Relational Graph Embeddings for Table Retrieval

Ad hoc table retrieval is the problem of identifying the most relevant datasets to a user's query. We present an approach to the problem that builds a knowledge graph by combining information about the collection of tables with external sources such as WordNet and pretrained Glove embeddings. We apply multi-relational graph convolutional networks to learn embeddings for the knowledge graph nodes and utilize three different methods to create vectors representing the tables and queries from these embeddings. We create a novel learning-to-rank neural architecture that incorporates the multiple embeddings in order to improve table retrieval results. We evaluate our approach using two large collections of tables from public WikiTables and Web tables data, demonstrating substantial improvements over state-of-the-art methods in table retrieval.

Presented in Seventh International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2020), held with IEEE BigData 2020, December 2020. In 2020 IEEE International Conference on Big Data (Big Data), pages 3005-3014. DOI: 10.1109/BigData50022.2020.9378239.

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