Towards Realistic Known-item Topics for the ClueWeb

Abstract

Known-item finding is the task of re-finding and re-accessing an item previously seen. Typical examples of known items include accessed Web sites, received emails, or documents on one's personal desktop. Current research on known-item finding heavily relies on corpora of known-item queries and the respective known items. However, many existing corpora are proprietary and not available to the public (in particular those derived from Web query logs), a fact which does not allow for repeatable research. The existing publicly available corpora either contain automatically generated queries or queries that were manually generated while seeing the known item itself. Hence, we consider these public corpora to be rather artificial in nature.

In this paper, we propose a methodology to create a known-item topic set that is much more realistic and that is built on top of a large-scale public test corpus. From know-item questions posted on the popular Yahoo! Answers platform we extract queries for known-items in a crowdsourcing setup. Since we ensure that all the known-items correspond to Web pages in the publicly available ClueWeb09 corpus (a large static Web crawl), we provide an environment for repeatable realistic Web-scale known-item searches.