Local Embeddings for Relational Data Integration

TL;DR

This paper introduces EmbDI, a framework for learning local relational data embeddings using graph-based representations and sentence derivation, improving data integration tasks like schema matching and entity resolution.

Contribution

The paper presents a novel graph-based approach for deriving effective local embeddings tailored for relational data integration, addressing limitations of previous NLP-inspired methods.

Findings

EmbDI outperforms baseline methods in schema matching tasks.

EmbDI achieves high accuracy in entity resolution in both supervised and unsupervised settings.

The proposed embeddings effectively capture relational context, enhancing data integration quality.

Abstract

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not always be an appropriate choice for enterprise datasets with custom vocabulary. Other methods adapt techniques from natural language processing to obtain embeddings for the enterprise's relational data. However, this approach blindly treats a tuple as a sentence, thus losing a large amount of contextual information present in the tuple. We propose algorithms for obtaining local embeddings that are effective for data integration tasks on relational databases. We make four major contributions. First, we describe a compact graph-based representation that allows the specification of a rich set of relationships inherent in the relational world. Second, we propose how to derive sentences from such a graph that effectively "describe" the similarity across elements (tokens, attributes, rows) in the two datasets. The embeddings are learned based on such sentences. Third, we propose effective optimization to improve the quality of the learned embeddings and the performance of integration tasks. Finally, we propose a diverse collection of criteria to evaluate relational embeddings and perform an extensive set of experiments validating them against multiple baseline methods. Our experiments show that our framework, EmbDI, produces meaningful results for data integration tasks such as schema matching and entity resolution both in supervised and unsupervised settings.