HANKE: Hierarchical Attention Networks for Knowledge Extraction in Political Science Domain
HANKE: Hierarchical Attention Networks for Knowledge Extraction in Political Science Domain
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DOI：
10.1109/dsaa49011.2020.00055
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Eric Parolin;L. Khan;Javier Osorio;Vito D'Orazio;Patrick T. Brandt;J. Holmes
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作者：
Eric Parolin;L. Khan;Javier Osorio;Vito D'Orazio;Patrick T. Brandt;J. Holmes
从不同领域的非结构化文本中提取结构化元数据正受到多个研究团体的强烈关注。在政治学中，这些元数据在研究政治实体之间的国家内部和国家间的相互作用方面发挥着重要作用。提取此类元数据的过程通常依赖于特定领域的本体和基于知识的存储库。特别是，政治科学家经常使用定义良好的本体CAMEO，它是为捕获冲突和调解关系而设计的。由于CAMEO存储库目前是由人工维护的，因此与更新它们相关的高成本和大量人工工作使得很难定期包含新条目。本文介绍了一种创新的框架HANKE，用于从非结构化源中自动提取知识表示，以扩展CAMEO本体在同一领域和其他相关领域的政治学。HANKE结合了层次注意网络作为识别原始文本中相关结构的引擎，以及基于频率的排名方法来获得CAMEO存储库的候选条目集合。为了展示所提出框架的效率，我们评估了其在软标记数据集中捕获现有CAMEO表示的性能。我们还通过将HANKE方法应用于CAMEO在其实际领域和有组织犯罪领域的扩展相关的两个案例研究，实证证明了HANKE方法的通用性和优越性。
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.