Abstract
Content management systems and frameworks (CMS/F) play a key role in Web development. They support common Web operations and provide for a number of optional modules to implement customized functionalities. Given the increasing demand for text mining (TM) applications, it seems logical that CMS/F extend their offer of TM modules. In this regard, this work contributes to Drupal CMS/F with modules that support customized named entity recognition and enable the construction of domain-specific document search engines. Implementation relies on well-recognized Apache Information Retrieval and TM initiatives, namely Apache Lucene, Apache Solr and Apache Unstructured Information Management Architecture (UIMA). As proof of concept, we present here the development of a Drupal CMS/F that retrieves biomedical articles and performs automatic recognition of organism names to enable further organism-driven document screening.
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References
Kano, Y., Baumgartner, W.A., McCrohon, L., et al.: U-Compare: share and compare text mining tools with UIMA. Bioinformatics 25, 1997–1998 (2009), doi:10.1093/bioinformatics/btp289
Fan, W., Wallace, L., Rich, S., Zhang, Z.: Tapping the power of text mining. Commun. ACM 49, 76–82 (2006), doi:10.1145/1151030.1151032
Gemert, J.: Van Text Mining Tools on the Internet An overview. Univ. Amsterdam 25, 1–75 (2000)
Lourenço, A., Carreira, R., Carneiro, S., et al.: @Note: A workbench for biomedical text mining. J. Biomed. Inform. 42, 710–720 (2009), doi:10.1016/j.jbi.2009.04.002
Hucka, M., Finney, A., Sauro, H.: A medium for representation and exchange of biochemical network models (2003)
Lu, Z., Hirschman, L.: Biocuration workflows and text mining: overview of the BioCreative, Workshop Track II. Database (Oxford) 2012:bas043 (2012), doi:10.1093/database/bas043
Feinerer, I., Hornik, K., Meyer, D.: Text Mining Infrastructure in R. J. Stat. Softw. 25, 1–54 (2008), doi:citeulike-article-id:2842334
Fernández-Suárez, X.M., Rigden, D.J., Galperin, M.Y.: The 2014 Nucleic Acids Research Database Issue and an updated NAR online Molecular Biology Database Collection. Nucleic Acids Res. 42, 1–6 (2014), doi:10.1093/nar/gkt1282
Papanicolaou, A., Heckel, D.G.: The GMOD Drupal bioinformatic server framework. Bioinformatics 26, 3119–3124 (2010), doi:10.1093bioinformatics/btq599
Decker, S., Melnik, S., van Harmelen, F., et al.: The Semantic Web: the roles of XML and RDF. IEEE Internet Comput. 4, 63–73 (2000), doi:10.1109/4236.877487
Rebholz-Schuhmann, D., Kafkas, S., Kim, J.-H., et al.: Monitoring named entity recognition: The League Table. J. Biomed Semantics 4, 19 (2013), doi:10.1186/2041-1480-4-19
Rzhetsky, A., Seringhaus, M., Gerstein, M.B.: Getting started in text mining: Part two. PLoS Comput. Biol. 5, e1000411 (2009), doi:10.1371/journal.pcbi.1000411
Gerner, M., Nenadic, G., Bergman, C.M.: LINNAEUS: A species name identification system for biomedical literature. BMC Bioinformatics 11, 85 (2010), doi:10.1186/1471-2105-11-85
Fielding, R.T., Kaiser, G.: The Apache HTTP Server Project. IEEE Internet Comput. (1997), doi:10.1109/4236.612229
Web server | Drupal.org., https://drupal.org/requirements/webserver
Smiley, D., Pugh, E.: Apache Solr 3 Enterprise Search Server, p. 418 (2011)
McCandless, M., Hatcher, E., Gospodnetic, O.: Lucene in Action, Second Edition: Covers Apache Lucene 3.0, p. 475 (2010)
Konchady, M.: Building Search Applications: Lucene, LingPipe, and Gate, p. 448 (2008)
Ferrucci, D., Lally, A.: UIMA: An architectural approach to unstructured information processing in the corporate research environment. Nat. Lang. Eng. (2004)
Rak, R., Rowley, A., Ananiadou, S.: Collaborative Development and Evaluation of Text-processing Workflows in a UIMA-supported Web-based Workbench. In: LREC (2012)
Lin, J.: Is searching full text more effective than searching abstracts? BMC Bioinformatics 10, 46 (2009), doi:10.1186/1471-2105-10-46
Baumgartner, W.A., Cohen, K.B., Hunter, L.: An open-source framework for large-scale, flexible evaluation of biomedical text mining systems. J. Biomed. Discov. Collab. 3(1) (2008), doi:10.1186/1747-5333-3-1
Móra, G.: Concept identification by machine learning aided dictionary-based named entity recognition and rule-based entity normalisation. Second CALBC Work
Kumar, J.: Apache Solr PHP Integration, p. 118 (2013)
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Ferrnandes, J., Lourenço, A. (2014). Bringing Named Entity Recognition on Drupal Content Management System. In: Saez-Rodriguez, J., Rocha, M., Fdez-Riverola, F., De Paz Santana, J. (eds) 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014). Advances in Intelligent Systems and Computing, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-319-07581-5_31
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DOI: https://doi.org/10.1007/978-3-319-07581-5_31
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