Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning
Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning
复制标题
学习工作流嵌入以提高面向过程的基于案例的推理的基于相似性的检索的性能
DOI：
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发表时间：
2019
期刊：
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通讯作者：
R. Bergmann
中科院分区：
文献类型：
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作者：
P. Klein;Lukas Malburg;R. Bergmann
在面向过程的案例推理中，基于相似性的案例检索仍然是一个困难的问题，由于计算昂贵的相似性评估。两阶段的MAC/FAC（“许多被称为，但很少被选择”）检索已被证明是有用的，以减少检索时间，但在成本的额外的建模工作，以实现MAC阶段。在本文中，我们提出了一种新的方法来实现MAC阶段的POCBR检索，它利用StarSpace嵌入算法自动学习的工作流，这可以用来显着加快MAC检索阶段的矢量表示。在烹饪工作流程领域的实验评估中，我们表明，所提出的方法优于现有的两个MAC/FAC方法相同的数据。
In process-oriented case-based reasoning, similarity-based retrieval of workflow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAC (“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.