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Combining Data-Driven and Semantic Approaches for Text Mining

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Foundations for the Web of Information and Services

Abstract

While the amount of structured data published on the Web keeps growing (fostered in particular by the Linked Open Data initiative), the Web still comprises of mainly unstructured—in particular textual—content and is therefore a Web for human consumption. Thus, an important question is which techniques are most suitable to enable people to effectively access the large body of unstructured information available on the Web, whether it is semantic or not. While the hope is that semantic technologies can be combined with standard Information Retrieval approaches to enable more accurate retrieval, some researchers have argued against this view. They claim that only data-driven or inductive approaches are applicable to tasks requiring the organization of unstructured (mainly textual) data for retrieval purposes. We argue that the dichotomy between data-driven/inductive and semantic approaches is indeed a false one. We further argue that bottom-up or inductive approaches can be successfully combined with top-down or semantic approaches and illustrate this for a number of tasks such as Ontology Learning, Information Retrieval, Information Extraction and Text Mining.

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Notes

  1. 1.

    While it is true that fuzzy and non-monotonic extensions to description logics and OWL have been proposed, we puristicly view OWL as a non-fuzzy and monotonic logic here.

  2. 2.

    http://icame.uib.no/brown/bcm.html.

  3. 3.

    http://www.cis.upenn.edu/~treebank/.

  4. 4.

    http://km.aifb.kit.edu/projects/swsc.

  5. 5.

    TFIDF is a widely used statistical distribution value of terms in documents given a corpus. For a specific term and document, the TFIDF value is the product of the term frequency (TF)—the number of occurrences of the term in the given document—and the inverse document frequency (IDF)—the inverse number of documents in the corpus that contain the term.

  6. 6.

    Further extensions such as those by Bloehdorn and Moschitti [5] combine this idea with more complex so-called tree kernel functions for text structure.

  7. 7.

    http://www.opencalais.com.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Stephan Bloehdorn

  2. Microsoft Search Technology Center Europe, Munich, Germany

    Sebastian Blohm

  3. Semantic Computing Group, Cognitive Interaction Technology Excellence Center (CITEC), University of Bielefeld, Bielefeld, Germany

    Philipp Cimiano

  4. FZI Forschungszentrum Informatik, Karlsruhe, Germany

    Eugenie Giesbrecht

  5. Data Mining and Information Retrieval Group, University of Würzburg, 97074, Würzburg, Germany

    Andreas Hotho

  6. Karlsruher Institut für Technologie, Karlsruhe, Germany

    Uta Lösch & Philipp Sorg

  7. Institute for Enterprise Systems (InES), University of Mannheim, Mannheim, Germany

    Alexander Mädche

  8. ontoprise GmbH, Karlsruhe, Germany

    Eddie Mönch

  9. Institute for Web Science and Technologies, University of Koblenz-Landau, Universitätsstrasse 1, Koblenz, 56070, Germany

    Steffen Staab

  10. KR & KM Research Group, University of Mannheim, Mannheim, Germany

    Johanna Völker

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Correspondence to Stephan Bloehdorn .

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  1. STI Innsbruck, ICT - Technologiepark, University of Innsbruck, Technikerstr. 21a, Innsbruck, 6020, Austria

    Dieter Fensel

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Bloehdorn, S. et al. (2011). Combining Data-Driven and Semantic Approaches for Text Mining. In: Fensel, D. (eds) Foundations for the Web of Information and Services. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19797-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-19797-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-19797-0

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