Ontology-Style Relation Annotation: A Case Study
Ontology-Style Relation Annotation: A Case Study
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发表时间：
2020-05
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通讯作者：
Savong Bou;Naoki Suzuki;Makoto Miwa;Yutaka Sasaki
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作者：
Savong Bou;Naoki Suzuki;Makoto Miwa;Yutaka Sasaki
本文提出了一种本体风格关系（OSR）标注方法。在传统的关系提取（RE）数据集中，关系被注释为实体提及之间的链接。相比之下，在我们的OSR注释中，关系被注释为关系提及（即，不是链接而是节点），并且域和范围链接从关系提及到其自变量实体提及被注释。我们期望以下好处：（1）关系注释可以很容易地转换为资源描述框架（RDF）三元组填充本体，（2）一些传统的RE任务的一部分，可以解决命名实体识别（NER）任务。关系类仅限于几个RDF属性，如域，范围和subClassOf，和（3）OSR注释可以是本体内容的清晰文档。作为一个案例研究，我们转换为OSR注释的日本交通规则的内部语料库，并建立了一个新的OSR-RoR（道路规则）语料库。注释者之间的转换协议为85- 87%。我们评估了神经NER和RE工具在传统和OSR注释上的性能。实验结果表明，OSR注释使RE任务更容易，同时引入轻微的复杂性到NER任务。
This paper proposes an Ontology-Style Relation (OSR) annotation approach. In conventional Relation Extraction (RE) datasets, relations are annotated as links between entity mentions. In contrast, in our OSR annotation, a relation is annotated as a relation mention (i.e., not a link but a node) and domain and range links are annotated from the relation mention to its argument entity mentions. We expect the following benefits: (1) the relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology, (2) some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks. The relation classes are limited to several RDF properties such as domain, range, and subClassOf, and (3) OSR annotations can be clear documentations of Ontology contents. As a case study, we converted an in-house corpus of Japanese traffic rules in conventional annotations into the OSR annotations and built a novel OSR-RoR (Rules of the Road) corpus. The inter-annotator agreements of the conversion were 85-87%. We evaluated the performance of neural NER and RE tools on the conventional and OSR annotations. The experimental results showed that the OSR annotations make the RE task easier while introducing slight complexity into the NER task.