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

Research output: Contribution to ConferenceAbstractAcademic

Abstract

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.

Conference

ConferenceBenelearn 2017
CountryNetherlands
CityEindhoven
Period9/06/1710/06/17
Internet address

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expert
paradigm
experiment
community

Keywords

  • Digital Humanities
  • Rule Learning
  • Knowledge Graphs

Cite this

@conference{88aee8bfd11640ccaa925704a3469fa4,
title = "User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study",
abstract = "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.",
keywords = "Digital Humanities, Rule Learning, Knowledge Graphs",
author = "W.X. Wilcke and {de Boer}, Viktor and {van Harmelen}, Frank",
year = "2017",
month = "6",
day = "9",
language = "English",
note = "Benelearn 2017 : The annual machine learning conference of the Benelux ; Conference date: 09-06-2017 Through 10-06-2017",
url = "http://wwwis.win.tue.nl/~benelearn2017/",

}

User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study. / Wilcke, W.X.; de Boer, Viktor; van Harmelen, Frank.

2017. Abstract from Benelearn 2017, Eindhoven, Netherlands.

Research output: Contribution to ConferenceAbstractAcademic

TY - CONF

T1 - User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study

AU - Wilcke, W.X.

AU - de Boer, Viktor

AU - van Harmelen, Frank

PY - 2017/6/9

Y1 - 2017/6/9

N2 - 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.

AB - 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.

KW - Digital Humanities

KW - Rule Learning

KW - Knowledge Graphs

M3 - Abstract

ER -