In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey.
翻译:近年来,DBpedia、FreeBase、OpenCyc、Wikidata和YAGO作为值得注意的大型、跨领域和可自由获取的知识图表出版。虽然这些知识图表在广泛使用,但在特定环境下很难相互比较。因此,研究人员和开发商为个人需求选择最佳知识图表是一项挑战。在最近的调查中,我们设计并应用了上述知识图表的数据质量标准。此外,我们提出了一个为特定环境寻找最合适的知识图表的框架。我们打算利用这份文件来简化规则,将个人数据质量要求绘制成具体知识图表,从而方便我们深入调查的获取。然而,本文不打算取代我们先前提出的决策支持框架。关于KG最适合你的决定,我们仍提及深入调查。