On multiword entity ranking in peer-to-peer search

Yuval Merhav, Ophir Frieder

Abstract: Previously [2], we postulated the advantage of using entity extraction to implement a new Peer-to-Peer (P2P) search framework for reducing network traffic and providing a trade off between precision and recall. We now propose an entity ranking method designed for the 'short documents' characteristic of P2P, which significantly improves both precision and recall in 'top results' P2P search. We construct a dynamic entity corpus using n-grams statistics and metadata, study its reliability, and use it to identify correlations between user query terms.