Reference reconciliation in complex information spaces

Reference reconciliation is the problem of identifying when di®erent references (i.e., sets of attribute values) in a dataset correspond to the same real-world entity. Most previous lit-erature assumed references to a single class that had a fair number of attributes (e.g., research publications). We con-sider complex information spaces: our references belong to multiple related classes and each reference may have very few attribute values. A prime example of such a space is Per-sonal Information Management, where the goal is to provide a coherent view of all the information on one's desktop. Our reconciliation algorithm has three principal features. First, we exploit the associations between references to de-sign new methods for reference comparison. Second, we propagate information between reconciliation decisions to accumulate positive and negative evidences. Third, we grad-ually enrich references by merging attribute values. Our experiments show that (1) we considerably improve preci-sion and recall over standard methods on a diverse set of personal information datasets, and (2) there are advantages to using our algorithm even on a standard citation dataset benchmark. 1