Data Cleaning publication categorizer

Heterogeneous and dirty data is abundant. It is stored under different, often opaque schemata, it rep-
resents identical real-world objects multiple times, causing duplicates, and it has missing values and
conflicting values. Without suitable techniques for integrating and fusing such data, the data quality of
an integrated system remains low. We present a suite of methods, combined in a single tool, that allows
ad-hoc, declarative fusion of such data by employing schema matching, duplicate detection and data
fusion.
Guided by a SQL-like query against one or more tables, we proceed in three fully automated steps:
First, instance-based schema matching bridges schematic heterogeneity of the tables by aligning cor-
responding attributes. Next, duplicate detection techniques find multiple representations of identical
real-world objects. Finally, data fusion and conflict resolution merges each duplicate into a single,
consistent, and clean representation.