Cluster-Based Patent Retrieval Using International Patent Classification System

Abstract

A patent collection provides a great test-bed for cluster-based information retrieval. International Patent Classification (IPC) system provides a hierarchical taxonomy with 5 levels of specificity. We regard IPC codes of patent applications as cluster information, manually assigned by patent officers according to their subjects. Such manual cluster provides advantages over automatically built clusters using document term similarities. There are previous researches that successfully apply cluster-based retrieval models using language modeling. We develop cluster-based language models that employ advantages of having manually clustered documents.