Abstract
Class attributes are important resources in question answering, knowledge base building and semantic retrieval. In this paper, we propose an approach extracting class attributes from online encyclopedias. This approach combines the tolerance rough set model and semantic relatedness computing. Firstly, the implementation of the tolerance rough set model ensures a high precision of top-\(k\) extracted class attributes, and then the semantic relatedness computing improves the coverage of top-\(k\) extracted class attributes in order to achieve higher accuracy. Finally experiments on the extracted class attributes show the effectiveness of our approach.
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Guo, H., Chen, Q., Sun, C. (2014). Extraction of Class Attributes from Online Encyclopedias. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_30
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DOI: https://doi.org/10.1007/978-3-662-45652-1_30
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