Multi-Domain and Explainable Prediction of Changes in Web Vocabularies

Albert Meroño-Peñuela, Romana Pernisch, Christophe Guéret, Stefan Schlobach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.
Original languageEnglish
Title of host publicationProceedings of the 11th on Knowledge Capture Conference
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)978-1-4503-8457-5
StatePublished - Dec 1 2021
Externally publishedYes

Publication series

NameK-CAP '21
PublisherAssociation for Computing Machinery


  • change modelling
  • ontology evolution
  • vocabulary change


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  • DL: ICAI Discovery Lab

    van Harmelen, F., De Rijke, M., Siebert, M., Hoekstra, R., Tsatsaronis, G., Groth, P., Cochez, M., Pernisch, R., Alivanistos, D., Mansoury, M., van Hoof, H., Pal, V., Pijnenburg, T., Mitra, P., Bey, T. & de Waard, A.


    Project: Research

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