Kv2vec: A Distributed Representation Method for Key-value Pairs from Metadata Attributes

Abstract

Distributed representation methods for words have been developed for years, and numerous methods exist, such as word2vec, GloVe, and fastText. However, they are not designed for key-value pairs, which is an important data pattern and widely used in many scenarios. For example, metadata attributes of scientific files consist of a collection of key-value pairs. In this research, we propose kv2vec, a method that captures relationships between keys and values and represents key-value pairs in dense vectors. The fundamental idea of the kv2vec method is utilizing recurrent neural networks (RNNs) with long short-term memory (LSTM) hidden units to convert each key-value pair to a distributed vector representation. This new method overcomes the weaknesses of existing embedding models for representing key-value pairs as vectors. Moreover, it can be integrated into dataset search solutions through querying metadata attributes for self-describing file formats that are widely used in HPC systems. We evaluate the kv2vec method with multiple real-world datasets, and the results show that kv2vec outperforms existing models.