Abstract
In this paper we describe a methodology for harvesting information from large distributed repositories (e.g. large Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries) or a user-defined lexicon. Retrieved information is then used to partially annotate documents. Annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotation to annotate more documents that will be used to train more complex IE engines and so on. In this paper we describe the methodology and its implementation in the Armadillo system, compare it with the current state of the art, and describe the details of an implemented application. Finally we draw some conclusions and highlight some challenges and future work.
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Ciravegna, F., Chapman, S., Dingli, A., Wilks, Y. (2004). Learning to Harvest Information for the Semantic Web. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds) The Semantic Web: Research and Applications. ESWS 2004. Lecture Notes in Computer Science, vol 3053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25956-5_22
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DOI: https://doi.org/10.1007/978-3-540-25956-5_22
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