Learning taxonomic relations from heterogeneous evidence
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Learning Taxonomic Relations from Heterogeneous Evidence. Philipp Cimiano Aleksander Pivk Lars Schmidt-Thieme Steffen Staab (ECAI 2004). Purpose. To examine the possibility of learning taxonomic relations by considering various sources of evidence

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Learning Taxonomic Relations from Heterogeneous Evidence

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Learning Taxonomic Relations from Heterogeneous Evidence

Philipp Cimiano

Aleksander Pivk

Lars Schmidt-Thieme

Steffen Staab (ECAI 2004)


Purpose

  • To examine the possibility of learning taxonomic relations by considering various sources of evidence

  • Main aim:

    • To gain insight into the behavior of different approaches to learn taxonomic relations

    • To provide a first step towards combining these different approaches

    • To establish a baseline model for further research


Introduction

  • Taxonomies or conceptual hierarchies are useful in many NLP applications.

  • However, the development of suitable ontologies is time-consuming.

  • Automatically acquiring ontological knowledge is required.

  • The approach proposed in this paper learns taxonomic relations (is-a relation) by considering four different evidences:

    • Hearst-patterns matched in a large corpus

    • Hearst-patterns matched in WWW

    • WordNet

    • The ‘vertical relations’-heuristic


Introduction

  • Goal:

    • Learning is-a relations in tourism domain

  • Training Corpus:

    • Domain-specific:

      • http://www.lonelyplanet.com

      • http://www.all-inall.de

    • General:

      • British National Corpus

  • The ontology for evaluation:

    • A tourism reference ontology modeled by ontology engineer.

    • A few abstract concepts are removed.

    • 272 concepts, 225 direct is-a relations, and 636 non-direct is-a relations


Hearst Patterns

  • Lexico-syntactic patterns proposed by Hearst (1992).

    • N such as N1, N2,…

    • such N as N1, N2,…

    • N1, N2,… and other N

    • N, (especially | including) N1, N2,…

  • From these patterns, we could derive is-a(Ni, N).

  • Numbers of Hearst-patterns between different terms are recorded and normalized to 0~1.

  • Different thresholds are set and experimented.


Hearst Patterns


WordNet

  • WordNet is not “unstructured” source of evidence.

  • However, it is general and domain-independent.

  • One term may have several senses and there may be more than one hypernym relation between two terms.

  • Two different strategies are used:

    • Normalizing all hypernym paths between two terms:

    • Considering only the most frequent sense of t1


WordNet


WordNet


‘Vertical Relations’-Heuristic

  • Given t1 and t2, if t2 matches t1 and t1 is additionally modified by certain terms or adjectives, the relation is-a(t1, t2) is derived.

  • Ex. is-aHEURISTIC(international conference, conference)


World Wide Web

  • Google API (http://www.google.com/apis/) is used to count the matches of certain Hearst-patterns in the Web.

  • The sum of the number of Google hits over all patterns for a certain pair (t1, t2) is normalized by dividing through the number of hits returned for t1.


World Wide Web


Combining Evidences


Conclusion and Further Work

  • A simple combination strategy improves the results.

  • It remains further work to find out if other sources of evidence could be integrated into this approach.

  • It could turn out to be useful to only consider domain-specific text collections instead of a general corpus such as the BNC and to consider only pages in the World Wide Web related to the domain.

  • It remains as a challenge to determine the optimal strategy to combine the different approaches.

  • In order to apply machine learning techniques for this purpose, it is necessary to cope with the high number of negative examples.


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