Linked Data-based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario
published: July 4, 2012, recorded: May 2012, views: 233
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. In this paper, we propose two Linked Data-based concept recommendation methods for topic discovery. The first one – called hyProximity - exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method – called Random Indexing - to the linked data. We compare the performance of the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !