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A Semantic Framework to Improve Interoperability of Malaria Surveillance Systems
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In: Online Journal of Public Health Informatics; Vol 10 No 1 (2018) ; 1947-2579 (2018)
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OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents
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Abstract:
Motivation: Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. Results: We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. Availability: The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. Contact: witte@semanticsoftware.info
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URL: http://doc.rero.ch/record/300573/files/btr452.pdf
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Why the Body Comes First: Effects of Experimenter Touch on Infants' Word Finding
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In: Faculty Journal Articles (2015)
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| 4 |
Access, recognition and representation: philanthropy and ageing in a multicultural society
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Semantic querying of relational data for clinical intelligence: a semantic web services-based approach
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| 7 |
A-class genitive subject effect : a pragmatic and discourse grammar approach to A-and O-class genitive subject selection in Hawaiian
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| 10 |
Diaspora philanthropy in Australia: a preliminary critique
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In: Diaspora: a journal of transnational studies, Vol. 16, no. 3 (Winter 2007), pp. 323-336 (2007)
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