research-article

Bayesian ontology querying for accurate and noise-tolerant semantic searches

Authors:

Sebastian Bauer,

Sebastian Köhler,

Marcel H. Schulz,

Peter N. RobinsonAuthors Info & Claims

Bioinformatics, Volume 28, Issue 19

Pages 2502 - 2508

https://doi.org/10.1093/bioinformatics/bts471

Published: 01 October 2012 Publication History

Abstract

Motivation: Ontologies provide a structured representation of the concepts of a domain of knowledge as well as the relations between them. Attribute ontologies are used to describe the characteristics of the items of a domain, such as the functions of proteins or the signs and symptoms of disease, which opens the possibility of searching a database of items for the best match to a list of observed or desired attributes. However, naive search methods do not perform well on realistic data because of noise in the data, imprecision in typical queries and because individual items may not display all attributes of the category they belong to.

Results: : We present a method for combining ontological analysis with Bayesian networks to deal with noise, imprecision and attribute frequencies and demonstrate an application of our method as a differential diagnostic support system for human genetics.

Availability: We provide an implementation for the algorithm and the benchmark at http://compbio.charite.de/boqa/.

Contact: [email protected] or [email protected]

Supplementary Information: Supplementary Material for this article is available at Bioinformatics online.

Cited By

View all

Yang JShu LHan MPan JChen LYuan TTan LShu QDuan HLi H(2024)RDmasterComputers in Biology and Medicine10.1016/j.compbiomed.2024.107924169:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.107924
Yang JShu LDuan HLi H(2023)A robust phenotype-driven likelihood ratio analysis approach assisting interpretable clinical diagnosis of rare diseasesJournal of Biomedical Informatics10.1016/j.jbi.2023.104372142:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.jbi.2023.104372
Shen YYuan KChen DColloc JYang MLi YLei K(2018)An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescriptionArtificial Intelligence in Medicine10.1016/j.artmed.2018.01.00386:C(20-32)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.artmed.2018.01.003
Show More Cited By

Bayesian ontology querying for accurate and noise-tolerant semantic searches
1. Computing methodologies
  1. Artificial intelligence
2. Theory of computation

Recommendations

Querying EL Ontology Stream with C-SPARQL
ACSAT '13: Proceedings of the 2013 International Conference on Advanced Computer Science Applications and Technologies

The availability of streaming information is progressively increasing, thanks to the knowledge management technologies such as ontologies that captures knowledge about this information. The semantic web initiative provides standards such as RDF and OWL, ...
Querying semantic web resources using TRIPLE views
LNCS-ISWC'03: Proceedings of the Second International Conference on Semantic Web Conference

Resources on the Semantic Web are described by metadata based on some formal or informal ontology. It is a common situation that casual users are not familiar with a domain ontology in detail. This makes it difficult for such users (or their user tools) ...
Merging ontology by semantic enrichment and combining similarity measures

In this paper, we present a new approach to merge OWL ontologies by semantic enrichment of initial ontologies. This work is situated in the general context of stored information heterogeneity in a decisional system such as data, metadata and knowledge, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Bioinformatics Volume 28, Issue 19

October 2012

154 pages

ISSN:1367-4803

Issue’s Table of Contents

Publisher

Oxford University Press, Inc.

United States

Publication History

Published: 01 October 2012

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yang JShu LHan MPan JChen LYuan TTan LShu QDuan HLi H(2024)RDmasterComputers in Biology and Medicine10.1016/j.compbiomed.2024.107924169:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.107924
Yang JShu LDuan HLi H(2023)A robust phenotype-driven likelihood ratio analysis approach assisting interpretable clinical diagnosis of rare diseasesJournal of Biomedical Informatics10.1016/j.jbi.2023.104372142:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.jbi.2023.104372
Shen YYuan KChen DColloc JYang MLi YLei K(2018)An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescriptionArtificial Intelligence in Medicine10.1016/j.artmed.2018.01.00386:C(20-32)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.artmed.2018.01.003
Hu HElrafey AKerschberg L(2017)Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian NetworksProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3110136(904-907)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3110136

Abstract

Cited By

Recommendations

Querying EL Ontology Stream with C-SPARQL

Querying semantic web resources using TRIPLE views

Merging ontology by semantic enrichment and combining similarity measures

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations