Log information describing the items the users have selected from the set of answers a query engine returns to their queries constitute an excel-lent form of indirect user feedback that has been extensively used in the web to improve the effectiveness of search engines. In this work we study how the logs can be exploited to improve the ranking of the results returned by an entity search engine. Entity search engines are becoming more and more popular as the web is changing from a web of documents into a \web of things". We show that entity search engines pose new challenges since their model is dierent than the one documents are based on. We present a novel framework for feature extraction that is based on the notions of entity matching and attribute frequencies. The extracted features are then used to train a ranking classier. We introduce dierent methods and metrics for ranking, we combine them with existing traditional techniques and we study their performance using real and synthetic data. The experiments show that our technique provides better results in terms of accuracy.