众包图:众包本体与微任务的协调CROWDMAP: Crowdsourcing Ontology Alignment with Microtasks |
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课程网址: | http://videolectures.net/iswc2012_sarasua_crowdmap/ |
主讲教师: | Cristina Sarasua |
开课单位: | 柯布伦兹兰道大学 |
开课时间: | 2012-12-03 |
课程语种: | 英语 |
中文简介: | 最近十年的本体对齐研究带来了各种计算技术来发现本体之间的对应关系。虽然自动方法的准确性不断提高,但人类贡献仍然是该过程的关键要素:该输入作为领域知识的宝贵来源,用于训练算法并验证和增强自动计算的比对。在本文中,我们介绍了CROWDMAP,这是一种通过微任务众包获得此类人类贡献的模型。对于给定的一对本体,CROWDMAP将对齐问题转换为解决个别对齐问题的微任务,在在线劳动力市场上发布微任务,并评估从人群中获得的结果的质量。我们使用Ontology Alignment Evaluation Initiative和众包平台CrowdFlower的本体和参考对齐,在一系列实验中评估了CROWDMAP的当前实施。实验清楚地表明整体方法是可行的,并且可以以快速,可扩展和成本有效的方式提高现有本体对准解决方案的准确性。 |
课程简介: | The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CROWDMAP, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CROWDMAP translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CROWDMAP in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner. |
关 键 词: | 本体对齐; 自动方法; 训练算法 |
课程来源: | 视频讲座网 |
最后编审: | 2019-05-06:cwx |
阅读次数: | 35 |