Exploiting background knowledge to build reference sets for information extraction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Asymmetric information distances for automated taxonomy construction
Knowledge and Information Systems
Constructing reference sets from unstructured, ungrammatical text
Journal of Artificial Intelligence Research
Materializing multi-relational databases from the web using taxonomic queries
Proceedings of the fourth ACM international conference on Web search and data mining
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An automatic taxonomy extraction algorithm is proposed. Given a set of terms or terminology related to a subject domain, the proposed approach uses Google page count to estimate the dependency links between the terms. A taxonomic link is an asymmetric relation between two concepts. In order to extract these directed links, neither mutual information nor normalized Google distance can be employed. Using the new measure of information theoretic inclusion index, term dependency matrix, which represents the pair-wise dependencies, is obtained. Next, using a proposed algorithm, the dependency matrix is converted into an adjacency matrix, representing the taxonomy tree. In order to evaluate the performance of the proposed approach, it is applied to several domains for taxonomy extraction.