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International Conference on Web Information Systems Engineering

WISE 2020: Web Information Systems Engineering pp 24–34Cite as

Intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data

Part of the Communications in Computer and Information Science book series (CCIS,volume 1155)

Abstract

The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from the big data generated on open, private, social and IoT (Internet of Things) data islands. Data Lakes introduced as a storage repository to organize this raw data in its native format until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to curate them later. Previously, we introduced the novel notion of Knowledge Lake, i.e., a contextualized Data Lake, and proposed algorithms to turn the raw data (stored in Data Lakes) into contextualized data and knowledge using extraction, enrichment, annotation, linking and summarization techniques. In this tutorial, we introduce Intelligent Knowledge Lakes to facilitate linking Artificial Intelligence (AI) and Data Analytics. This will enable AI applications to learn from contextualized data and use them to automate business processes and develop cognitive assistance for facilitating the knowledge intensive processes or generating new rules for future business analytics.

Keywords

  • Knowledge Lake
  • Data Lake
  • Data analytics
  • Data curation
  • Artificial Intelligence
  • Big data

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Boston_Marathon_bombing.

  2. 2.

    https://developers.google.com/knowledge-graph.

  3. 3.

    https://www.wikidata.org/.

  4. 4.

    An entity E is represented as a data object that exists separately and has a unique identity. Entities are described by a set of attributes.

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Acknowledgements

We Acknowledge the AI-enabled Processes (AIP) (https://aip-research-center.github.io/) Research Centre for funding part of this research.

Author information

Authors and Affiliations

  1. Macquarie University, Sydney, Australia

    Amin Beheshti, Quan Z. Sheng & Francesco Schiliro

  2. University of New South Wales, Sydney, Australia

    Boualem Benatallah

  3. Australian Federal Police, Canberra, Australia

    Francesco Schiliro

Authors
  1. Amin Beheshti
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  2. Boualem Benatallah
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  3. Quan Z. Sheng
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  4. Francesco Schiliro
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Corresponding author

Correspondence to Amin Beheshti .

Editor information

Editors and Affiliations

  1. University of Macau, Macau, China

    Leong Hou U

  2. Macquarie University, Sydney, NSW, Australia

    Prof. Jian Yang

  3. South China University of Technology, Guangzhou, China

    Yi Cai

  4. International Institute of Information Technology, Hyderabad, India

    Kamalakar Karlapalem

  5. Soochow University, Suzhou, China

    An Liu

  6. Hong Kong Baptist University, Hong Kong, China

    Xin Huang

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Beheshti, A., Benatallah, B., Sheng, Q.Z., Schiliro, F. (2020). Intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_3

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  • DOI: https://doi.org/10.1007/978-981-15-3281-8_3

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