TY - GEN
T1 - Characterizing regulatory documents and guidelines based on text mining
AU - Winter, Karolin
AU - Rinderle-Ma, Stefanie
AU - Grossmann, Wilfried
AU - Feinerer, Ingo
AU - Ma, Zhendong
N1 - .
PY - 2017
Y1 - 2017
N2 - Implementing rules, constraints, and requirements contained in regulatory documents such as standards or guidelines constitutes a mandatory task for organizations and institutions across several domains. Due to the amount of domain-specific information and actions encoded in these documents, organizations often need to establish cooperations between several departments and consulting experts to guide managers and employees in eliciting compliance requirements. Providing computer-based guidance and support for this often costly and tedious compliance task is the aim of this paper. The presented methodology utilizes well-known text mining techniques and clustering algorithms to classify (families) of documents according to topics and to derive significant sentences which support users in understanding and implementing compliance-related documents. Applying the approach to collections of documents from the security and the medical domain demonstrates that text mining is a promising domain-independent mean to provide support to the understanding, extraction, and analysis of regulatory documents.
AB - Implementing rules, constraints, and requirements contained in regulatory documents such as standards or guidelines constitutes a mandatory task for organizations and institutions across several domains. Due to the amount of domain-specific information and actions encoded in these documents, organizations often need to establish cooperations between several departments and consulting experts to guide managers and employees in eliciting compliance requirements. Providing computer-based guidance and support for this often costly and tedious compliance task is the aim of this paper. The presented methodology utilizes well-known text mining techniques and clustering algorithms to classify (families) of documents according to topics and to derive significant sentences which support users in understanding and implementing compliance-related documents. Applying the approach to collections of documents from the security and the medical domain demonstrates that text mining is a promising domain-independent mean to provide support to the understanding, extraction, and analysis of regulatory documents.
KW - Compliance
KW - Regulatory documents
KW - Requirements extraction
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85032693400&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69462-7_1
DO - 10.1007/978-3-319-69462-7_1
M3 - Conference contribution
SN - 978-3-319-69461-0
T3 - Lecture Notes in Computer Science (LNCS)
SP - 3
EP - 20
BT - On the Move to Meaningful Internet Systems. OTM 2017 Conferences
A2 - Panetto, Herve
A2 - Paschke, Adrian
A2 - Meersman, Robert
A2 - Papazoglou, Mike
A2 - Debruyne, Christophe
A2 - Gaaloul, Walid
A2 - Ardagna, Claudio Agostino
PB - Springer
CY - Cham
T2 - Confederated International Conference On the Move to Meaningful Internet Systems, OTM 2017 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2017
Y2 - 23 September 2017 through 27 September 2017
ER -