CodeSlinger: An Interactive Biomedical Ontology Browser

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.

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BRES: EXTRACTING MULTICLASS BIOMEDICAL RELATIONS WITH SEMANTIC NETWORK

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500099 ◽

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.

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Large-scale biomedical ontology matching with ServOMap

IRBM ◽

10.1016/j.irbm.2012.12.011 ◽

2013 ◽

Vol 34 (1) ◽

pp. 56-59 ◽

Cited By ~ 12

Author(s):

M. Ba ◽

G. Diallo

Keyword(s):

Large Scale ◽

Biomedical Ontology ◽

Ontology Matching

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Biotea, semantics for Pubmed Central

10.7287/peerj.preprints.3469 ◽

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

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National Center for Biomedical Ontology

Encyclopedia of Systems Biology ◽

10.1007/978-1-4419-9863-7_1515 ◽

2013 ◽

pp. 1492-1492 ◽

Cited By ~ 1

Author(s):

Mark A. Musen

Keyword(s):

Biomedical Ontology

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Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽

10.3390/biology10121287 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1287

Author(s):

Xingsi Xue ◽

Pei-Wei Tsai ◽

Yucheng Zhuang

Keyword(s):

Evolutionary Algorithm ◽

Large Scale ◽

Biomedical Ontology ◽

Biomedical Data ◽

Biomedical Ontologies ◽

Ontology Alignment ◽

Multi Objective Optimization ◽

Matching Problem ◽

Multi Objective ◽

Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

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