Collaborative indexing over networked information resources by distributed agents

Various information tools have appeared to help users locate and retrieve information resources over the Internet. The amount of information resources is increasing dramatically, and it is necessary to use indexing services to deal with the scalability problems. Existing indexing systems collect index data periodically from information servers only. Thus the users of indexing systems lack a common framework where they can share their heuristic information on available resources. This paper introduces collaborative indexing for gathering index data from users. The indexing system is seamlessly integrated with existing information tools. In our model, a group of users with a common view shares feedback of resource discovery. One user's feedback about searches is used by future users to help locate relevant resources. We implemented a prototype system where agents share index data by weighted association. Each agent defines its domain by the community and topic the agent serves. Agents gather users' feedback and use it to change weight values of associations. By doing this, users are relieved from a large volume of irrelevant resources in a global search space, and get more closely related matches to the search request.