Data Cleaning publication categorizer

Data quality problems can arise from abbreviations, data entry mistakes, duplicate records, missing fields, and many other sources. These problems proliferate when you integrate multiple data sources in data warehousing, federated databases, and global information systems. A newly discovered class of erroneous data is spurious links, where a real-world entity has multiple links that might not be properly associated with it. The existence of such spurious links often leads to confusion and misrepresentation in the data records representing the entity. Although the data set is well known for its high-quality bibliographic information, collecting and maintaining the data from diverse sources requires enormous effort. Errors, including spurious links, are inevitable. To solve this problem, we use context information to identify spurious links. First, we identify data records that contain potential spurious links. We then determine the set of attributes that constitute each record's context. Experiments with three real-world databases have demonstrated that our approach can accurately identify spurious links. Comparing context information between data records can help solve the data quality problem of spurious links-that is, multiple links between data entries and real-world entities.