  We present a machine learning based information retrieval system for
astronomical observatories that tries to address user defined queries related
to an instrument. In the modern instrumentation scenario where heterogeneous
systems and talents are simultaneously at work, the ability to supply with the
right information helps speeding up the detector maintenance operations.
Enhancing the detector uptime leads to increased coincidence observation and
improves the likelihood for the detection of astrophysical signals. Besides,
such efforts will efficiently disseminate technical knowledge to a wider
audience and will help the ongoing efforts to build upcoming detectors like the
LIGO-India etc even at the design phase to foresee possible challenges. The
proposed method analyses existing documented efforts at the site to
intelligently group together related information to a query and to present it
on-line to the user. The user in response can further go into interesting links
and find already developed solutions or probable ways to address the present
situation optimally. A web application that incorporates the above idea has
been implemented and tested for LIGO Livingston, LIGO Hanford and Virgo
observatories.
