title: A knowledge-driven approach to biomedical document conceptualization.
abstract: OBJECTIVE
  Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively.
  
  
  METHODS
  We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents.
  
  
  RESULTS
  Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures.
  
  
  CONCLUSIONS
  The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision.
authors.affiliation: Tsinghua-Southampton Web Science Laboratory at Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China. zheng.haitao@sz.tsinghua.edu.cn
authors.forename: Hai-Tao
authors.initials: HT
authors.lastname: Zheng
authors.forename: Charles
authors.initials: C
authors.lastname: Borchert
authors.forename: Yong
authors.initials: Y
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003196:Computer Graphics; D057225:Data Mining; D003628:Database Management Systems; D030541:Databases, Genetic; D046650:Medical Subject Headings; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D012660:Semantics; D014584:User-Computer Interface; D018875:Vocabulary, Controlled