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Semantic Software Lab
Concordia University
Montréal, Canada

Text Mining

OMM Query

OMM Query is our online search interface for an index for full-text research papers from the PMC Open Access Corpus (nearly half a million documents) that have been mined for mutation information with Open Mutation Miner (OMM) and OrganismTagger. It can be accessed using the Mímir query language, combining entity annotations with their features with plain text (see below for some examples).

Note that you can index your own set of documents through OMM and install a local query server, if you want to mine a different set of documents for mutation impact information: all software used in this process is freely available under open source licenses. Besides the web interface, it is also possible to query the Mímir server through a RESTful API.

Semantic Computing Course

The Semantic Computing course (SOEN 6211) is offered at Concordia University, providing graduate students with a unique opportunity to study research and development of novel semantic software systems. The course is taught by Prof. René Witte and supported by team members from the Semantic Software Lab. Students from other universities in Québec can register for this course through CREPUQ.

This course provide an introduction to selected topics from Semantic Computing, including text mining, tagging and tag analysis, recommender systems, RDF and linked data, semantic desktops and semantic wikis.

Open Mutation Miner (OMM)

Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually reading through the rich and fast growing repository of biomedical literature is expensive and time-consuming. Text mining methods can help by automatically analysing the literature and extracting mutation-related knowledge into a structured represenation.

Our Open Mutation Miner (OMM) system provides a number of advanced text mining components for mutation mining from full-text research papers, including the detection of various forms of mutation mentions, protein properties, organisms, impact mentions, and the relations between them. OMM provides output options in various formats, including populating an OWL ontology, Web service access, structured queries, and interactive use embedded in desktop clients. It is described and evaluated in detail in our paper, Naderi, N., and R. Witte, "Automated extraction and semantic analysis of mutation impacts from the biomedical literature", BMC Genomics, vol. 13, no. Suppl 4, pp. S10, 06/2012.

Semantic Text Mining for Lignocellulose Research

Ananiadou, S., D. Lee, S. Navathe, and M. Song (Eds.), Meurs, M. - J., C. Murphy, I. Morgenstern, N. Naderi, G. Butler, J. Powlowski, A. Tsang, and R. Witte, "Semantic Text Mining for Lignocellulose Research", The ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics in conjunction with CIKM, Glasgow, UK : ACM New York, NY, USA ©2011, 10/2011.

OwlExporter v3.0 Released

We just released a new version of the OwlExporter ontology population plugin for GATE. The OwlExporter PR can be added to any NLP pipeline to facilitate the population of an existing OWL ontology with entities detected in the corpus. It supports the population of separate NLP- and domain-ontologies and has support for some advanced features, like the export of coreference chains.

In this release, we included a pre-compiled binary and a complete example pipeline that transforms GATE's ANNIE information extraction example into an ontology population system. We also completely revamped the documentation and website to make it more accessible to ontology population novices.

The OrganismTagger System

Our open source 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. Our pipeline provides the flexibility of annotating the species of particular interest to bio-engineers on different corpora, by optionally including detection of common names, acronyms, and strains. The OrganismTagger performance has been evaluated on two manually annotated corpora, OT and Linneaus. On the OT corpus, the OrganismTagger achieves a precision and recall of 95% and 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linneaus-100, the results show a precision and recall of 99% and 97% and grounding with an accuracy of 97.4%. It is described in detail in our publication, Naderi, N., T. Kappler, C. J. O. Baker, and R. Witte, "OrganismTagger: Detection, normalization, and grounding of organism entities in biomedical documents", Bioinformatics, vol. 27, no. 19 Oxford University Press, pp. 2721--2729, August 9, 2011.

Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten

Witte, R., and J. Mülle (Eds.), Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten, Universität Karlsruhe, Fakultät für Informatik, Institut für Programmstrukturen und Datenorganisation (IPD), 2006.
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