A Comparative Study of Ontology Based Term Similarity Measures on PubMed Document Clustering

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...

• Type: Conference
• Year: 2007
• Where: DASFAA
• Authors: Xiaodan Zhang, Liping Jing, Xiaohua Hu, Michael K. Ng, Xiaohua Zhou
• Keywords: DASFAA 2007, Database, Document Clustering, Semantic Similarity, Similarity Measures