Qian Chen
ABSTRACT
Entity type matching has many real world applications, especially in entity clustering, de-duplication and efficient query processing. Current methods to extract entities from text usually disregard regularities in the order of entities appearing in the text. In this paper, we focus on enumerative descriptions which enlist entity names in a certain hidden order, often occurring in web documents as listings and tables. We propose an algorithm to discover entity types from enumerative descriptions, where a type hierarchy is known but enumerating orders are hidden and heterogeneous, and partial entity-type mappings are given as seed instances. Our algorithm is iterative: We extract skeletons from syntactic patterns, then train a hidden Markov model to find an optimum enumerating order from seed instances and skeletons, to find a best-fit entity-type assignment.
- L. E. Baum. An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities, 3, 1972.Google Scholar
- X. L. Dong, A. Halevy, and C. Yu. Data integration with uncertainty. VLDBJ, 18(2), 2009 Google ScholarDigital Library
- H. Huang, C. Liu. Query evaluation on probabilistic RDF databases. In WISE, 2009. Google ScholarDigital Library
- X. Lian and L. Chen. Efficient query answering in probabilistic RDF graphs. SIGMOD'11, 2011. Google ScholarDigital Library
- T. Y. Li, P. Chubak, L.V.S. Lakshmanan. Efficient extraction of ontologies from domain specific text corpora. CIKM'12, 2012. Google ScholarDigital Library
- Y. Ma, T. Tra, V. Bicer. TYPifier: inferring the type semantics of structured data. ICDE '13, 2013. Google ScholarDigital Library
- A.D. Sarma, A.Jain, D. Srivastava. I4E: interactive investigation of iterative information extraction. SIGMOD'10, 2010. Google ScholarDigital Library
- A. Viterbi, "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm," IEEE Trans. Inform. Theory,IT-13:260--269, Apr 1967. Google ScholarDigital Library
- X. Zhang, G. Cheng, Y. Z. Qu. Ontology summarization based on RDF sentence graph. In IW3C2, 2007. Google ScholarDigital Library
- P. Zhao and J. Han. On graph query optimization in large networks. PVLDB, 3(1), 2010. Google ScholarDigital Library
Index Terms
- Iterative algorithm for inferring entity types from enumerative descriptions
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