The effect of citation analysis on query expansion for patent retrieval

Abstract

Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents.

Query expansion techniques for information retrieval: A survey

“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

A study of query reformulation for patent prior art search with partial patent applications

“…The results showed the advantage of using the term distribution of the cited documents together with the publication dates. In [15] authors propose to build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. They identify the important documents in the topic dependent citation graph using a citation analysis measure.…”

Patent retrieval: a literature review

“…Patents are not only textual documents, they contain lot of non-textual metadata and bibliographic information as well (e.g., citations, tables, formulas, drawings, classification...etc). Combining metadata analysis with text-based PR has shown improvements in performance in the literature Fujii (2007); Romary (2009, 2010); Eisinger et al (2013); Mahdabi and Crestani (2014a). Metadata features are also language independent making them advantageous when used for CLIR.…”

“…textbfHybrid: these methods utilize various sources of metadata to improve PR performance. Mahdabi and Crestani (2014c) built upon previous work in Mahdabi et al (2011) and Mahdabi and Crestani (2014a) and proposed a query expansion method that utilizes time-aware random walk on a weighted patent citations network. Citation weights are derived from various metadata (e.g., classification codes, inventors, assignee...etc).…”