Similarity-Based Semantics Searching in Super-Peer Network Model
Ge Yu, Yan Ting · 2010
On researching the key facts in unstructured P2P searching technologies and analyzing their limitations, this paper proposes a Similarity-based Semantics Searching in Super-Peer Network Model. It gathers peers with the same interests into a similar semantic field. Then divides nodes into two types: super-nodes and ordinary-nodes, the super-node manages ordinary-nodes which are in the same field. When search requests are advanced, firstly look in the same region, if failed, then the super-node will forward the request to the highest semantic similarity to the other super-nodes, thus improving the efficiency if searching and the hit rate. Simulation results prove the effectiveness and efficiency of the proposed searching model.