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Semantic Software Lab
Concordia University
Montréal, Canada

Text Mining

Natural Language Processing for MediaWiki: First major release of the Semantic Assistants Wiki-NLP Integration

We are happy to announce the first major release of our Semantic Assistants Wiki-NLP integration. This is the first comprehensive open source solution for bringing Natural Language Processing (NLP) to wiki users, in particular for wikis based on the well-known MediaWiki engine and its Semantic MediaWiki (SMW) extension. It allows you to bring novel text mining assistants to wiki users, e.g., for automatically structuring wiki pages, answering questions in natural language, quality assurance, entity detection, summarization, among others, which are deployed in the General Architecture for Text Engineering (GATE) and brokered as web services through the Semantic Assistants server.

Supporting Wiki Users with Natural Language Processing

Sateli, B., and R. Witte, "Supporting Wiki Users with Natural Language Processing", The 8th International Symposium on Wikis and Open Collaboration (WikiSym 2012), Linz, Austria : ACM, 08/2012.

First Release of the Open Mutation Miner (OMM) System

We are happy to announce the first major public release of our protein mutation impact analysis system, Open Mutation Miner (OMM), together with a new open access publication: 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.

OMM is the first comprehensive, fully open source system for extracting and analysing mutation-related information from full-text research papers. Novel features not available in other systems include: the detection of various forms of mutation mentions, in particular mutation series, full mutation impact analysis, including linking impacts with the causative mutation and the affected protein properties, such as molecular functions, kinetic constants, kinetic values, units of measurements, and physical quantities. OMM provides output options in various formats, including populating an OWL ontology, Web service access, structured queries, and interactive use embedded in desktop clients. OMM is robust and scalable: we processed the entire PubMed Open Access Subset (nearly half a million full-text papers) on a standard desktop PC, and larger document sets can be easily processed and indexed on appropriate hardware.

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