Advanced search engine

We offer both:  

1- The expertise for customized search engine integration according to various search criteria and platforms, that address the following issues of:

  • interoperability
  • scalability for large volumes of data and expanded systems
  • indexing data streams arising from documents, social media networks, audio or video sources

2- The expertise for extending search engine capabilities through semantic exploration

Computer-assisted information searching has become commonplace, as anyone with search engine availability can easily access documents in response to user-supplied subjects of interest. Nevertheless, polysemy (a word capable of having multiple meanings, such as “draft” in the sense of “preliminary form of writing” or “draft” in the sense of “gust of wind”) still represents a major problem to information searching, since it often leads to irrelevant results.

Information searching can also be performed in a question-answer manner, where the user is interested in a precise answer (e.g.: In what year did Mozart die?), in contrast to a list of documents on the subject (as in a Wikipedia page on Mozart). This avenue encompasses a vast area of research that includes natural language processing (***see other section) and possibly, in addition, the use of semantic Web (***see other section) for encoding the information in a more structured fashion.

Teams

Publications

Recent news

  • Jan. 29 : CRIM's Journée Techno - 5G
    15/01/2019

    5G: business model transformation, socioeconomic impacts and new technological landscape. Get the tools you need to make the most of these new opportunities!

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Upcoming event

  • ICCSP 2019
    21 January 2019 0:00
    Kuala Lumpur, Malaysia
    CRIM will present an article at the 3rd International Conference on Cryptography, Security and Privacy, which will take place January 19th to 21th, 2019 in Kuala Lumpur, Malaysia.
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Recent Publications

  • Towards Automatic Feature Extraction for Activity Recognition from Wearable Sensors: A Deep Learning Approach

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  • Checking Sequence Generation for Symbolic Input/Output FSMs by Constraint Solving

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