User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study

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In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of archaeological knowledge, expressed as knowledge graph, and which form would best meet the communities' needs. A case study was held which involved the user-driven mining of generalized association rules. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant by domain experts.
Original languageEnglish
Number of pages2
Publication statusPublished - 9 Jun 2017
EventBenelearn 2017: The annual machine learning conference of the Benelux - TU Eindhoven, Eindhoven, Netherlands
Duration: 9 Jun 201710 Jun 2017


ConferenceBenelearn 2017
Internet address


  • Digital Humanities
  • Rule Learning
  • Knowledge Graphs


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