Abstract
In recent years, there has been a growing interest from the digital humanities in knowledge graphs as data modelling paradigm. Already, this has led to the creation of many such knowledge graphs, many of which are now available as part of the Linked Open Data cloud. This presents new opportunities for data mining. In this work, we develop, implement, and evaluate (both data-driven and user-driven) an end-to-end pipeline for user-centric pattern mining on knowledge graphs in the humanities. This pipeline combines constrained generalized association rule mining with natural language output and facet rule browsing to allow for transparency and interpretability—two key domain requirements. Experiments in the archaeological domain show that domain experts were positively surprised by the range of patterns that were discovered and were overall optimistic about the future potential of this approach.
Original language | English |
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Article number | 100486 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Journal of Web Semantics |
Volume | 59 |
Early online date | 13 Dec 2018 |
DOIs | |
Publication status | Published - Dec 2019 |
Funding
We wish to express our deep gratitude to domain experts Milco Wansleeben and Rein van ’t Veer for their enthusiastic encouragement and useful critiques during the various steps that have lead to this work. We also wish to thank all domain experts who participated in our survey for their willingness to sacrifice their free time, and without whom we would not have been able to complete this research. This research has been partially funded by the ARIADNE project through the European Commission under the Community’s Seventh Framework Programme, contract no. FP7-INFRASTRUCTURES-2012-1-313193 .
Funders | Funder number |
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Seventh Framework Programme | 313193 |
European Commission | |
Seventh Framework Programme | FP7-INFRASTRUCTURES-2012-1-313193 |
Keywords
- Archaeology
- Digital humanities
- Generalized association rules
- Knowledge graphs
- Pattern mining
- User-centric