Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
Search engine driven author disambiguation
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Generative models for name disambiguation
Proceedings of the 16th international conference on World Wide Web
Name Disambiguation Using Atomic Clusters
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
GHOST: an effective graph-based framework for name distinction
Proceedings of the 17th ACM conference on Information and knowledge management
Disambiguating authors in academic publications using random forests
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Efficient name disambiguation for large-scale databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
What's in a name?: an unsupervised approach to link users across communities
Proceedings of the sixth ACM international conference on Web search and data mining
Vietnamese author name disambiguation for integrating publications from heterogeneous sources
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
A semi-supervised approach for author disambiguation in KDD CUP 2013
Proceedings of the 2013 KDD Cup 2013 Workshop
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Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically.