Abstract
We address the problem of learning automatically to map heterogeneous semi-structured documents onto a mediated target XML schema. We adopt a machine learning approach where the mapping between input and target documents is learned from a training corpus of documents. We first introduce a general stochastic model of semi structured documents generation and transformation. This model relies on the concept of meta-document which is a latent variable providing a link between input and target documents. It allows us to learn the correspondences when the input documents are expressed in a large variety of schemas. We then detail an instance of the general model for the particular task of HTML to XML conversion. This instance is tested on three different corpora using two different inference methods: a dynamic programming method and an approximate LaSO-based method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chung, C.Y., Gertz, M., Sundaresan, N.: Reverse engineering for web data: From visual to semantic structures. In: ICDE (2002)
Zhang, S., Dyreson, C.: Polymorphic xml restructuring. In: IIWeb 2006: Workshop on Information Integration on the Web (2006)
Wisniewski, G., Gallinari, P.: From layout to semantic: a reranking model for mapping web documents to mediated xml representations. In: Proceedings of the 8th RIAO International Conference on Large-Scale Semantic Access to Content (2007)
Chidlovskii, B., Fuselier, J.: Supervised learning for the legacy document conversion. In: DocEng 2004. Proceedings of the 2004 ACM symposium on Document engineering, New York, NY, USA, pp. 220–228. ACM Press, New York (2004)
Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AI Magazine, Special Issue on Semantic Integration (2005)
Denoyer, L., Gallinari, P.: Bayesian network model for semi-structured document classification. Information Processing and Management (2004)
Denoyer, L.: Xml document mining challenge. Technical report, LIP6 (2005)
Berger, A.L., Pietra, V.J.D., Pietra, S.A.D.: A maximum entropy approach to natural language processing. Comput. Linguist. 22, 39–71 (1996)
Malouf, R.: A comparison of algorithms for maximum entropy parameter estimation. In: COLING-02. proceeding of the 6th conference on Natural language learning, Morristown, NJ, USA, pp. 1–7. Association for Computational Linguistics (2002)
Chidlovskii, B., Fuselier, J.: A Probabilistic Learning Method for XML Annotation of Documents. In: IJCAI (2005)
Denoyer, L., Wisniewski, G., Gallinari, P.: Document structure matching for heterogeneous corpora. In: Workshop SIGIR 2004. Workshop on IR and XML, Sheffield (2004)
Daumé III, H., Marcu, D.: Learning as search optimization: approximate large margin methods for structured prediction. In: ICML 2005. Proceedings of the 22nd international conference on Machine learning, New York, NY, USA, pp. 169–176. ACM Press, New York (2005)
Fuhr, N., Govert, N., Kazai, G., Lalmas, M.: Inex: Initiative for the evaluation of xml retrieval. In: SIGIR 2002 Workshop on XML and Information Retrieval (2002)
Doan, A., Domingos, P., Halevy, A.: Learning to match the schemas of data sources: A multistrategy approach. Mach. Learn. 50, 279–301 (2003)
McCallum, A.: Information extraction: distilling structured data from unstructured text. Queue 3, 48–57 (2005)
Young-Lai, M., Tompa, F.W.: Stochastic grammatical inference of text database structure. Machine Learning (2000)
Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wisniewski, G., Maes, F., Denoyer, L., Gallinari, P. (2007). Probabilistic Model for Structured Document Mapping. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-73499-4_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73498-7
Online ISBN: 978-3-540-73499-4
eBook Packages: Computer ScienceComputer Science (R0)