ARIADNE: Final Report on Data Mining

W.X. Wilcke, V. de Boer, F.A.H. van Harmelen, M.T.M. de Kleijn, M. Wansleeben, Harry Dimitropoulos, Holly Wright (Editor)

Research output: Book/ReportReport

Abstract

Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to archaeological data from various sources. Parts of these data have been being published as Linked Data, and are currently 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. While general-purpose software exists, recent studies have revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trust in the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to the understanding of linked archaeological data. A case study was held, which involved the mining of semantic association rules over data sets of increasing levels of knowledge
granularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant.
LanguageEnglish
PublisherAriadne
StatePublished - 2017

Publication series

NameARIADNE
No.D16.3

Fingerprint

level of knowledge
networking
semantics
expert
infrastructure
experiment
evaluation
community
software

Cite this

Wilcke, W. X., de Boer, V., van Harmelen, F. A. H., de Kleijn, M. T. M., Wansleeben, M., Dimitropoulos, H., & Wright, H. (Ed.) (2017). ARIADNE: Final Report on Data Mining. (ARIADNE; No. D16.3). Ariadne.
Wilcke, W.X. ; de Boer, V. ; van Harmelen, F.A.H. ; de Kleijn, M.T.M. ; Wansleeben, M. ; Dimitropoulos, Harry ; Wright, Holly (Editor). / ARIADNE: Final Report on Data Mining. Ariadne, 2017. (ARIADNE; D16.3).
@book{fcb11c81af894034a33d5a6d705166b0,
title = "ARIADNE: Final Report on Data Mining",
abstract = "Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to archaeological data from various sources. Parts of these data have been being published as Linked Data, and are currently 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. While general-purpose software exists, recent studies have revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trust in the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to the understanding of linked archaeological data. A case study was held, which involved the mining of semantic association rules over data sets of increasing levels of knowledgegranularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant.",
author = "W.X. Wilcke and {de Boer}, V. and {van Harmelen}, F.A.H. and {de Kleijn}, M.T.M. and M. Wansleeben and Harry Dimitropoulos and Holly Wright",
year = "2017",
language = "English",
series = "ARIADNE",
publisher = "Ariadne",
number = "D16.3",

}

Wilcke, WX, de Boer, V, van Harmelen, FAH, de Kleijn, MTM, Wansleeben, M, Dimitropoulos, H & Wright, H (ed.) 2017, ARIADNE: Final Report on Data Mining. ARIADNE, no. D16.3, Ariadne.

ARIADNE: Final Report on Data Mining. / Wilcke, W.X.; de Boer, V.; van Harmelen, F.A.H.; de Kleijn, M.T.M.; Wansleeben, M.; Dimitropoulos, Harry; Wright, Holly (Editor).

Ariadne, 2017. (ARIADNE; No. D16.3).

Research output: Book/ReportReport

TY - BOOK

T1 - ARIADNE: Final Report on Data Mining

AU - Wilcke,W.X.

AU - de Boer,V.

AU - van Harmelen,F.A.H.

AU - de Kleijn,M.T.M.

AU - Wansleeben,M.

AU - Dimitropoulos,Harry

A2 - Wright,Holly

PY - 2017

Y1 - 2017

N2 - Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to archaeological data from various sources. Parts of these data have been being published as Linked Data, and are currently 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. While general-purpose software exists, recent studies have revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trust in the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to the understanding of linked archaeological data. A case study was held, which involved the mining of semantic association rules over data sets of increasing levels of knowledgegranularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant.

AB - Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to archaeological data from various sources. Parts of these data have been being published as Linked Data, and are currently 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. While general-purpose software exists, recent studies have revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trust in the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to the understanding of linked archaeological data. A case study was held, which involved the mining of semantic association rules over data sets of increasing levels of knowledgegranularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant.

M3 - Report

T3 - ARIADNE

BT - ARIADNE: Final Report on Data Mining

PB - Ariadne

ER -

Wilcke WX, de Boer V, van Harmelen FAH, de Kleijn MTM, Wansleeben M, Dimitropoulos H et al. ARIADNE: Final Report on Data Mining. Ariadne, 2017. (ARIADNE; D16.3).