ontologizing concept maps using graph theory
Given the new challenges of open and unsupervised information extraction, there is a need to identify important and relevant knowledge structures (concepts and relationships) in the vast amount of extracted data and to filter the noise that results from unsupervised information extraction. This is generally referred to as the ontologization task. This paper uses measures from graph theory to identify these key elements such as Page Rank, Betweenness, and Degree. We also propose a combination of metrics for ranking concepts and relationships. Our approach shows effective results in terms of precision compared to other standard measures for weighting concepts and relationships such as TF*IDF or frequency of co-occurrences.
ontologization ontologization graph theory metrics precision concept and relation importance ontology