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Leveraging Enterprise Knowledge Graphs for Efficient Bridging Between Business Data with Large-Scale Web Data

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Knowledge Graphs and Semantic Web (KGSWC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1459))

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Abstract

The diversification of the web into social media and the Web of Data means that companies need to collect the necessary data to make the best-informed market decisions. To deal with this, the new concept of Enterprise Knowledge Graphs (EKGs) is emerging as a backbone for federating valuable open information on the web together with the information contained in internal enterprise documents and databases. This paper examines the current challenges in this area, discusses the limitations of some existing integration systems, and addresses them by proposing a set of tools for virtually integrating enterprise data with social and linked data at scale. The proposed framework’s implementation is a configurable middleware and user-friendly keyword faceted search web interface that retrieves its input data from internal enterprise data combined with popular SPARQL endpoints and social network web APIs. We conducted an evaluation study to test our approach’s effectiveness using various metrics and compare it to state-of-the-art systems. The evaluation results show a competitive accuracy and usability of the proposed approach, facilitating the integration of data into a knowledge graph.

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Notes

  1. 1.

    https://lod-cloud.net/.

  2. 2.

    https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html.

  3. 3.

    http://wiki.dbpedia.org/.

  4. 4.

    https://www.wikidata.org.

  5. 5.

    https://github.com/Microsoft/sql-server-samples.

  6. 6.

    https://developers.facebook.com/docs/graph-api/.

  7. 7.

    https://jena.apache.org/documentation/fuseki2.

  8. 8.

    https://github.com/SamRepository/.

  9. 9.

    https://lov.linkeddata.es/dataset/lov.

  10. 10.

    https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks.

  11. 11.

    https://wiki.dbpedia.org/downloads-2016-10.

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Authors and Affiliations

  1. LIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria

    Samir Sellami & Nacer Eddine Zarour

  2. IRIT Laboratory, University of Toulouse 2 - Jean Jaurès, Toulouse, France

    Taoufiq Dkaki & Pierre-Jean Charrel

Authors
  1. Samir Sellami
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  2. Taoufiq Dkaki
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  3. Nacer Eddine Zarour
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  4. Pierre-Jean Charrel
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Corresponding author

Correspondence to Samir Sellami .

Editor information

Editors and Affiliations

  1. Tinamica/Universidad Internacional de la Rioja, Madrid, Madrid, Spain

    Boris Villazón-Terrazas

  2. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. Texas A&M University – Kingsville, Kingsville, TX, USA

    Ayush Goyal

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    MA Jabbar

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Sellami, S., Dkaki, T., Zarour, N.E., Charrel, PJ. (2021). Leveraging Enterprise Knowledge Graphs for Efficient Bridging Between Business Data with Large-Scale Web Data. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-91305-2_16

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