弱标签的本体学习
Ontological Learning from Weak Labels
摘要：
本体包含通过定义领域的概念或属性以及这些概念之间的关系来对知识进行形式化表示。在这项工作中，我们试图研究使用这种本体信息是否会改善弱标记数据的学习，弱标记数据更容易收集，因为它只需要知道事件的存在或不存在。我们使用AudioSet本体和数据集，其中包含用本体概念弱标记的音频剪辑，并且本体提供概念之间的“Is A”关系。我们首先重新实现了soundevent_ontology提出的模型，并进行了修改以适应多标签场景，然后通过使用图卷积网络（GCN）对本体信息进行建模来扩展该想法以学习概念。我们发现，基线暹罗不执行更好地通过将本体信息在弱和多标签的情况下，但GCN确实捕捉本体知识更好地为弱，多标签的数据。在我们的实验中，我们还研究了不同的模块如何能够容忍弱标签引入的噪声，并更好地结合本体信息。我们最好的Siamese-GCN模型对于较低级别的概念实现了mAP=0.45和AUC=0.87，对于较高级别的概念实现了mAP=0.72和AUC=0.86，这是对基准Siamese的改进，但与我们不使用本体信息的模型大致相同。
英文摘要：
Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts. In this work, we seek to investigate whether using this ontological information will improve learning from weakly labeled data, which are easier to collect since it requires only the presence or absence of an event to be known. We use the AudioSet ontology and dataset, which contains audio clips weakly labeled with the ontology concepts and the ontology providing the "Is A" relations between the concepts. We first re-implemented the model proposed by soundevent_ontology with modification to fit the multi-label scenario and then expand on that idea by using a Graph Convolutional Network (GCN) to model the ontology information to learn the concepts. We find that the baseline Siamese does not perform better by incorporating ontology information in the weak and multi-label scenario, but that the GCN does capture the ontology knowledge better for weak, multi-labeled data. In our experiments, we also investigate how different modules can tolerate noises introduced from weak labels and better incorporate ontology information. Our best Siamese-GCN model achieves mAP=0.45 and AUC=0.87 for lower-level concepts and mAP=0.72 and AUC=0.86 for higher-level concepts, which is an improvement over the baseline Siamese but about the same as our models that do not use ontology information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
