CoreKG: a Knowledge Lake service

Amin Beheshti*, Boualem Benatallah, Reza Nouri, Alireza Tabebordbar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


With Data Science continuing to emerge as a powerful differentiator across industries, organisations are now focused on transforming their data into actionable insights. This task is challenging as in today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of raw data from a variety of sources. Data Lakes introduced as a storage repository to organize this raw data in its native format (supporting from relational to NoSQL DBs) until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to cook/curate them later. In this paper, we present the notion of Knowledge Lake, i.e. a contextualized Data Lake. The Knowledge Lake will provide the foundation for big data analytics by automatically curating the raw data in the Data Lake and to prepare them for deriving insights. We present CoreKG -an open source Data and Knowledge Lake service- which offers researchers and developers a single REST API to organize, curate, index and query their data and metadata in the Lake and over time. CoreKG manages multiple database technologies (from Relational to NoSQL) and offers a built-in design for data curation, security and provenance.

Original languageEnglish
Pages (from-to)1942-1945
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - Aug 2018


Dive into the research topics of 'CoreKG: a Knowledge Lake service'. Together they form a unique fingerprint.

Cite this