Abstract
Information analysis, scientific discovery, web navigation and many other information related activities rely on the collection, analysis, and integration of unstructured, disparate data. In spite of significant research efforts, automatic methods for information integration are still vastly inferior to human capabilities. Tasks which are simple for humans, such as recognizing the underlying similarities between superficially different objects or sorting out the semantics of ambiguous statements, continue to present significant technical challenges for automated processes.
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Wong, P.C., Foote, H., Leung, R., Adams, D., Thomas, J.: Data Signatures and Visualization of Very Large Datasets. IEEE Computer Graphics and Applications 20(2) (2000)
Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11) (1975)
Lewis, D.D.: Reuters-21578 Test Collection, Distribution 1.0 (January 1997), http://www.daviddlewis.com/resources/testcollections/reuters21578
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© 2004 Springer-Verlag Berlin Heidelberg
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Thomson, J., Cowell, A., Paulson, P., Butner, S., Whiting, M. (2004). Knowledge Signatures for Information Integration. In: Meersman, R., Tari, Z., Corsaro, A. (eds) On the Move to Meaningful Internet Systems 2004: OTM 2004 Workshops. OTM 2004. Lecture Notes in Computer Science, vol 3292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30470-8_12
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DOI: https://doi.org/10.1007/978-3-540-30470-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23664-1
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