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In activity recognition, one major challenge is huge manual effort in labeling when a new domain of activities is to be tested. In this paper, we ask an interesting question: can we transfer the available labeled data from a set of existing activities in one domain to help recognize the activities in another different but related domain? Our answer is "yes", provided that the sensor data from the two domains are related in some way. We develop a bridge between the activities in two domains by learning a similarity function via Web search, under the condition that the sensor data are from the same feature space. Based on the learned similarity measures, our algorithm interprets the data from the source domain as the data in the domain with different confidence levels, thus accomplishing the cross-domain knowledge transfer task. Our algorithm is evaluated on several real-world datasets to demonstrate its effectiveness.