{'fraction_non_alphanumeric': 0.020588235294117647, 'fraction_numerical': 0.003676470588235294, 'mean_word_length': 5.543269230769231, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Developing Crowdsourced Ontology Engineering Tasks: An iterative process

It is increasingly evident that the realization of the Semantic Web will require not only computation, but also human contribution. Crowdsourcing is becoming a popular method to inject this human element. Researchers have shown how crowdsourcing can contribute to managing semantic data. One particular area that requires significant human curation is ontology engineering. Verifying large and complex ontologies is a challenging and expensive task. Recently, we have demonstrated that online, crowdsourced workers can assist with ontology verification. Specifically, in our work we sought to answer the following driving questions: (1) Is crowdsourcing ontology verification feasible? (2) What is the optimal formulation of the verification task? (3) How does this crowdsourcing method perform in an application? In this work, we summarize the experiments we developed to answer these questions and the results of each experiment. Through iterative task design, we found that workers could reach an accuracy of 88% when verifying SNOMED CT. We then discuss the practical knowledge we have gained from these experiments. This work shows the potential that crowdsourcing has to offer other ontology engineering tasks and provides a template one might follow when developing such methods.