  The name disambiguation task partitions a collection of records pertaining to
a given name, such that there is a one-to-one correspondence between the
partitions and a group of people, all sharing that given name. Most existing
solutions for this task are proposed for static data. However, more realistic
scenarios stipulate emergence of records in a streaming fashion where records
may belong to known as well as unknown persons all sharing the same name. This
requires a flexible name disambiguation algorithm that can not only classify
records of known persons represented in the train- ing data by their existing
records but can also identify records of new ambiguous persons with no existing
records included in the initial training dataset. Toward achieving this
objective, in this paper we propose a Bayesian non-exhaustive classification
frame- work for solving online name disambiguation. In particular, we present a
Dirichlet Process Gaussian Mixture Model (DPGMM) as a core engine for online
name disambiguation task. Meanwhile, two online inference algorithms, namely
one-pass Gibbs sampler and Sequential Importance Sampling with Resampling (also
known as particle filtering), are proposed to simultaneously perform online
classification and new class discovery. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups.Our experimental results
demonstrate that the proposed method is significantly better than existing
methods for performing online name disambiguation task. We also propose an
interactive version of our online name disambiguation method designed to
leverage user feedback to improve prediction accuracy.
