HANKE: Hierarchical attention networks for knowledge extraction in political science domain

Erick Skorupa Parolin, Latifur Khan, Javier Osorio, Vito D'Orazio, Patrick T. Brandt, Jennifer Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Extracting structured metadata from unstructured text in different domains is gaining strong attention from multiple research communities. In Political Science, these metadata play a significant role on studying intra and inter-state interactions between political entities. The process of extracting such metadata usually relies on domain specific ontologies and knowledge-based repositories. In particular, Political Scientists regularly use the well-defined ontology CAMEO, which is designed for capturing conflict and mediation relations. Since CAMEO repositories are currently human maintained, the high cost and extensive human effort associated with updating them makes it difficult to include new entries on a regular basis. This paper introduces HANKE: an innovative framework for automatically extracting knowledge representations from unstructured sources, in order to extend CAMEO ontology both in the same domain and towards other related domains in political science. HANKE combines Hierarchical Attention Networks as engine for identifying relevant structures in raw-text and the novel Frequency-Based Ranker approach to obtain a collection of candidate entries for CAMEO's repositories. To show the efficiency of the proposed framework, we evaluate its performance on capturing existing CAMEO representations in a soft-labelled dataset. We also empirically demonstrate the versatility and superiority of HANKE method by applying it to two case studies related to CAMEO extension on its actual domain and towards organized crime domain.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
EditorsGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages410-419
Number of pages10
ISBN (Electronic)9781728182063
DOIs
StatePublished - Oct 2020
Externally publishedYes
Event7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australia
Duration: Oct 6 2020Oct 9 2020

Publication series

NameProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020

Conference

Conference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
Country/TerritoryAustralia
CityVirtual, Sydney
Period10/6/2010/9/20

Keywords

  • CAMEO
  • Hierarchical attention networks
  • Knowledge extraction
  • Political events

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Analysis
  • Discrete Mathematics and Combinatorics

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