Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

By Raul Castro Fernandez, Essam Mansour, A. Qahtan, Ahmed K. Elmagarmid, Ihab F. Ilyas, Samuel Madden, Mourad Ouzzani, M. Stonebraker, Nan Tang. 2018

143 citations recorded by the source corpus.

Employees that spend more time finding relevant data than analyzing it suffer from a data discovery problem. The large volume of data in enterprises, and sometimes the lack of knowledge of the schemas aggravates this problem. Similar to how we navigate the Web, we propose to identify semantic links that assist analysts in their discovery tasks. These links relate tables to each other, to facilitate navigating the schemas. They also relate data to external data sources, such as ontologies and dictionaries, to help explain the schema meaning. We materialize the links in an enterprise knowledge graph, where they become available to analysts. The main challenge is how to find pairs of objects that are semantically related. We propose SEMPROP, a DAG of different components that find links based on syntactic and semantic similarities. SEMPROP is commanded by a semantic matcher which leverages word embeddings to find objects that are semantically related. We introduce coherent group, a technique to combine word embeddings that works better than other state of the art combination alternatives. We implement SEMPROP as part of Aurum, a data discovery system we are building, and conduct user studies, real deployments and a quantitative evaluation to understand the benefits of links for data discovery tasks, as well as the benefits of SEMPROP and coherent groups to find those links.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marc Robinson-Rechavi
2. Manuel Gil
3. Maria Anisimova
4. Christophe Dessimoz

In Management Science and Operations Research

1. Manuel Gil
2. Maria Anisimova
3. Christophe Dessimoz

In Molecular Biology

1. Christophe Dessimoz
2. Tarcisio Mendes de Farias
3. Manuel Gil
4. Marc Robinson-Rechavi
5. Maria Anisimova
6. Ari Löytynoja
7. Nick Goldman
8. Olivier Gascuel

Citations in the corpus

• Enabling semantic queries across federated bioinformatics databases (2019, background): “An emerging research direction entails automatically discovering links between datasets using Word Embeddings (28).”