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Multi-path traces in semantic graphs for latent knowledge elicitation

In this work an online collaborative semantic network is explored. The method used is based on multi-path traces for extracting latent contextual knowledge, which explores an unknown Semantic proximity measures based on search engines are uses as heuristics to navigate the collaborative network, in order to find multiple random paths representing traces between seed concepts. The exploration is driven by an online randomized walk informed by those heuristics, where the multiple traces model reinforces the most relevant explanatory paths using a pheromone-like approach to elicit latent contexts. Experiments have been held on Wikipedia and on the Word Similarity 353 dataset to evaluate the effectiveness of the method. The general methodology can be easily extended to other online collaborative graphs and to non-textual domains.