|
Title:
|
ONTOLOGY CONCEPT ENRICHMENT VIA TEXT MINING |
|
Author(s):
|
Qiang Wang, Susan Gauch, Hiep Luong |
|
ISBN:
|
978-972-8939-31-1 |
|
Editors:
|
Piet Kommers, Tomayess Issa and Pedro Isaías |
|
Year:
|
2010 |
|
Edition:
|
Single |
|
Keywords:
|
Ontology Enrichment, Text Mining, Similarity Computation, WordNet, Amphibian Morphology |
|
Type:
|
Full Paper |
|
First Page:
|
147 |
|
Last Page:
|
154 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
A critical problem for ontology enrichment is how to accurately add new vocabulary to an existing ontology while remaining rational and coherent. A common existing approach uses WordNetbased similarity calculation algorithms to identify and add related words. However, this approach does not work well when applied to a narrow domain because (i) many domain-specific words are missing from the WordNet database and (ii) many words are ambiguous and appear multiple times, once per sense. In this paper, we present a similarity computation method based on text mining. We efficiently determine the relatedness between two words by comparing the contexts in which the words appear within documents related to the domain ontology. Experimental results using an amphibian ontology show that the similarity computation method is more accurate than the WordNet-based approaches and that we are able to employ automatic techniques to add newly mined vocabulary to the ontology. |
|
|
|
|
|
|
