OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

Naderi, Nona; Kappler, Thomas; Baker, Christopher J. O.; Witte, René

doi:10.1093/bioinformatics/btr452

Cited by 40 publications

(36 citation statements)

References 14 publications

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“…The OrganismTagger performance has previously been evaluated on two corpora, where it showed a precision of 95%-99%, a recall of 94%-97%, and a grounding accuracy of 97.4%-97.5% [22]. Since its results here are lower, we examined the error cases in more detail.…”

Section: Discussionmentioning

confidence: 99%

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Semantic text mining support for lignocellulose research

Meurs

Murphy

Morgenstern

et al. 2012

BMC Med Inform Decis Mak

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BackgroundBiofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties.ResultsSemantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources.ConclusionsWorking closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information.

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Section: Discussionmentioning

confidence: 99%

“…The OrganismTagger is a hybrid rule-based/machine-learning system that extracts organism mentions from the biomedical literature, normalizes them to their scientific name, and provides grounding to the NCBI Taxonomy database [22]. …”

Section: Introductionmentioning

confidence: 99%

Semantic text mining support for lignocellulose research

Meurs

Murphy

Morgenstern

et al. 2012

BMC Med Inform Decis Mak

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“…Typically, TM system that are based on algorithms such as hidden Markov models (HMM) [57,58], maximum entropy Markov models (MEMM) [59], conditional random fields (CRF) [60,61], and support vector machines (SVM) [62,63], need to be trained on a carefully constructed annotated training data set that is representative for the real life data set before the actual NER task. Machine learning based TM systems are used for instance to identify chemical entities in text [64], or are used in combination with rule-based and lexical methods to identify organism names in text [65] or used for extraction of cancer staging information from health records to improve clinical decision making [66].…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Application of text mining in the biomedical domain

Fleuren

Alkema

2015

Methods

163

107

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“…On the one hand are tools for the fundamental task of recognising names within text, such as the dictionary-based TaxonFinder [12], Linnaneus [13] and OrganismTagger [14]; the machine learning-based NetiNeti [15]; and hybrid systems such as TaxonGrab [16] and SPECIES [17]. On the other hand are name-linking tools which are aimed at resolving taxonomic names mentioned in text to their preferred names, accomplished at three various levels: (1) orthographic or spelling, i.e, linking misspelt names to the correct ones; (2) nomenclature, i.e., taking into account scientific names with or without author, date and annotations; and (3) semantic, i.e., taking into account both scientific names and vernacular names.…”

Section: Introductionmentioning

confidence: 99%

Constructing a biodiversity terminological inventory

et al. 2017

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The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.

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OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

Abstract: witte@semanticsoftware.info.

Cited by 40 publications

References 14 publications

Semantic text mining support for lignocellulose research

Semantic text mining support for lignocellulose research

Application of text mining in the biomedical domain

Constructing a biodiversity terminological inventory

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