Paper Info
Title
Towards a Flexible Embedding Learning Framework
Abstract
Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining problems, their applicability is often limited because these methods have pre-determined assumptions on the type of semantics captured by the learned embeddings, and the assumptions may not well align with specific downstream tasks. In this work, we propose an embedding learning framework that 1) uses an input format that is agnostic to input data type, 2) is flexible in terms of the relationships that can be embedded into the learned representations, and 3) provides an intuitive pathway to incorporate domain knowledge into the embedding learning process. Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database. Moreover, a sampling mechanism is carefully designed to establish a direct connection between the input and the information captured by the output embeddings. To complete the representation learning toolbox, we also outline a simple yet effective post-processing technique to properly visualize the learned embeddings. Our empirical results demonstrate that the proposed framework, in conjunction with a set of relevant entity-relation-matrices, outperforms the existing state-of-the-art approaches in various data mining tasks.
Year
DOI
Venue
2020
10.1109/ICDMW51313.2020.00086
2020 International Conference on Data Mining Workshops (ICDMW)
Keywords
DocType
ISSN
embedding learning,representation learning,unsupervised learning,visualization,clustering
Conference
2375-9232
ISBN
Citations 
PageRank 
978-1-7281-9013-6
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Chin-Chia Michael Yeh111.71
Dhruv Gelda200.34
Zhongfang Zhuang383.54
Yan Zheng402.37
Liang Gou526915.49
Wei Zhang6569.29