A semi-automatic emerging technology trend classifier using SCOPUS
and PATSTAT

dc.contributor.author	Seonho, Kim	en
dc.contributor.author	Woondong, Yeo	en
dc.contributor.author	Byong-Youl, Coh	en
dc.contributor.author	Waqas, Rasheed	en
dc.contributor.author	Jaewoo, Kang	en
dc.date.accessioned	2015-04-25T16:36:45Z
dc.date.available	2015-04-25T16:36:45Z
dc.date.issued	2015-04-25
dc.identifier.uri	http://hdl.handle.net/11400/8930
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://history.icininfo.net/2011/	el
dc.source	http://history.icininfo.net/2011/FileStore/procs_INFO_2011.pdf	el
dc.subject	Machine learning
dc.subject	SCOPUS
dc.subject	PATSTAT
dc.subject	Emerging trend detection
dc.subject	Αναδυόμενες τάσης ανίχνευσης
dc.subject	Μηχανή μάθησησης
dc.subject	Artificial neural network
dc.subject	Τεχνητό νευρωνικό δίκτυο
dc.title	A semi-automatic emerging technology trend classifier using SCOPUS and PATSTAT	en
heal.type	conferenceItem
heal.classification	Computer science
heal.classification	Information systems
heal.classification	Πληροφορική
heal.classification	Πληροφοριακά συστήματα
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://skos.um.es/unescothes/C01993
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Πληροφοριακά συστήματα
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85079324
heal.contributorName	Γιαννακόπουλος, Γεώργιος Α. (συντ.)	el
heal.contributorName	Σακκάς, Δαμιανός Π. (συντ.)	el
heal.language	en
heal.access	free
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Διοίκησης και Οικονομίας. Τμήμα Βιβλιοθηκονομίας και Συστημάτων Πληροφόρησης	el
heal.publicationDate	2011-09
heal.bibliographicCitation	Seonho, K., Woondong, Y, Byong-Youl, C., Waqas, R. and Jaewoo, K. (2011). A semi-automatic emerging technology trend classifier using SCOPUS and PATSTAT. International Conference on Integrated Information (IC-ININFO 2011), Kos Island, Greece, 29 September - 3 Octomber 2011. pp. 62-65. Available from: http://history.icininfo.net/2011/FileStore/procs_INFO_2011.pdf	en
heal.abstract	Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS [1] and PATSTAT [2]. Unlike most previous research, our emerging technology trend classifier utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Integrated Information (IC-ININFO 2011)	en
heal.conferenceItemType	full paper