WDV:基于维基数据构建的广泛的数据语言化数据集

Gabriel Amaral, Odinaldo Rodrigues, E. Simperl
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引用次数: 4

摘要

数据语言化是当前自然语言处理领域中非常重要的一项任务,因为将我们丰富的结构化和半结构化数据转换为人类可读的格式有很大的好处。语言化知识图(KG)数据侧重于将相互关联的三元声明(由主语、谓语和宾语组成)转换为文本。尽管KG语言化数据集存在于一些KG,但它们在许多场景中的适用性仍然存在差距。对于Wikidata来说尤其如此,其中可用的数据集要么松散地将声明集与文本信息耦合在一起,要么主要关注传记、城市和国家周围的谓词。为了解决这些差距,我们提出了WDV,这是一个从维基数据构建的大型KG索赔语言化数据集,具有三元组和文本之间的紧密耦合,涵盖了各种实体和谓词。我们还通过可重复使用的工作流程来评估我们的语言质量,以衡量以人为中心的流利性和充分性得分。我们的数据和代码是公开的,希望进一步研究KG语言化。
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WDV: A Broad Data Verbalisation Dataset Built from Wikidata
Data verbalisation is a task of great importance in the current field of natural language processing, as there is great benefit in the transformation of our abundant structured and semi-structured data into human-readable formats. Verbalising Knowledge Graph (KG) data focuses on converting interconnected triple-based claims, formed of subject, predicate, and object, into text. Although KG verbalisation datasets exist for some KGs, there are still gaps in their fitness for use in many scenarios. This is especially true for Wikidata, where available datasets either loosely couple claim sets with textual information or heavily focus on predicates around biographies, cities, and countries. To address these gaps, we propose WDV, a large KG claim verbalisation dataset built from Wikidata, with a tight coupling between triples and text, covering a wide variety of entities and predicates. We also evaluate the quality of our verbalisations through a reusable workflow for measuring human-centred fluency and adequacy scores. Our data and code are openly available in the hopes of furthering research towards KG verbalisation.
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