|
Title:
|
LEARNING SYNONYM RELATIONS FROM FOLKSONOMIES |
|
Author(s):
|
Alex Rêgo, Leandro Marinho, Carlos Eduardo Pires |
|
ISBN:
|
978-989-8533-09-8 |
|
Editors:
|
Bebo White and Pedro Isaías |
|
Year:
|
2012 |
|
Edition:
|
Single |
|
Keywords:
|
Folksonomy, Machine Learning, Synonym Detection, Tag, Semantics |
|
Type:
|
Full Paper |
|
First Page:
|
273 |
|
Last Page:
|
280 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Folksonomies such as Delicious, Flickr, and BibSonomy are now widespread with thousands of users using them daily to upload, tag, and retrieve on-line resources (e.g., web pages, photos, and videos). Tags are keywords freely chosen by users that reflect the vocabulary employed by these users to annotate resources. The automatic detection of semantic relations (e.g. synonym, hypernym, and hyponym) between tags may improve the ability of users in finding relevant resources, e.g., queries may be expanded with semantically related tags. Several works in the literature propose approaches to automatically detect semantic relations between tags in folksonomies. In general, these works adopt similarity/distance measures (e.g. edit distance, tag-tag co-occurrence, and cosine) as heuristics for semantic similarity detection. In this paper, we present a more principled approach that learns a pairwise decision model for predicting synonym relations between pairs of tags directly from folksonomy data. The features are extracted from pairs of tags through the application of similarity/distance measures that capture the different aspects of a synonym relation. We conduct a comprehensive set of experiments on a recent snapshot of BibSonomy, a real-world social tagging system, and show that our approach is up to 8.1% superior than the best performing baseline. |
|
|
|
|
|
|
