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Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning

机译:学习工作流嵌入,提高基于相似性的基于案例的推理的检索性能

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In process-oriented case-based reasoning, similarity-based retrieval of workilow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAG ("Many are called, but few are chosen") retrieval has been proven useful to reduce the retrieval time but comes at the cost of an additional modeling effort for implementing the MAC phase. In this paper, we present a new approach to implement the MAC phase for POCBR retrieval, which makes use of the StarSpace embedding algorithm to automatically learn a vector representation for workflows, which can be used to significantly speed-up the MAC retrieval phase. In an experimental evaluation in the domain of cooking workflows, we show that the presented approach outperforms two existing MAC/FAC approaches on the same data.
机译:在以过程为导向的基于案例的推理,由于计算昂贵的相似性评估,基于相似性的工作流程案例来自大型案例基础的仍然是一个困难问题。已经证明了两阶段Mac / FAG(“许多人被称为少数被选中”)检索可用于减少检索时间,但是以实现MAC阶段的额外建模工作成本为有用。在本文中,我们提出了一种实现POCBR检索的MAC阶段的新方法,这使得使用Starspace嵌入算法自动学习工作流的向量表示,这可以用于显着加速MAC检索阶段。在烹饪工作流域的实验评估中,我们表明所提出的方法优于同一数据上的两个现有MAC / FAC。

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