The PatentSemTech'24 workshop aims to establish a long-term collaboration and a two-way communication channel between the IP industry and academia from relevant fields such as natural language processing (NLP), text and data mining (TDM), and semantic technologies (ST) in order to explore and transfer new knowledge, methods, and technologies for the benefit of industrial applications as well as support research in applied sciences for the IP and neighbouring domains.

PatentSemTech'24 will be held as a full-day event in conjunction with SIGIR'24.

Workshop website: http://ifs.tuwien.ac.at/patentsemtech/

Submission Guidelines

Submissions must be in English, in PDF, and in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website:https://www.acm.org/publications/proceedings-template ("sigconf" template for LaTeX; Interim Template for Word).

• Submissions should be at most 8 (full) or 4 (short) pages (including figures and references) in length.
• Submissions should be submitted electronically via EasyChair:https://www.easychair.org/conferences/?conf=patentsemtech2024.
• At least one author of each accepted paper is required to register for, and present the work in person at the workshop.

Important Dates:

• Submission deadline: April 25, 2024 Extended 'til April 29, AoE
• Acceptance notification: May 29 (updated), 2024
• PatentSemTech'24 workshop: July 18, 2024

List of Topics

We encourage submissions of high quality research papers on all topics related to the IP domain. Topics of interest include (but are not limited to):

• Text mining and retrieval from patents, legal documents, or other scientific-technical information sources
• Machine learning methods applied to patent data, in particular deep learning methods for 
  • Representation learning (word and document embeddings) 
  • Language models e.g. BERT, LLMs
  • Query expansion
  • Clustering and classification
  • Recommendation
  • IPC/CPC prediction
  • Trend detection
  • Entity extraction
• Semantic approaches for 
  • Linking semantic information
  • Integrating external knowledge sources
  • Semantic enrichment
• Methods and applications for retrieving, mining, and analysing, including 
  • Patent landscaping 
  • Hot spot / White spot analysis
  • Multi-modal analysis
  • Technology trend analysis
  • Innovative user interfaces
  • Visual user interface concepts

Contributions

We solicit two types of submissions: full papers and short papers for three tracks: research, demo, and summarization task. Full papers will be limited to 8 pages (including references); short papers will be 4 pages (including references).

The submissions will be peer-reviewed (no double-blind) by at least two program committee members and evaluated based on innovativeness, novelty, interestingness, and impact. We plan for three tracks:

• Research Track
  • For this track, we solicit contributions from academia that present
    • Novel applications of existing state of the art methods for the IP domain
    • Novel methods or tasks in the IP domain
    • Novel user interfaces for the IP domain
    • Novel evaluation or analysis insights in the IP domain
    • Novel benchmark datasets or other resources of interest
    • A survey or overview related to a particular task in the IP domain
• Demo/System Track
  • We solicit demos, case study, insights, or novel ideas from industry that present
  • Focused case studies making use of semantic technologies or machine learning
  • Interesting IP-related task descriptions or best practices for patent analysis
  • In-use systems or prototype implementations of semantic technologies
  • Demos on processing or analysing data from the IP domain, or user interfaces
  • In-use resources related to patents or external resources, e.g., linked open data.
• Dataset Track: 
  • The Dataset track presents an open-application challenge where participants can explore various tasks, including text segment classification, automatic technical terminology recognition, and summarization.
  • We will provide a semi-annotated dataset referred to as the Open Innovation Ecosystem Dataset (EcoNLP-AI-GP), comprising 20,000 patents and scientific publications from the AI and Green plastic domain. Each document in the collection has been segmented into text tiles using discourse analysis techniques such as Texttiling and sentence classification.
  • The primary objective is to identify sentences discussing problems, solutions, contradictions, or prior art.
  • All terms within each text tile have also been labeled based on their relevance to the text tile’s topic, scientific domain, and taxonomy categories.
  • These categories span various areas including Substances, Processes, Drugs, Polymers, Chemical classes, and Nutrition.

Committees

Program Committee

• Simone Ponzetto, University of Mannheim
• Hans-Peter Zorn, inovex GmbH
• Rene Hackl-Sommer, DeepL
• Christoph Hewel, Paustian and Partner, Munich
• Tobias Fink, TU Wien
• Anthony    Trippe, Patinformatics, LLC
• Florian Matthes, TU Munich
• Ian Wetherbee, Google
• Alexander Klenner-Bajaja, EPO
• Mike Salampasis, International Hellenic University
• Karin Verspoor, RMIT University in Melbourne

Organizing committee

• Ralf Krestel (ZBW & CAU Kiel, Germany)
• Hidir Aras (FIZ Karlsruhe, Germany)
• Linda Andersson (Artificial Researcher, Austria)
• Florina Piroi (Data Science Studio, RSA FG, Austria)
• Allan Hanbury (TU Wien, Austria)
• Dean Alderucci (CMU, USA)

Invited Speakers

• TBA

Publication

Accepted papers will be published as CEUR (ceur-ws.org) proceedings. Selected contributions will be invited to submit extended, full papers to Elsevier’s World Patent Information (WPI) journal: https://www.journals.elsevier.com/world-patent-information/

Venue

The workshop will be held in conjunction with SIGIR'24, Washington D.C. USA, July 14-18, 2024.

Contact

All questions about submissions should be emailed to: rkr@informatik.uni-kiel.de and hidir.aras@fiz-karlsruhe.de