Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination. While the wide coverage of knowledge about entities and events make Wikidata a promising resource for analogical reasoning across situations and domains, Wikidata has not been employed for this task yet. In this paper, we investigate whether the knowledge in Wikidata supports analogical reasoning. Specifically, we study whether relational knowledge is modeled consistently in Wikidata, observing that relevant relational information is typically missing or modeled in an inconsistent way. Our further experiments show that Wikidata can be used to create data for analogy classification, but this requires much manual effort. To facilitate future work that can support analogies, we discuss key desiderata, and devise a set of metrics to guide an automatic method for extracting analogies from Wikidata.
翻译:分析推理方法建立在各种资源之上, 包括常识知识基础、 词汇资源、 语言模型或其组合。 虽然关于实体和事件的知识的覆盖面很广, 使维基数据成为不同情况和领域模拟推理的有希望的资源, 但维基数据尚未用于这项任务。 在本文中, 我们调查维基数据中的知识是否支持模拟推理。 具体地说, 我们研究维基数据中的关系知识的模型是否一致, 发现相关的关联信息通常丢失或以不一致的方式建模。 我们的进一步实验显示, 维基数据可以用来创建数据进行类比分类, 但是这需要大量手工工作。 为了便利未来能够支持模拟的工作, 我们讨论关键偏斜, 并设计一套衡量尺度来指导从维基数据中提取模拟的自动方法 。