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Knowledge Graphs for Community Detection in Textual Data

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Knowledge Graphs and Semantic Web (KGSWC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1686))

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Abstract

Online sources produce a huge amount of textual data, i.e., freeform text. To derive insightful information from them and facilitate the application of Machine Learning algorithms textual data need to be processed and structured. Knowledge Graphs (KGs) are intelligent systems for the analysis of documents. In recent years, they have been adopted in multiple contexts, including text mining for the development of data-driven solutions to different problems. The scope of this paper is to provide a methodology to build KGs from textual data and apply algorithms to group similar documents in communities. The methodology exploits semantic and statistical approaches to extract relevant insights from each document; these data are then organized in a KG that allows for their interconnection. The methodology has been successfully tested on news articles related to crime events occurred in the city of Modena, in Italy. The promising results demonstrate how KG-based analysis can improve the management of information coming from online sources.

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Notes

  1. 1.

    https://www.dbpedia-spotlight.org/.

  2. 2.

    https://github.com/MartinoMensio/spacy-dbpedia-spotlight.

  3. 3.

    See examples of the “diversity” parameter in KeyBERT at https://github.com/MaartenGr/KeyBERT.

  4. 4.

    https://neo4j.com/.

  5. 5.

    https://neo4j.com/docs/graph-data-science/.

  6. 6.

    https://gazzettadimodena.gelocal.it.

  7. 7.

    Italian Crime News dataset: https://paperswithcode.com/dataset/italian-crime-news.

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Acknowledgments

This work is partially supported by the project “Deep Learning for Urban Event Extraction from News and Social media streams” founded by the Engineering Department “Enzo Ferrari” of the University of Modena and Reggio Emilia.

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Authors and Affiliations

  1. “Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, Modena, Italy

    Federica Rollo & Laura Po

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  1. Federica Rollo
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Corresponding author

Correspondence to Federica Rollo .

Editor information

Editors and Affiliations

  1. EY wavespace/UNIR, Madrid, Spain

    Boris Villazón-Terrazas

  2. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodriguez

  3. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. University of Alcalá, Alcalá de Henares, Spain

    Miguel-Angel Sicilia

  5. Universidad Camilo José Cela, Madrid, Spain

    David Martín-Moncunill

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Rollo, F., Po, L. (2022). Knowledge Graphs for Community Detection in Textual Data. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_15

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  • DOI: https://doi.org/10.1007/978-3-031-21422-6_15

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