Text Mining for the Semantic Web

2017 ◽  
pp. 1262-1263
Author(s):  
Marko Grobelnik ◽  
Dunja Mladenić ◽  
Michael Witbrock
Keyword(s):  
Text Mining ◽  
Semantic Web

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

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

scholarly journals Text-Mining-Methoden im Semantic Web

2010 ◽  
Vol 47 (1) ◽  
pp. 35-46
Author(s):  
Gerold Schneider ◽  
Heinrich Zimmermann
Keyword(s):  
Text Mining ◽  
Semantic Web

scholarly journals Exploring the digital humanities research agenda: a text mining approach

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.

Ontologies and Text Mining as a Basis for a Semantic Web for the Life Sciences

2006 ◽  
pp. 164-183
Author(s):  
Andreas Doms ◽  
Vaida Jakonienė ◽  
Patrick Lambrix ◽  
Michael Schroeder ◽  
Thomas Wächter
Keyword(s):  
Text Mining ◽  
Semantic Web ◽  
Life Sciences

Text Mining for the Semantic Web

2011 ◽  
pp. 978-980
Author(s):  
William Uther ◽  
Dunja Mladenić ◽  
Massimiliano Ciaramita ◽  
Bettina Berendt ◽  
Aleksander Kołcz ◽  
...  
Keyword(s):  
Text Mining ◽  
Semantic Web

Text Mining for the Semantic Web

2016 ◽  
pp. 1-3
Author(s):  
Marko Grobelnik ◽  
Dunja Mladenić ◽  
Michael Witbrock
Keyword(s):  
Text Mining ◽  
Semantic Web
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