TY - GEN
T1 - A comparative study of ontology based term similarity measures on PubMed document clustering
AU - Zhang, Xiaodan
AU - Jing, Liping
AU - Hu, Xiaohua
AU - NG, Kwok Po
AU - Zhou, Xiaohua
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.
AB - Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.
KW - Document clustering
KW - Domain ontology
KW - Semantic similarity measure
UR - http://www.scopus.com/inward/record.url?scp=38049162356&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71703-4_12
DO - 10.1007/978-3-540-71703-4_12
M3 - Conference contribution
AN - SCOPUS:38049162356
SN - 9783540717027
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 126
BT - Advances in Databases
PB - Springer Verlag
T2 - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007
Y2 - 9 April 2007 through 12 April 2007
ER -