DescriptionIn 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.
|Period||9 Jun 2017|
|Event title||Benelearn 2017: The annual machine learning conference of the Benelux|
|Degree of Recognition||International|
Documents & Links
Research output: Contribution to Conference › Abstract › Academic