Ecole d'ingénieur et centre de recherche en Sciences du numérique

Local embeddings for relational data integration

Cappuzzo, Riccardo; Papotti, Paolo; Thirumuruganathan, Saravanan

Submitted on ArXiV, 3 September 2019

Integrating information from heterogeneous data sources is one of the fundamental problems facing any enterprise. Recently, it has been shown that deep learning based techniques such as embeddings are a promising approach for data integration problems. Prior efforts directly use pre-trained embeddings or simplistically adapt techniques from natural language processing to obtain relational embeddings. In this work, we propose algorithms for obtaining local embeddings that are effective for data integration tasks on relational data. We make three major contributions. First, we describe a compact graph-based representation that allows the specification of a rich set of relationships inherent in relational world. Second, we propose how to derive sentences from such graph that effectively "describe" the similarity across elements (tokens, attributes, rows) across the two datasets. The embeddings are learned based on such sentences. Finally, we propose a diverse collection of criteria to evaluate relational embeddings and perform extensive set of experiments validating them. Our experiments show that our system, EmbDI, produces meaningful results for data integration tasks and our embeddings improve the result quality for existing state of the art methods.

Arxiv Bibtex

Titre:Local embeddings for relational data integration
Type:Conférence
Langue:English
Ville:
Date:
Département:Data Science
Eurecom ref:6002
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted on ArXiV, 3 September 2019 and is available at :
Bibtex: @inproceedings{EURECOM+6002, year = {2019}, title = {{L}ocal embeddings for relational data integration}, author = {{C}appuzzo, {R}iccardo and {P}apotti, {P}aolo and {T}hirumuruganathan, {S}aravanan}, booktitle = {{S}ubmitted on {A}r{X}i{V}, 3 {S}eptember 2019}, address = {}, month = {09}, url = {http://www.eurecom.fr/publication/6002} }
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