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Is the Crowd Better as an Assistant or a Replacement in Ontology Engineering? An Exploration Through the Lens of the Gene Ontology

机译：人群是本体工程学的助手还是替代者？基因本体论的探索

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Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential overcome the limited accuracy and scalability found in current ontology quality assurance approaches. Crowd-based methods have identified errors in SNOMED CT, a large, clinical ontology, with an accuracy similar to that of experts, suggesting that crowdsourcing is indeed a feasible approach for identifying ontology errors. This work uses that same crowd-based methodology, as well as a panel of experts, to verify a subset of the Gene Ontology (200 relationships). Experts identified 16 errors, generally in relationships referencing acids and metals. The crowd performed poorly in identifying those errors, with an area under the receiver operating characteristic curve ranging from 0.44 to 0.73, depending on the methods configuration. However, when the crowd verified what experts considered to be easy relationships with useful definitions, they performed reasonably well. Notably, there are significantly fewer Google search results for Gene Ontology concepts than SNOMED CT concepts. This disparity may account for the difference in performance – fewer search results indicate a more difficult task for the worker. The number of Internet search results could serve as a method to assess which tasks are appropriate for the crowd. These results suggest that the crowd fits better as an expert assistant, helping experts with their verification by completing the easy tasks and allowing experts to focus on the difficult tasks, rather than an expert replacement.
机译：生物医学本体包含错误。众包（Crowdsourcing）是指从事传统上由指定代理执行的工作，然后将其外包给未定义的一大群人，从而提供了可扩展的人员访问权限。因此，人群有潜力克服当前本体质量保证方法中发现的有限的准确性和可伸缩性。基于人群的方法已经在大型临床本体SNOMED CT中发现了错误，其准确性与专家相近，这表明，众包确实是一种识别本体错误的可行方法。这项工作使用相同的基于人群的方法以及一个专家小组来验证基因本体论的一个子集（200个关系）。专家们确定了16个错误，通常是在涉及酸和金属的关系中。人群在识别这些错误方面表现不佳，根据方法配置的不同，接收机工作特性曲线下的面积在0.44至0.73之间。但是，当人群通过有用的定义验证专家认为简单的关系时，他们的表现就相当不错。值得注意的是，与SNOMED CT概念相比，Gene Ontology概念的Google搜索结果要少得多。这种差异可能是性能差异的原因-搜索结果越少，说明工人的工作就越困难。互联网搜索结果的数量可以用作评估哪些任务适合人群的一种方法。这些结果表明，人群更适合作为专家助手，通过完成简单的任务并允许专家专注于困难的任务而不是专家的替代来帮助专家进行验证。

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