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

Name Disambiguation in Anonymized Graphs using Network Embedding