scholarly journals BiOSS: A system for biomedical ontology selection

2014 ◽  
Vol 114 (1) ◽  
pp. 125-140 ◽  
Author(s):  
Marcos Martínez-Romero ◽  
José M. Vázquez-Naya ◽  
Javier Pereira ◽  
Alejandro Pazos
Keyword(s):  
Biomedical Ontology ◽  
Ontology Selection

scholarly journals National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge

2006 ◽  
Vol 10 (2) ◽  
pp. 185-198 ◽  
Author(s):  
Daniel L. Rubin ◽  
Suzanna E. Lewis ◽  
Chris J. Mungall ◽  
Sima Misra ◽  
Monte Westerfield ◽  
...  
Keyword(s):  
Scientific Knowledge ◽  
Biomedical Ontology

Special Issue: Biomedical Ontology in Action

2009 ◽  
Vol 4 (1) ◽  
pp. 1-4
Author(s):  
Olivier Bodenreider
Keyword(s):  
Biomedical Ontology ◽  
Special Issue

scholarly journals Exploring biomedical ontology mappings with graph theory methods

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2990 ◽  
Author(s):  
Simon Kocbek ◽  
Jin-Dong Kim
Keyword(s):  
Graph Theory ◽  
Biomedical Ontology ◽  
Data Consistency ◽  
Mapping Data ◽  
Time Points ◽  
Modularity Analysis ◽  
Analyse Data ◽  
Ontology Reuse ◽  
Core Ontologies ◽  
Over Time

Background In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies. Methods We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature. Results With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.

BRES: EXTRACTING MULTICLASS BIOMEDICAL RELATIONS WITH SEMANTIC NETWORK

2013 ◽  
Vol 25 (01) ◽  
pp. 1350009
Author(s):  
Lejun Gong ◽  
Ronggen Yang ◽  
Xiao Sun
Keyword(s):  
Text Mining ◽  
Argument Structure ◽  
Semantic Network ◽  
Noun Phrases ◽  
Biomedical Ontology ◽  
Biomedical Knowledge ◽  
Mining System ◽  
Semantic Enrichment ◽  
Shallow Parsing ◽  
Predicate Argument Structure

With an overwhelming amount of published biomedical research, the underlying biomedical knowledge is expanding at an exponential rate. This expansion makes it very difficult to find interested genetics knowledge. And therefore, there is an urgent need for developing text mining approaches to discover new knowledge from publications. This paper presents a text mining approach for multiclass biomedical relations based on predicate argument structure (PAS) and shallow parsing. The approach can mine explicit biomedical relations with semantic enrichment, and visualize relations with semantic network. It first identifies noun phrases based on shallow parsing, and then filters arguments from noun phrases via biomedical ontology dictionary. We have implemented BRES, a text mining system, based on our proposed approach. Our results obtained 67.7% F-measure, 62.5% precision and 73.8% recall for the test dataset. This also shows our proposed approach is promising for developing biomedical text mining technology. Highlights: • Mining multiclass biomedical relations; • Representing biomedical relations with semantic enrichment; • Visualizing relations by semantic network; • Extracting direct and indirect biomedical relations.

Large-scale biomedical ontology matching with ServOMap

IRBM ◽  
2013 ◽  
Vol 34 (1) ◽  
pp. 56-59 ◽  
Author(s):  
M. Ba ◽  
G. Diallo
Keyword(s):  
Large Scale ◽  
Biomedical Ontology ◽  
Ontology Matching

scholarly journals Biotea, semantics for Pubmed Central

2017 ◽  
Author(s):  
Alexander Garcia ◽  
Federico Lopez ◽  
Leyla Garcia ◽  
Olga Giraldo ◽  
Victor Bucheli ◽  
...  
Keyword(s):  
Linked Data ◽  
Scientific Literature ◽  
Query Language ◽  
Biomedical Ontology ◽  
Sparql Query ◽  
Biomedical Literature ◽  
Automatic Annotation ◽  
Pubmed Central ◽  
Web Technologies ◽  
Aggregate Content

A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Availability: Our datasets, methods and techniques are available at http://biotea.github.io

Sign in / Sign up

Export Citation Format

Share Document