Vol-3775
urn:nbn:de:0074-3775-1

Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

PatentSemTech 2024
Patent Text Mining and Semantic Technologies 2024

Proceedings of the 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2024)
co-located with the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)

Washington D.C., USA, July 28th, 2024.

Edited by

Hidir Aras *
Ralf Krestel
Linda Andersson *
Florina Piroi **
Allan Hanbury *
Dean Alderucci ****

* FIZ Karlsruhe - Leibniz Institute for Information Infrastructure , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
** ZBW - Leibniz Information Centre for Economics & Kiel University, Düsternbrooker Weg 120, 24105 Kiel, Germany
*** Artificial Researcher IT GmbH, Taubstummengasse 11 (i2c), 1040, Wien Austria
**** RSA FG Studio Data Science, Thurngasse 8/16, 1090, Vienna Austria
***** Institute of Information Systems Engineering, TU Wien, Favoritenstr. 9-11/194-04, Vienna, Austria
****** Center for AI and Patent Analysis, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

Table of Contents

Preface
Summary: There were 17 papers submitted for peer-review to this workshop. Out of these, 10 papers were accepted for this volume, 6 as full papers and 4 as short papers.

Full Papers

Multi-Task Classification Model for Multilingual Patents 1-7
Yongxin Peng, Xinyu Tong, Yueyun Zhang, Yonghe Lu
Improving Patent Classification using AI-Generated Summaries 8-17
Naoya Yoshikawa, Ralf Krestel
Semi-Supervised Learning Methods for Patent Classification Using Search-Optimized Graph-Based Representations 18-24
Ekaterina Kotliarova, Sebastian Björkqvist
Logic Mill - A Knowledge Navigation System 25-35
Sebastian Erhardt, Mainak Ghosh, Erik Buunk, Michael E. Rose, Dietmar Harhoff
Automated Patent Landscaping using Deep Learning 36-50
Sebastian Erhardt
Large Language Model Informed Patent Image Retrieval 51-60
Hao-Cheng Lo, Jung-Mei Chu, Jieh Hsiang, Chun-Chieh Cho

Short Papers

ClaimCompare: A Data Pipeline for Evaluation of Novelty Destroying Patent Pairs 61-66
Arav Parikh, Shiri Dori-Hacohen
Automatic Identification of Patent Claim Types: Enhancing Efficiency in Patent Analysis 67-71
Rima Dessi, Hidir Aras, Mark Prince
Comparing Complex Concepts with Transformers: Matching Patent Claims Against Natural Language Text 72-76
Matthias Blume, Ghobad Heidari, Christoph Hewel
Employing Active Learning for Training a DL-Model for Citation Identification in Patent Text 77-81
Farag Saad, Hidir Aras, Mark Prince

2023-11-29: submitted by Ralf Krestel, metadata incl. bibliographic data published under Creative Commons CC0
2024-10-04: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|