Text Mining for the Semantic Web

Encyclopedia of Machine Learning ◽

10.1007/978-0-387-30164-8_829 ◽

2011 ◽

pp. 978-980

Author(s):

William Uther ◽

Dunja Mladenić ◽

Massimiliano Ciaramita ◽

Bettina Berendt ◽

Aleksander Kołcz ◽

...

Keyword(s):

Text Mining ◽

Semantic Web

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Text Mining for the Semantic Web

Encyclopedia of Machine Learning and Data Mining ◽

10.1007/978-1-4899-7687-1_835 ◽

2017 ◽

pp. 1262-1263

Author(s):

Marko Grobelnik ◽

Dunja Mladenić ◽

Michael Witbrock

Keyword(s):

Text Mining ◽

Semantic Web

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Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics

Autonomous Intelligent Systems: Multi-Agents and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72839-9_2 ◽

2007 ◽

pp. 11-27

Author(s):

Vladimir Khoroshevsky ◽

Irina Efimenko ◽

Grigory Drobyazko ◽

Polina Kananykina ◽

Victor Klintsov ◽

...

Keyword(s):

Text Mining ◽

Semantic Web ◽

Web Text Mining

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Text-Mining-Methoden im Semantic Web

HMD Praxis der Wirtschaftsinformatik ◽

10.1007/bf03340436 ◽

2010 ◽

Vol 47 (1) ◽

pp. 35-46

Author(s):

Gerold Schneider ◽

Heinrich Zimmermann

Keyword(s):

Text Mining ◽

Semantic Web

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A method for automatic construction of learning contents in semantic web by a text mining approach

International Journal of Knowledge and Learning ◽

10.1504/ijkl.2006.009681 ◽

2006 ◽

Vol 2 (1/2) ◽

pp. 89 ◽

Cited By ~ 4

Author(s):

Hsin-Chang Yang

Keyword(s):

Text Mining ◽

Semantic Web ◽

Automatic Construction

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Text Mining-Based Semantic Web Architecture (TMSWA) for e-Learning Systems

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2014.v4.433 ◽

2014 ◽

Vol 4 (4) ◽

pp. 333-338

Author(s):

Hamad Ibrahim Alomran

Keyword(s):

Text Mining ◽

Semantic Web ◽

Learning Systems ◽

Web Architecture ◽

E Learning

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A multilingual text-mining approach based on self-orginizing maps for semantic web mining

IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2002.1173454 ◽

2005 ◽

Cited By ~ 1

Author(s):

Chung-Hong Lee ◽

Hsin-Chang Yang

Keyword(s):

Text Mining ◽

Semantic Web ◽

Web Mining ◽

Multilingual Text

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Exploring the digital humanities research agenda: a text mining approach

Journal of Documentation ◽

10.1108/jd-03-2021-0066 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Soohyung Joo ◽

Jennifer Hootman ◽

Marie Katsurai

Keyword(s):

Text Mining ◽

Semantic Web ◽

Digital Media ◽

Digital Humanities ◽

Topic Models ◽

Open Data ◽

Recent Decade ◽

Research Trends ◽

Large Set ◽

Content Type

PurposeThis study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.Design/methodology/approachResearch bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.FindingsTerm-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.Originality/valueThis study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.

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