Patent Query Formulation by Synthesizing Multiple Sources of Relevance Evidence

Author:

Mahdabi Parvaz1,Crestani Fabio1

Affiliation:

1. University of Lugano, Switzerland

Abstract

Patent prior art search is a task in patent retrieval with the goal of finding documents which describe prior art work related to a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Fortunately, other relevance evidence sources (i.e., classification tags and bibliographical data) provide additional details about the underlying information need. In this article, we propose a unified framework that integrates multiple relevance evidence components for query formulation. We first build a query model from the textual fields of a query patent. To overcome the term mismatch, we expand this initial query model with the term distribution of documents in the citation graph, modeling old and recent domain terminology. We build an IPC lexicon and perform query expansion using this lexicon incorporating proximity information. We performed an empirical evaluation on two patent datasets. Our results show that employing the temporal features of documents has a precision enhancing effect, while query expansion using IPC lexicon improves the recall of the final rank list.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM;Mobile Information Systems;2021-12-28

2. Patent Analytic Citation-Based VSM: Challenges and Applications;IEEE Access;2020

3. Patent expanded retrieval via word embedding under composite-domain perspectives;Frontiers of Computer Science;2019-06-17

4. Patent retrieval: a literature review;Knowledge and Information Systems;2019-01-14

5. Query Oriented Extractive-Abstractive Summarization System (QEASS);Proceedings of the ACM India Joint International Conference on Data Science and Management of Data;2019-01-03

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