Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser
Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identiﬁers or when those identiﬁers may not be perceived perfectly. In such cases, two ob- servations may or may not correspond to the same object. In this paper, we consider the problem in the context of citation matching—the prob- lem of deciding which citations correspond to the same publication. Our approach is based on the use of a relational probability model to deﬁne a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar. Identity uncertainty is handled by extending standard models to incorporate probabilities over the possible mappings between terms in the language and objects in the domain. Inference is based on Markov chain Monte Carlo, augmented with speciﬁc methods for generating efﬁcient proposals when the domain contains many objects. Results on several citation data sets show that the method outperforms current algorithms for citation matching. The declarative, relational nature of the model also means that our algorithm can determine object characteristics such as author names by combining multiple citations of multiple papers.