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Formal and relational concept analysis for fuzzy-based automatic semantic annotation

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Abstract

Semantic annotation is at the core of Semantic Web technology: it bridges the gap between legacy non-semantic web resource descriptions and their elicited, formally specified conceptualization, converting syntactic structures into knowledge structures, i.e., ontologies. Most existing approaches and tools are designed to deal with manual or semi-/automatic semantic annotation that exploits available ontologies through the pattern-based discovery of concepts. This work aims to generate the automatic semantic annotation of web resources, without any prefixed ontological support. The novelty of our approach is that, starting from web resources, content with a high-level of abstraction is obtained: concepts, connections between concepts, and instance-population are identified and arranged into an ex-novo ontology. The framework is designed to process resources from different sources (textual information, images, etc.) and generate an ontology-based annotation. A data-driven analysis reveals the data and their intrinsic relationships (in the form of triples) extracted from the resource content. On the basis of the discovered semantics, corresponding concepts and properties are modeled, allowing an ad hoc ontology to be built through an OWL-based coding annotation. The benefit of this approach is the generation of knowledge structured in a quite automatic way (i.e., the human support is restricted to the configuration of some parameters). The approach exploits a fuzzy extension of the mathematical modeling of Formal Concept Analysis and Relational Concept Analysis to generate the ontological structure of data resources.

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Notes

  1. http://annotation.semanticweb.org.

  2. http://www.acemedia.org/aceMedia/results/software/m-ontomat-annotizer.html.

  3. http://www.ontotext.com/kim.

  4. http://gate.ac.uk/.

  5. AeroSWARM project page (http://ubot.lockheedmartin.com/ubot/hotdaml/aeroswarm.html accessed on 2 August 2004).

  6. http://www.opencalais.com.

  7. The fuzzy intersection and union are calculated using t-norm and t-conorm, respectively. The most commonly adopted t-norm is the minimum, while the most common t-conorm is the maximum. That is, given two fuzzy sets A and B with membership functions μ A (x) and μ B (x), μ A∩B (x)=min(μ A (x),μ B (x)) and μ AUB (x)=max(μ A (x),μ B (x)).

  8. Statistical parsers associate grammar rules according to a probability function. We have chosen the Stanford Parser: http://nlp.stanford.edu/software/lex-parser.shtml.

  9. http://Wordnet.princeton.edu/.

  10. http://www.Alchemyapi.com/api/demo.html.

  11. http://edition.cnn.com/services/rss/.

  12. http://rss.cnn.com/rss/edition.rss.

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Acknowledgements

The authors would like to thank the reviewers for their valuable comments to improve the quality of the paper. This research is partially supported by the EC under the Project ARISTOTELE “Personalized Learning & Collaborative Working Environments Fostering Social Creativity and Innovations Inside the Organisations”, VII FP, Theme ICT-2009.4.2 (Technology-Enhanced Learning), Grant Agreement No. 257886.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Salerno, Via Ponte don Melillo, 84084, Fisciano, SA, Italy

    C. De Maio, G. Fenza, M. Gallo, V. Loia & S. Senatore

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  1. C. De Maio
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  2. G. Fenza
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  3. M. Gallo
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  4. V. Loia
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  5. S. Senatore
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Correspondence to S. Senatore.

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De Maio, C., Fenza, G., Gallo, M. et al. Formal and relational concept analysis for fuzzy-based automatic semantic annotation. Appl Intell 40, 154–177 (2014). https://doi.org/10.1007/s10489-013-0451-7

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