IMPRECISE EMPIRICAL ONTOLOGY REFINEMENT - Application to Taxonomy Acquisition

Vít Novácěk

Abstract

The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty incorporation into ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning) – a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called ANUIC (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of ANUIC can improve the results of initial knowledge extraction methods.

References

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  19. 1: process the resources by the pattern-based method and produce a set of ontologies Sp
  20. 2: merge the ontologies in Sp into one ontology R
  21. 3: process the resources by the clustering-based method (Alg. 1 and Alg. 2) using R as a reference ontology in Alg. 2 and produce set of ontologies
  22. Require: r - number of optimisation repeats, value 5 was found to be sufficient
  23. Require: pickBal(di,V ) - abstract (due to simplicity of the description) function, which pops a subset S from set V ; S is characterised by these conditions: (1) all v ? S all the closest possible vectors to di, and (2) all the sets picked from V are balanced in size after a sequence of pickBal() applications that makes V empty
  24. 1: Minit ? random v ? V {* initial means *}
  25. 2: Vtmp ? V
  26. 3: repeat
  27. 4: c ? centroid(Minit )
  28. 5: v ? u such that dist(u, c) is maximal for u ? Vtmp
  29. 6: Minit ? Minit ? {v}
  30. 7: Vtmp ? Vtmp - {v}
  31. 8: until |Minit | < k
  32. 9: FACT ? {} {* empty map *}
  33. 10: Vtmp ? V
  34. 11: j ? 0
  35. 12: for di ? Minit do
  36. 13: Sbalanced ? pickBal(di,Vtmp)
  37. 14: j ? j + 1
  38. 15: FACT [ j] ? Sbalanced
  39. 16: end for
  40. 17: C ? 0/
  41. 18: for j ? FACT.keys() do
  42. 19: C ? C ? centroid(FACT [ j])
  43. 20: end for
  44. 21: V ECT 2SCORE ? {} {* empty map *}
  45. 22: for v ? V do
  46. 23: V ECT 2SCORE[v] ? {(c0, 0), . . . , (ck-1, k - 1)} such that {c0, . . . , ck-1} is a sequence of centroids from C ordered by the increasing distance from v
  47. 24: end for
  48. 25: CLUST ? 0/ {* clustering structure *}
  49. 26: S ? {} {* empty map *}
  50. 27: for j ? {1, . . . , r} do
  51. 28: Stmp ? random shuffle of V
  52. 29: initialize clustering c j with clusters given by pivotal centroids from C
  53. 30: sequentially process Stmp and assign each vector to the nearest available cluster from c j, keeping the clusters as balanced in size as possible
  54. 31: compute the score S[ j] for the obtained clustering by summing up the numbers pointed by respective centroids in V ECT 2SCORE for each vector in each cluster in c j
  55. 32: CLUST ? CLUST ? c j
  56. 33: end for
  57. 34: return cx ? CLUST with lowest score S[ j], s ? {1, . . . , r} associated
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Paper Citation


in Harvard Style

Novácěk V. (2007). IMPRECISE EMPIRICAL ONTOLOGY REFINEMENT - Application to Taxonomy Acquisition . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 31-38. DOI: 10.5220/0002391800310038


in Bibtex Style

@conference{iceis07,
author={Vít Novácěk},
title={IMPRECISE EMPIRICAL ONTOLOGY REFINEMENT - Application to Taxonomy Acquisition},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2007},
pages={31-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002391800310038},
isbn={978-972-8865-89-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - IMPRECISE EMPIRICAL ONTOLOGY REFINEMENT - Application to Taxonomy Acquisition
SN - 978-972-8865-89-4
AU - Novácěk V.
PY - 2007
SP - 31
EP - 38
DO - 10.5220/0002391800310038