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High-throughput techniques for protein-protein interaction detection in a genomic scale have provided us a genomic wide view of molecular interactions of many living organisms. A few approaches were proposed to scrutinize protein-protein interactions of living organisms. By the way, the binary nature of the current protein interaction data sets imposes challenges for effective analysis. Furthermore, their performance was suffered by the intrinsic defect, i.e., high noise level, of high-throughput data. This unpleasantly high false positive rate could lead many devoted researches to erroneous biological conclusions. We propose a novel reliability measurement for protein interactions integrating the similarity in gene ontology and the topological similarity in protein interaction networks. Our metric has been proven to be an effective reliability metric for identifying biologically more reliable interactions through the analyses performed from various view points, e.g., functional homogeneity, subcellular localizational homogeneity, and gene expression correlation, etc.