  Automatically extracted metadata from scholarly documents in PDF formats is
usually noisy and heterogeneous, often containing incomplete fields and
erroneous values. One common way of cleaning metadata is to use a bibliographic
reference dataset. The challenge is to match records between corpora with high
precision. The existing solution which is based on information retrieval and
string similarity on titles works well only if the titles are cleaned. We
introduce a system designed to match scholarly document entities with noisy
metadata against a reference dataset. The blocking function uses the classic
BM25 algorithm to find the matching candidates from the reference data that has
been indexed by ElasticSearch. The core components use supervised methods which
combine features extracted from all available metadata fields. The system also
leverages available citation information to match entities. The combination of
metadata and citation achieves high accuracy that significantly outperforms the
baseline method on the same test dataset. We apply this system to match the
database of CiteSeerX against Web of Science, PubMed, and DBLP. This method
will be deployed in the CiteSeerX system to clean metadata and link records to
other scholarly big datasets.

Digital Libraries;Information Retrieval