Clustering cliques for graph-based summarization of the biomedical research literature

• Published in: BMC bioinformatics Vol. 14; no. 1; p. 182
• Main Authors: Zhang, Han, Fiszman, Marcelo, Shin, Dongwook, Wilkowski, Bartlomiej, Rindflesch, Thomas C
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.06.2013; BioMed Central
• Subjects: Algorithms; Biomedical Research; Cluster Analysis; Computational biology; Computational linguistics; Data mining; Data Mining - methods; Electronic data processing; Evaluation; Language processing; Medical Subject Headings; Methods; Natural language interfaces; PubMed; Semantics; Denmark; United States; China
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-182

Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively.