  In real-world, our DNA is unique but many people share names. This phenomenon
often causes erroneous aggregation of documents of multiple persons who are
namesake of one another. Such mistakes deteriorate the performance of document
retrieval, web search, and more seriously, cause improper attribution of credit
or blame in digital forensic. To resolve this issue, the name disambiguation
task is designed which aims to partition the documents associated with a name
reference such that each partition contains documents pertaining to a unique
real-life person. Existing solutions to this task substantially rely on feature
engineering, such as biographical feature extraction, or construction of
auxiliary features from Wikipedia. However, for many scenarios, such features
may be costly to obtain or unavailable due to the risk of privacy violation. In
this work, we propose a novel name disambiguation method. Our proposed method
is non-intrusive of privacy because instead of using attributes pertaining to a
real-life person, our method leverages only relational data in the form of
anonymized graphs. In the methodological aspect, the proposed method uses a
novel representation learning model to embed each document in a low dimensional
vector space where name disambiguation can be solved by a hierarchical
agglomerative clustering algorithm. Our experimental results demonstrate that
the proposed method is significantly better than the existing name
disambiguation methods working in a similar setting.
