@inproceedings{5e2c58b3d7654c3f8922bfee4c06ec43,
title = "A comparison of unsupervised taxonomical relationship induction approaches for building ontology in RDF resources",
abstract = "Automatically generated ontology can describe the relationship of meta-data in Linked Data or other RDF resources generated from programs, and advances the utility of the data sets. Hierarchical document clustering methods used to generate concept hierarchies from retrieved documents or social tags can be used for constructing taxonomy or ontology for Linked Data and RDF documents. This paper introduces a framework for building an ontology using the hierarchical document clustering methods and compares the performance of three classic algorithms that are UPGMA, Subsumption, and EXT for building the ontology. The experiment shows EXT is the best algorithm to build the ontology for RDF resources and demonstrates that the quality of the ontology generated can be affected by the number of concepts that are used to represent the entities and to formalize the classes in the ontology.",
keywords = "Hierarchy generation, Linked data, Ontology generation, RDF, Taxonomical relationship induction",
author = "Nansu Zong and Sungin Lee and Kim, {Hong Gee}",
note = "Funding Information: This research was funded by the MSIP(Ministry of Science, ICT & Future Planning), Korea in the ICT R&D Program 2013. We appreciate Ashley Hess for the proof reading. ; 3rd Joint International Semantic Technology Conference, JIST 2013 ; Conference date: 28-11-2013 Through 30-11-2013",
year = "2014",
doi = "10.1007/978-3-319-06826-8_32",
language = "English (US)",
isbn = "9783319068251",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "445--459",
booktitle = "Semantic Technology - Third Joint International Conference, JIST 2013, Revised Selected Papers",
}