The Semantic Software Lab was founded in 2008 by René Witte at Concordia University in Montréal, Québec, Canada. Our lab focuses on research and applications of Semantic Computing, Text Mining, Linked Data, Natural Language Processing (NLP), Information Extraction, Intelligent Information Systems, and related technologies. We are committed to providing free, open source software and open research data to the community.
This website provides information about the lab's research activities and our published tools and resources. It also provide information for students interested in course or research work, as well as career opportunities for researchers. It also aims to serve as a community portal for selected topics and events in the area of semantic systems within (north-east) America in general and Montréal in particular. You can also follow us on Twitter @SemSoft, on LinkedIn or connect with us on Google+.
Save the dates! SMWCon Spring 2014 will be held at Concordia University this year in the vibrant and culturally-fascinating city of Montréal, from May 21-23. We are inviting you to submit your contributions to assemble the conference program. Registration is now open, with early bird rates applicable until April 30th.
This twice-yearly conference brings together researchers, users, developers and enthusiasts of Semantic MediaWiki and related projects, such as Wikidata. Semantic MediaWiki is a family of extensions to the open-source wiki software MediaWiki (best known for powering Wikipedia) that allow a wiki to store structured data in addition to textual content, thereby, turning a wiki into a flexible, collaborative knowledge repository.
The complete volume contains abstracts for the two invited talks, two full papers, two short papers, five early career track papers, and four systems papers. Individual papers can be downloaded from the CEUR-WS.org site, where you can also find a BibTeX file with all references.
The Fourth Canadian Semantic Web Symposium will be held at Concordia University, Montreal, Quebec, on July 10, 2013. CSWS 2013 aims to bring together Canadian and international researchers in semantic technologies and knowledge management to discuss issues related to the Semantic Web.
The event is part of the Semantic Trilogy 2013 featuring:
- International Conference on Biomedical Ontologies (ICBO 2013)
- Canadian Semantic Web Symposium (CSWS 2013)
- Data Integration in the Life Sciences (DILS 2013)
For more information, please refer to:
Twitter: https://twitter.com/CSWS2013 (@CSWS2013)
Our research on Natural Language Processing (NLP) for wiki systems has been featured in Concordia University's NOW newsletter. Explaining the technology and its applications to a general audience, it quickly become one of the most read and shared articles of the week.
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.
Last week I introduced the very first release of the OpenTrace tool at this year's WCRE conference in the lovely city of Kingston, Ontario. This 4-day event was the 19th conference on reverse engineering and hosted talks from research and industry on the state-of-the art techniques for program comprehension of software systems.
WikiSym is an international symposium on wikis and open collaborative techniques, mainly focused on wiki research and practice. Back in 2007, we coined the term "self-aware" wiki systems in our paper submitted to the WikiSym '07, fostering the idea that the integration of Natural Language Processing (NLP) techniques within wiki systems allows the wiki systems to read, understand, transform, and even write their own content, as well as supporting their users in information analysis and content development. Now after a few years, we have realized this idea through an open service-oriented architecture.
As part of the Semantic Assistants project, we developed the idea of a "self-aware" wiki system that can develop and organize its own content using state-of-art techniques from the Natural Language Processing (NLP) and Semantic Computing domains. This is achieved with our open source Wiki-NLP integration, a Semantic Assistants add-on that allows to incorporate NLP services into the MediaWiki environment, thereby enabling wiki users to benefit from modern text mining techniques.
Here, we want to exhibit how a seamless integration of NLP techniques into wiki systems helps to increase their acceptability and usability as a powerful, yet easy-to-use collaborative platform. We hope this will help you to identify new human-computer interaction patterns for other scenarios, allowing you to make the best possible use of this new technology.
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: "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.