Abstract
Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest.We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges, with top ranked results in several of them.
Highlights
Text mining technologies are increasingly providing an effective response to the growing demand for faster access to the vast amounts of information hidden in the literature
The OntoGene annotator offers an open architecture allowing for a considerable level of customization so that it is possible to plug in in-house terminologies
We have presented novel web services which aim at making the text mining capabilities of the OntoGene system more accessible for a variety of potential users
Summary
Text mining technologies are increasingly providing an effective response to the growing demand for faster access to the vast amounts of information hidden in the literature. The biomedical text mining community regularly verifies the progress of the field through competitive evaluations, such as BioCreative [8,9,10], BioNLP [11,12], i2b2 [13], CALBC [14], CLEF-ER [15], DDI [16], BioASQ [17], etc Each of these competitions targets different aspects of the problem, sometimes with several subtasks, such as detection of mentions of specific entities (e.g. gene and chemicals), detection of protein interactions, assignment of Gene Ontology tags (BioCreative), There are numerous institutional attempts to structure some of the knowledge derived from the scientific literature into a more accessible format, such as that represented by life science databases. Examples of well-known biomedical databases are UniProt (proteins) [18], Entrez Gene (genes) [19], NCBI Taxonomy (species) [20], IntAct (protein interactions) [21], BioGrid (protein and genetic interactions) [22,23], PharmGKB (drug-gene-disease relations) [24], CTD (chemical-gene-disease relations) [25], and RegulonDB (regulatory interactions in E. coli ) [26]
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