Towards alias detection without string similarity: an active learning based approach.
SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval Portland Oregon USA August, 2012(2012)
摘要
Entity aliases commonly exist and accurately detecting these aliases plays a vital role in various applications. In this paper, we use an active-learning-based method to detect aliases without string similarity. To minimize the cost on pairwise comparison, a subset-based method restricts the alias selection within a small-scale entity set. Within each generated entity set, an active learning based logistic regression classifier is employed to predict whether a candidate is the alias of a given entity. The experimental results on three datasets clearly demonstrate that our proposed approach can effectively detect this kind of entity aliases.
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