We study the problem of Entity Resolution (ER) with limited information. ER is the problem of identifying and merging records that represent the same real-world entity. In this paper, we focus on the resolution of a single node g from one social graph (Google+ in our case) against a second social graph (Twitter in our case). We want to find the best match for g in Twitter, by dynamically probing the Twitter graph (using a public API), limited by the number of API calls that social systems allow. We propose two strategies that are designed for limited information and can be adapted to different limits. We evaluate our strategies against a naive one on a real dataset and show that our strategies can provide improved accuracy with significantly fewer API calls.
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