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Mining semantic networks of bioinformatics e-resources from the literature.

Afzal H, Eales J, Stevens R, Nenadic G - J Biomed Semantics (2011)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Telecommunication Engineering, National University of Sciences and Technology, Islamabad, Pakistan. Hammad.Afzal@deri.org.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

A snapshot of a Web service description taken from BioCatalogue
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Related In: Results  -  Collection

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Figure 1: A snapshot of a Web service description taken from BioCatalogue

Mentions: One of the key aims of providing bioinformatics resources with semantic descriptions is to improve resource discovery. Semantically-described resources can not only be searched, browsed and discovered by using keyword-based queries (for instance, via their names or task descriptions), but also on the basis of the semantic relatedness of their functionalities or their input/output parameters. For example, a user can search for a Web service that corresponds to a particular input, output or operation performed. If, however, the retrieved services do not fulfill the exact requirement or are not available, the user may explore similar services (for example, with more generic/specific input/output, but still with a related functionality). This process has been facilitated by concept-based annotations using domain ontologies that have been used to annotate the resources (as in myGrid [7] and BioCatalogue). Descriptions of services may also have “pre-computed” similar services (see Figure 1) so that the users can identify them without additional searches.


Mining semantic networks of bioinformatics e-resources from the literature.

Afzal H, Eales J, Stevens R, Nenadic G - J Biomed Semantics (2011)

A snapshot of a Web service description taken from BioCatalogue
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3105496&req=5

Figure 1: A snapshot of a Web service description taken from BioCatalogue
Mentions: One of the key aims of providing bioinformatics resources with semantic descriptions is to improve resource discovery. Semantically-described resources can not only be searched, browsed and discovered by using keyword-based queries (for instance, via their names or task descriptions), but also on the basis of the semantic relatedness of their functionalities or their input/output parameters. For example, a user can search for a Web service that corresponds to a particular input, output or operation performed. If, however, the retrieved services do not fulfill the exact requirement or are not available, the user may explore similar services (for example, with more generic/specific input/output, but still with a related functionality). This process has been facilitated by concept-based annotations using domain ontologies that have been used to annotate the resources (as in myGrid [7] and BioCatalogue). Descriptions of services may also have “pre-computed” similar services (see Figure 1) so that the users can identify them without additional searches.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Telecommunication Engineering, National University of Sciences and Technology, Islamabad, Pakistan. Hammad.Afzal@deri.org.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus