ANITA: a narrative interpretation of taxonomies for their adaptation to text collections

ABSTRACT

Taxonomies embody formalized knowledge and define aggregations between concepts/categories in a given domain, facilitating the organization of the data and making the contents easily accessible to the users. Since taxonomies have significant roles in the data annotation, search and navigation, they are often carefully engineered. However, especially in very dynamic content, they do not necessarily reflect the content knowledge. Thus, in this paper, we propose A Narrative Interpretation of Taxonomies for their Adaptation (ANITA) for re-structuring existing taxonomies to varying application contexts and we evaluate the proposed scheme by user studies that show that the proposed algorithm is able to adapt the taxonomy in a new compact and understandable structure from a human point of view.

References

1. C. Brewster, F. Ciravegna, and Y. Wilks. User-centred ontology learning for knowledge management. In NLDB '02, pages 203--207. Springer-Verlag, 2002.
2. P. Buitelaar, P. Cimiano, and B. Magnini, editors. Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press, 2005.
3. M. Cataldi, C. Schifanella, K. S. Candan, M. L. Sapino, and L. Di Caro. Cosena: a context-based search and navigation system. In MEDES '09, pages 218--225. ACM, 2009.
4. P. Cimiano, A. Hotho, and S. Staab. Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research, 24:305--339, 2005.
5. J. W. Kim and K. S. Candan. Cp/cv: concept similarity mining without frequency information from domain describing taxonomies. In CIKM '06, 2006.
6. D. J. Lawrie and W. B. Croft. Generating hierarchical summaries for web searches. In SIGIR '03, pages 457--458, 2003.
7. C. Muller, I. Gurevych, and M. Muhlhauser. Integrating semantic knowledge into text similarity and information retrieval. In ICSC '07, pages 257--264, 2007.
8. S. P. Ponzetto and M. Strube. Deriving a large-scale taxonomy from wikipedia. In AAAI, pages 1440--1445, 2007.
9. M. Sanderson. Word sense disambiguation and information retrieval. In SIGIR '94, pages 142--151. Springer-Verlag New York, Inc., 1994.
10. M. Sanderson and B. Croft. Deriving concept hierarchies from text. In SIGIR'99, pages 206--213, 1999.
11. O. Zamir and O. Etzioni. Web document clustering: a feasibility demonstration. In SIGIR '98, pages 46--54, 1998.
12. Y. Zhao and G. Karypis. Evaluation of hierarchical clustering algorithms for document datasets. In Data Mining and Knowledge Discovery, pages 515--524. ACM Press, 2002.

Index Terms

• Applied computing

  • Document management and text processing

    • Document preparation

      • Multi / mixed media creation

• Human-centered computing

  • Human computer interaction (HCI)

    • Interaction paradigms

      • Hypertext / hypermedia

• Information systems

  • Information retrieval