Multifold Concept Relationships Metrics

How to establish the relationship between concepts based on the large scale real-world click data from commercial engine is a challenging topic due to that the click data suffers from the noise such as typos, the same concept with different queries etc. In this paper, we propose an approach for automatically establishing the concept relationship. We first define five specific relationships between concepts and leverage them to annotate the images collected from commercial search engine. We then extract some conceptual features in textual and visual domain to train the concept model. The relationship of each pairwise concept will thus be classified into one of the five special relationships. Experimental results demonstrate our proposed approach is more effective than Google Distance.