Reducing Noises for Recall-Oriented Patent Retrieval

Patents have been considered as key enablers for many knowledge- and information-centric companies as well as institutes. The higher the required patent capability, the more important is the need for an effective and efficient patent search system. Many conventional patent search systems produce unsatisfactory results for patent queries because the inherent search systems come from traditional keyword-based models, which inevitably lead to too many unrelated items in the search results. Consequently, these systems cost the patent experts lots of time to iteratively refine search results manually. In this paper, we propose a specialized patent-searching method, in which relationships between the keywords within each document and their implication for each patent document are investigated. With this elaborated ranking capability, keywords for valid patents are placed in higher ranks and those for noise patents are placed in sub-ranked data positions. As a benefit, our method significantly eliminates noisy data from the search results. Hence, our method is very useful for recall-oriented search for patents. Experimental results with real-life datasets show that our method outperformed many conventional patent search systems with respect to time and cost.