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Using Embeddings to Predict Changes in Large Semantic Graphs

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Information Management and Big Data (SIMBig 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1070))

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Abstract

Understanding and predicting how large knowledge graphs change over time is as difficult as it is useful. An important subtask to address this artificial intelligence challenge is to characterize and predict three types of nodes: add-only nodes that can solely add up new edges, constant nodes whose edges remain unchanged, and del-only nodes whose edges can only be deleted. In this work, we improve previous prediction approaches by using word embeddings from NLP to identify the nodes of the large semantic graph and build a Logistic Regression model. We tested the proposed model in different versions of DBpedia and obtained the following prediction improvements on F1 measure: up to 10% for add-only nodes, close to 15% for constant nodes, and close to 22% for del-only nodes.

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Notes

  1. 1.

    http://dbpedia.org.

  2. 2.

    https://wiki.dbpedia.org/develop/datasets.

  3. 3.

    https://spark.apache.org/.

  4. 4.

    https://github.com/data61/stellar-random-walk.

  5. 5.

    Note that \(k\le l\) because \(o_k\) could be a literal, date for example, then it has no outgoing edges.

  6. 6.

    https://databus.dbpedia.org/dbpedia/mappings.

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Author information

Authors and Affiliations

  1. Group of Analysis and Processing of Large Social and Semantic Networks, FaMAF, Universidad Nacional de Córdoba, Córdoba, Argentina

    Damián Barsotti & Martín Ariel Domínguez

Authors
  1. Damián Barsotti
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  2. Martín Ariel Domínguez
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Corresponding authors

Correspondence to Damián Barsotti or Martín Ariel Domínguez .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. University of A Coruña, A Coruña, Spain

    Nelly Condori-Fernandez

  3. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

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Barsotti, D., Domínguez, M.A. (2020). Using Embeddings to Predict Changes in Large Semantic Graphs. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_26

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  • DOI: https://doi.org/10.1007/978-3-030-46140-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46139-3

  • Online ISBN: 978-3-030-46140-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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