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Open ontology learning is the process of extracting a domain ontology from a knowledge source in an unsupervised way. Due to its unsupervised nature, it requires filtering mechanisms to rate the importance and correctness of the extracted knowledge. This paper presents OntoCmaps, a domain-independent and open ontology learning tool that extracts deep semantic representations from corpora. OntoCmaps generates rich conceptual representations in the form of concept maps and proposes an innovative filtering mechanism based on metrics from graph theory. Our results show that using metrics such as Betweenness, PageRank, Hits and Degree centrality outperforms the results of standard text-based metrics (TF-IDF, term frequency) for concept identification. We propose voting schemes based on these metrics that provide a good performance in relationship identification, which again provides better results …