Abstract
A natural semantic network (NSN) represents the knowledge of a group of persons with respect to a particular topic. NSN comparison would allow to discover how close one group is to the other in terms of expertise in the topic— for example, how close apprentices are to experts or students to teachers. We propose to model natural semantic networks as weighted bipartite graphs and to extract feature vectors from these graphs for calculating similarity between pairs of networks. By comparing a set of networks from different topics, we show the approach is feasible.
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Torres, F., Garza, S.E. (2014). Similarity for Natural Semantic Networks. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_18
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DOI: https://doi.org/10.1007/978-3-319-11988-5_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11987-8
Online ISBN: 978-3-319-11988-5
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