Tools for Knowledge Acquisition within the NeuroScholar system and their application to anatomical tract-tracing data. Burns, A, G, Cheng, & C, W J Biomed Discov Collab, 1(1):10, 2006. ![Tools for Knowledge Acquisition within the NeuroScholar system and their application to anatomical tract-tracing data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex ABSTRACT: BACKGROUND: Knowledge bases that summarize the published literature provide useful online references for specific areas of systems-level biology that are not otherwise supported by large-scale databases. In the field of neuroanatomy, groups of small focused teams have constructed medium size knowledge bases to summarize the literature describing tract-tracing experiments in several species. Despite years of collation and curation, these databases only provide partial coverage of the available data. Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation. RESULTS: In this paper, we describe an open-source, freely available knowledge management system called 'NeuroScholar' that allows straightforward structured markup of the PDF files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. Through the use of this system, not only could members of the community contribute to the collation task, but input data can be gathered for automated approaches to permit knowledge acquisition through the use of Natural Language Processing (NLP). CONCLUSIONS: We present a functional, working tool to permit users to populate knowledge bases for neuroanatomical connectivity data from the literature through the use of structured questionnaires. This system is open-source, fully functional and available for download from http://www.neuroscholar.org/.
@article{ burns_tools_2006,
title = {Tools for Knowledge Acquisition within the {NeuroScholar} system and their application to anatomical tract-tracing data},
volume = {1},
url = {http://www.j-biomed-discovery.com/content/1/1/10},
doi = {doi:10.1186/1747-5333-1-10},
abstract = {{ABSTRACT:} {BACKGROUND:} Knowledge bases that summarize the published literature provide useful online references for specific areas of systems-level biology that are not otherwise supported by large-scale databases. In the field of neuroanatomy, groups of small focused teams have constructed medium size knowledge bases to summarize the literature describing tract-tracing experiments in several species. Despite years of collation and curation, these databases only provide partial coverage of the available data. Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation. {RESULTS:} In this paper, we describe an open-source, freely available knowledge management system called {'NeuroScholar'} that allows straightforward structured markup of the {PDF} files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. Through the use of this system, not only could members of the community contribute to the collation task, but input data can be gathered for automated approaches to permit knowledge acquisition through the use of Natural Language Processing {(NLP).} {CONCLUSIONS:} We present a functional, working tool to permit users to populate knowledge bases for neuroanatomical connectivity data from the literature through the use of structured questionnaires. This system is open-source, fully functional and available for download from http://www.neuroscholar.org/.},
number = {1},
journal = {J Biomed Discov Collab},
author = {Burns, G A and Cheng, W C},
year = {2006},
pages = {10},
annote = {1747-5333 {(Electronic)} Journal article},
file = {Attachment:/Users/burns/Library/Application Support/Zotero/Profiles/5ctc8acy.default/zotero/storage/Z2VV9Z2P/1747-5333-1-10.pdf:application/pdf}
}
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Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation. RESULTS: In this paper, we describe an open-source, freely available knowledge management system called 'NeuroScholar' that allows straightforward structured markup of the PDF files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. 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