutilization, disparities, and related harms. One problem is a multiyear latency between discovery of new best practice knowledge and its widespread adoption. Decreasing this latency requires new capabilities to better manage and more rapidly share biomedical knowledge in computable forms. Knowledge objects package machine‐executable knowledge resources in a way that easily enables knowledge as a service. To help improve knowledge management and accelerate knowledge sharing, the Knowledge Object Reference Ontology (KORO) defines what knowledge objects are in a formal way. Methods Development of KORO began with identification of terms for classes of entities and for properties. Next, we established a taxonomical hierarchy of classes for knowledge objects and their parts. Development continued by relating these parts via formally defined properties. We evaluated the logical consistency of KORO and used it to answer several competency questions about parthood. We also applied it to guide knowledge object implementation. Results As a realist ontology, KORO defines what knowledge objects are and provides details about the parts they have and the roles they play. KORO provides sufficient logic to answer several basic but important questions about knowledge objects competently. KORO directly supports creators of knowledge objects by providing a formal model for these objects. Conclusion KORO provides a formal, logically consistent ontology about knowledge objects and their parts. It exists to help make computable biomedical knowledge findable, accessible, interoperable, and reusable. KORO is currently being used to further develop and improve computable knowledge infrastructure for learning health systems.

show abstract

Section: Background and Significancementioning

confidence: 99%

The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems

Flynn

Friedman

Boisvert

et al. 2018

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…The current Bio-TDS version includes three ontologies to enable robust annotations and searching capabilities. These ontologies are: EDAM, an ontology of concepts that are prevalent within bioinformatics, including types of data, data identifiers, data formats, operations and topics (http://edamontology.org/) (22); SWO, a resource for describing analytic tools, their types, tasks, versions, data requirements and provenance (http://theswo.sourceforge.net/) (23); and NGSOnto, an ontology to describe workflows from DNA extraction to contigs in a Whole Genome Sequence experiment (http://darwin.phyloviz.net/∼msilva/NGSonto/). Attribute values within each BETS document are queried against these ontologies and the located terms are used to annotate the analytic tool.…”

Section: Methodsmentioning

confidence: 99%

“…For example, ELIXIR has based its core annotation workflow on the EDAM ontology (22). EDAM focuses only on one bioinformatics domain information representation, and does not represent some key tool development information such as version or platform which are available in the Software Ontology (SWO) (23). …”

Section: Introductionmentioning

confidence: 99%

Bio-TDS: bioscience query tool discovery system

et al. 2016

View full text Add to dashboard Cite

Bioinformatics and computational biology play a critical role in bioscience and biomedical research. As researchers design their experimental projects, one major challenge is to find the most relevant bioinformatics toolkits that will lead to new knowledge discovery from their data. The Bio-TDS (Bioscience Query Tool Discovery Systems, http://biotds.org/) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. The Bio-TDS is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 15 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation. The Bio-TDS’s scientific impact was evaluated using sample questions posed by researchers retrieved from Biostars, a site focusing on biological data analysis. The Bio-TDS was compared to five similar bioscience analytic tool retrieval systems with the Bio-TDS outperforming the others in terms of relevance and completeness. The Bio-TDS offers researchers the capacity to associate their bioscience question with the most relevant computational toolsets required for the data analysis in their knowledge discovery process.

show abstract

“…Over 8,800 of these classes are new to ERO. Most were imported from other ontologies, e.g., the Mammalian Phenotype Ontology (MP) [51] and the Software Ontology (SWO) [52]. Similarly, most of the 7,727 added classes in the introduced partial-area material entity (7758) are imported from UBERON [53].…”

Section: Resultsmentioning

confidence: 99%

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network

Ochs

Perl

Geller

et al. 2015

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology’s content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. In this paper, we introduce Diff Abstraction Networks (“Diff AbNs”), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and Eagle-I Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated.

show abstract

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

Cited by 67 publications

References 40 publications

The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems

The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems

Bio-TDS: bioscience query tool discovery system

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network

Contact Info

Product

Resources

About