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The H \(\imath\) L ε X System for Semantic Information Extraction

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Transactions on Large-Scale Data- and Knowledge-Centered Systems V

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 7100))

Abstract

The explosive growth and popularity of the Web has resulted in a huge amount of digital information sources on the Internet. Unfortunately, such sources only manage data, rather than the knowledge they carry. Recognizing, extracting, and structuring relevant information according to their semantics is a crucial task. Several approaches in the field of Information Extraction (IE) have been proposed to support the translation of semi-structured/unstructured documents into structured data or knowledge. Most of them have a high precision but, since they are mainly syntactic, they often have a low recall, are dependent on the document format, and ignore the semantics of information they extract. In this paper, we describe a new approach for semantic information extraction that could represent the basis for automatically extracting highly structured data from unstructured web sources without any undesirable trade-off between precision and recall. In short, the approach (i) is ontology driven, (ii) is based on a unified representation of documents, (iii) integrates existing IE techniques, (iv) implements semantic regular expressions, (v) has been implemented through Answer Set Programming, (vi) is employed in real-world applications, and (vii) is having a positive feedback from business customers.

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Authors and Affiliations

  1. Department of Mathematics, University of Calabria, Italy

    Marco Manna, Mario Alviano & Nicola Leone

  2. DEIS, University of Calabria, Italy

    Ermelinda Oro

  3. ICAR-CNR, University of Calabria, Italy

    Massimo Ruffolo

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  1. Marco Manna
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  2. Ermelinda Oro
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  3. Massimo Ruffolo
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  4. Mario Alviano
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  5. Nicola Leone
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Abdelkader Hameurlain Josef Küng Roland Wagner

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Manna, M., Oro, E., Ruffolo, M., Alviano, M., Leone, N. (2012). The H \(\imath\) L ε X System for Semantic Information Extraction. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems V. Lecture Notes in Computer Science, vol 7100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28148-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-28148-8_5

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