This article is part of the supplement: Semantic Web Applications and Tools for Life Sciences (SWAT4LS), 2009
Research
Mining semantic networks of bioinformatics e-resources from the literature
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* Corresponding author: Goran Nenadic G.Nenadic@manchester.ac.uk
1 School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
2 College of Telecommunication Engineering, National University of Sciences and Technology, Islamabad, Pakistan
3 Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland
Journal of Biomedical Semantics 2011, 2(Suppl 1):S4 doi:10.1186/2041-1480-2-S1-S4
Published: 7 March 2011Abstract
Background
There have been a number of recent efforts (e.g. BioCatalogue, BioMoby) to systematically catalogue bioinformatics tools, services and datasets. These efforts rely on manual curation, making it difficult to cope with the huge influx of various electronic resources that have been provided by the bioinformatics community. We present a text mining approach that utilises the literature to automatically extract descriptions and semantically profile bioinformatics resources to make them available for resource discovery and exploration through semantic networks that contain related resources.
Results
The method identifies the mentions of resources in the literature and assigns a set of co-occurring terminological entities (descriptors) to represent them. We have processed 2,691 full-text bioinformatics articles and extracted profiles of 12,452 resources containing associated descriptors with binary and tf*idf weights. Since such representations are typically sparse (on average 13.77 features per resource), we used lexical kernel metrics to identify semantically related resources via descriptor smoothing. Resources are then clustered or linked into semantic networks, providing the users (bioinformaticians, curators and service/tool crawlers) with a possibility to explore algorithms, tools, services and datasets based on their relatedness. Manual exploration of links between a set of 18 well-known bioinformatics resources suggests that the method was able to identify and group semantically related entities.
Conclusions
The results have shown that the method can reconstruct interesting functional links between resources (e.g. linking data types and algorithms), in particular when tf*idf-like weights are used for profiling. This demonstrates the potential of combining literature mining and simple lexical kernel methods to model relatedness between resource descriptors in particular when there are few features, thus potentially improving the resource description, discovery and exploration process. The resource profiles are available at http://gnode1.mib.man.ac.uk/bioinf/semnets.html webcite