About
What was it?
OU Expert Search (OUES) was a document content-based indexing and search engine. It had a Google-like search interface, but instead of finding documents, OUES found people. Given a search query topic such as “Java programming,” OUES generated a ranked list of experts from current OU employees who had expertise in the search query topic. Documents supporting expertise were presented, and the expert list was integrated with the OU staff directory.
Who worked on it?
Many information retrieval groups around the world were working on this at the time. In KMi, Dr. Jianhan Zhu, Dr. Dawei Song, Prof. Marc Eisenstadt, Prof. Enrico Motta, and Prof. Stefan Ruger worked on OUES. In the TREC (Text REtrieval Conference) 2006 expert search task, KMi was ranked among the top research groups internationally across all major Information Retrieval measures.
How did it work?
OUES indexed documents on the OU intranet periodically for expert search. Given a search query, OUES estimated the relevance of a person’s expertise on the query topic based on the relevance of documents to the topic and the relevance of the person to the topic within those documents.
Why was it significant?
Expert finding was very important in a distributed environment like the OU, where students and media liaisons needed to find experts remotely. However, constructing a complete expertise database for OU was challenging, and such a database quickly became outdated. On the other hand, OUES provided speedy, up-to-date, and accurate expertise information at zero or very low cost.
What were the downsides?
Some people preferred not to be found, but a privacy filter was added to ensure that this information was only accessible on the OU Intranet.
Where was it going?
Work was being done on a smoothly integrated search system consisting of OUES and the Ultraseek powered OU intranet search. Efforts were also made to integrate OUES with the OU expertise database and domain knowledge.
What were the implications for teaching and learning?
OUES supported teaching and learning by providing an advanced expertise search service. For example:
(i) Students could use OUES to find subject experts (who had granted ‘visibility’).
(ii) OU course designers could use OUES to assemble a group of experts to work on a course.
(iii) On-site experts could be identified to respond to subject-specific inquiries.
Team
Dr. Jianhan Zhu
Prof. Marc Eisenstadt
