Knowledge Modeling in Prior Art Search

This study explores the benefits of integrating knowledge representations in prior art patent retrieval. Key to the introduced approach is the utilization of human judgment available in the form of classifications assigned to patent documents. The paper first outlines in detail how a methodology for the extraction of knowledge from such an hierarchical classification system can be established. Further potential ways of integrating this knowledge with existing Information Retrieval paradigms in a scalable and flexible manner are investigated. Finally based on these integration strategies the effectiveness in terms of recall and precision is evaluated in the context of a prior art search task for European patents. As a result of this evaluation it can be established that in general the proposed knowledge expansion techniques are particularly beneficial to recall and, with respect to optimizing field retrieval settings, further result in significant precision gains.

[1]  Susan T. Dumais,et al.  The vocabulary problem in human-system communication , 1987, CACM.

[2]  W. Kintsch.  The role of knowledge in discourse comprehension: a construction-integration model. , 1988, Psychological review.

[3]  Stephen E. Robertson,et al.  Microsoft Cambridge at TREC 13: Web and Hard Tracks , 2004, TREC.

[4]  Noriko Kando,et al.  Overview of Classification Subtask at NTCIR-6 Patent Retrieval Task , 2007, NTCIR.

[5]  Michelle Moloney,et al.  Knowledge, expectations, and inductive reasoning within conceptual hierarchies , 2004, Cognition.

[6]  C. J. van Rijsbergen,et al.  Automatically Generating Queries for Prior Art Search , 2009, CLEF.

[7]  WhartonCathleen,et al.  An overview of construction-integration model , 1991 .

[8]  W. Bruce Croft.  Approaches to Intelligent Information Retrieval , 1987, Inf. Process. Manag..

[9]  Fabio Crestani,et al.  Application of Spreading Activation Techniques in Information Retrieval , 1997, Artificial Intelligence Review.

[10]  Paul R. Cohen,et al.  Information retrieval by constrained spreading activation in semantic networks , 1987, Inf. Process. Manag..

[11]  W. J. Hutchins.  The concept of “aboutness” in subject indexing , 1997 .

[12]  Daniel G. Shapiro,et al.  RUBRIC: A System for Rule-Based Information Retrieval , 1985, IEEE Transactions on Software Engineering.

[13]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[14]  James Allan,et al.  HARD Track Overview in TREC 2003: High Accuracy Retrieval from Documents , 2003, TREC.

[15]  W. Bruce Croft.  Combining Approaches to Information Retrieval , 2002 .

[16]  Noriko Kando,et al.  Overview of Patent Retrieval Task at NTCIR-5 , 2005, NTCIR.

[17]  Stefan Bordag,et al.  Elements of knowledge-free and unsupervised lexical acquisition , 2007 .

[18]  Gerard Salton,et al.  Associative Document Retrieval Techniques Using Bibliographic Information , 1963, JACM.

[19]  Justin Zobel,et al.  Document expansion versus query expansion for ad-hoc retrieval , 2005 .

[20]  Noriko Kando,et al.  Overview of the Patent Retrieval Task at the NTCIR-6 Workshop , 2007, NTCIR.

[21]  Leif Azzopardi,et al.  A Methodology for Building a Patent Test Collection for Prior Art Search , 2008, EVIA@NTCIR.

[22]  Cathleen Wharton,et al.  An overview of construction-integration model: a theory of comprehension as a foundation for a new cognitive architecture , 1991, SGAR.

[23]  Ellen M. Voorhees,et al.  The fourteenth text retrieval conference TREC 2005 , 2006 .

[24]  G. Liedl,et al.  Draft guidelines for examination in the European Patent Office , 1977 .

[25]  Lance J. Rips,et al.  Concepts and categories: Meaning, memory, and metaphysics , 2005 .

[26]  W. Bruce Croft,et al.  An Association Thesaurus for Information Retrieval , 1994, RIAO.

[27]  Edward E. Smith,et al.  Concepts and categories: Memory, meaning, and metaphysics , 2012 .

[28]  Sebastiano Vigna,et al.  MG4J at TREC 2005 , 2005, TREC.

[29]  Stephen E. Robertson,et al.  Simple BM25 extension to multiple weighted fields , 2004, CIKM '04.

[30]  John Tait,et al.  CLEF-IP 2009: Retrieval Experiments in the Intellectual Property Domain , 2009, CLEF.