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Aims and Scope

The rapid growth of online available scientific, technical, and legal data such as patents, reports, articles, etc. has made the large-scale analysis and processing of such data a crucial task. Today, scientists, patent experts, inventors, and other information professionals (e.g., information scientists, lawyers, etc.) contribute to this data every day by publishing articles, writing technical reports, or patent applications.

It is a challenging task to process, analyze, and explore documents due to their length, the use of domain-specific vocabulary, and the complexity introduced by targeting various scientific fields and domains. Documents are semi-structured and cover unstructured textual parts as well as structured parts such as tables, mathematical formulas, diagrams, and domain-specific information such as chemical names, bio-sequences, etc.

Such kind of information brings complexity in processing such documents; however, data is the lifeblood of many applications, and its preservation, analysis, enrichment, and use are key for applications in several domains. In order to benefit from the scientific-technical knowledge present in such documents, e.g., for decision-making or for professional search and analytics, there is an urgent need for analyzing, enriching, and linking such data by employing state-of-the-art Semantic Web technologies and AI methods.

However, as they are heterogeneous and are written using domain-specific terminology applying the existing semantic technologies is not straightforward. To address the challenges mentioned above, Semantic Web Technologies, Natural Language Processing (NLP) techniques, Deep Neural Networks (DNN), and Large Language Models (LLMs) must be leveraged in order to provide efficient and effective solutions for creating easily accessible and machine-understandable knowledge.

Abstract deadline

March 1th, 2024

March 12th, 2024

Contact us if you did not make it on time!

Paper deadline

March 7th, 2024

March 17th, 2024


April 4th, 2024

Camera-ready Paper

April 18th, 2024

Workshop Topics

The workshop accepts contributions in all topics related to semantic web technologies and deep learning focused (but not limited) to:

  • Data Collection
    • Leveraging Large Language Models (LLMs) for generating scientific, technical, and legal data.
    • New tools and systems for capturing scientific, technical, and legal data such as scientific articles, patent publications, etc.
    • Procedures and tools for storing, sharing, and preserving data.
    • Collecting and sharing data sets such as benchmarks, etc.
    • Pipelines and protocols to capture peculiarities from data.
    • Employing Semantic Web Technologies to represent and preserve sensitive data in terms of ethics, privacy, security, trust, etc.
  • Novel Semantic Technologies for scientific, technical and legal data
    • Ontologies and annotation schema to model such data.
    • Annotation, linking and disambiguation of the data.
    • Knowledge graph construction.
    • LLMs to generate metadata, vocabularies, ontologies, and semantic models for specific data.
  • Applications for patents, scientific, technical and legal data by exploiting semantic technologies
    • Exploiting knowledge graphs to drive document similarity, question answering, search etc.
    • Recommender systems.
    • Semantic content-based retrieval.
    • Natural language processing techniques for classification, summarization, etc.
    • Exploratory search using semantic technologies on scientific, technical, and legal data.
    • Key enabling tools (also based on LLMs) for semantic technologies on specific data and domains.
    • Applications based on Generative AI and LLMs.
    • Lessons learned or/and use cases both from academia and industry around semantic models and LLMs for data in specific domains.


The submissions must be in English and adhere to the CEUR-WS one-column template (see Session 2: The New CEURART Style). The papers should be submitted as PDF files to EasyChair. The review process will be single-blind. Please be aware that at least one author per paper must be registered and attend the workshop to present the work and that ESWC is a 100% in person conference.

We will consider three different submission types:

  • Full Research Papers (8-12 pages) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature is encouraged.
  • Short Papers (5-7 pages) should describe significant novel work in progress. Compared to full papers, their contribution may be narrower in scope, be applied to a narrower set of application domains, or have weaker empirical support than that expected for a full paper. Submissions likely to generate discussions in new and emerging areas of legal data are encouraged.
  • Position or Industry Papers (3-5 pages) should introduce new points of view in the workshop topics or summarize the experience of a group in the field.
  • Extended Abstracts (1-2 pages) should introduce an ongoing work that aligns with the main scope of the workshop.

Submissions should not exceed the indicated number of pages, including any diagrams and references.

Each submission will be reviewed by three independent reviewers on the basis of relevance for the workshop, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references and reproducibility.

The accepted papers will be available on the Workshop website. The proceedings will be published in a CEUR-WS volume and consequently indexed on Google Scholar, DBLP, and Scopus.


All the information to register and attend the workshop can be found on the ESWC registration page.


SemTech4STLD workshop will take place on May 26th, 2024.

Timing Content
14:00 14:05
Opening & Welcome
14:05 14:50 Keynote and Q&A on Understanding Scientific and Societal Adoption of Scientific Knowledge and Resources Through NLP and Knowledge Graphs

Speaker: Prof. Dr. Stefan Dietze
GESIS – Leibniz Institute for the Social Sciences & Heinrich-Heine-University Düsseldorf

Abstract: Scientific discourse is scattered across unstructured scholarly publications and increasingly takes place online, e.g. in news or social media. Understanding the state-of-the-art in specific research fields, involved data, software, or methods, and their impact on both science and society requires substantial efforts and has become increasingly challenging. At the same time, societal debates about topics such as COVID or climate change have demonstrated the impact of science discourse on public opinion, policies, and society as a whole. This talk will provide an overview of a range of works that use deep learning-based NLP, such as PLMs and LLMs, to construct and use knowledge graphs about scientific discourse. These include, on the one hand, approaches that extract metadata about scholarly entities, such as code, data, tasks or machine learning models from scientific publications to enable machine-interpretable research information and understand dependencies between scholarly artefacts. On the other hand, we introduce NLP methods and knowledge graphs that enable an understanding of societal discourse about science, e.g. on Twitter/X, and facilitate interdisciplinary research into (mis-)representation and -information of scientific claims and findings in societal debates.

Short Bio: Stefan Dietze is Professor for Data & Knowledge Engineering at Heinrich-Heine-University Düsseldorf (HHU), and scientific director of the Department of Knowledge Technologies for the Social Science (KTS) at GESIS - Leibniz Institute for the Social Sciences. He also is deputy director at the Heine Center for Artificial Intelligence & Data Science (HeiCAD), and an affiliated member at the Düsseldorf Institute for Internet & Democracy (DIID) and the L3S Research Center of the Leibniz University Hanover, Germany. His research interests are at the intersection of information retrieval, knowledge graphs, and NLP and his work is concerned with the extraction, fusion and search of knowledge and data, in particular, on the Web. His work has been published in top-tier conferences such as CIKM, EMNLP, ISWC, SIGIR, NAACL, or WebConf, where he also frequently serves as PC and/or organization committee member.

14:50 15:30
Paper Session I
Paper I: GerPS-NER: A Dataset for Named Entity Recognition to Support Public Service Process Creation in Germany
Leila Feddoul, Sarah T. Bachinger, Clara Lachenmaier, Sebastian Apel, Pirmin Karg, Norman Klewer, Denys Forshayt, Robin Erd and Marianne Mauch, (12 min + 3 Q&A)
Paper II: Automating Citation Placement with Natural Language Processing and Transformers
Davide Buscaldi, Danilo Dessì, Enrico Motta, Marco Murgia, Francesco Osborne and Diego Reforgiato, (10 min + 3 Q&A)
Paper III: Combining Knowledge Graphs and Large Language Models to Ease Knowledge Access in Software Architecture Research
Angelika Kaplan, Jan Keim, Marco Schneider, Anne Koziolek and Ralf Reussner, (10 min + 3 Q&A)
15:30 16:00
Coffee Break
16:00 16:35
Invited Talk and Q&A on Semantic Web and Machine Learning Systems for Intelligent Systems in Complex Domains

Speaker: Prof. Dr. Marta Sabou
Vienna University of Economics and Business (WU)

Abstract: Creating intelligent applications that valorise complex domain data such as in the scientific, technical, and legal domain often calls for solutions that combine learning and symbolic artificial intelligence (AI) methods. In line with such developments, in the first part of this talk, we focus on describing a new sub-area of AI that focuses on combining Machine Learning components with techniques developed by the Semantic Web community—Semantic Web Machine Learning (SWeML). We report on the results of a systematic mapping study during which we analysed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features of such systems. In the second part of the talk, we describe the development and evaluation of a concrete SWeML system that aims to extract key elements from official Austrian permits, including the Issuing Authority, the Operator of the facility in question, the Reference Number, and the Issuing Date. We hope that our lessons learned both about this area as a whole (through the survey of SWeML systems) and the concrete system we built will provide inspiration for researchers and practitioners working with such complex data as in the legal domain and beyond.

Short Bio: Prof. Dr. Marta Sabou is a professor for Information Systems and Business Engineering at the Vienna University of Economics and Business (WU) and the Head of Institute for Data, Process and Knowledge Management (DPKM). She holds a PhD in Artificial Intelligence from Vrije Universiteit Amsterdam, for which she won the IEEE Intelligent System’s Ten to Watch Award in 2006. During her career, she performed Artificial Intelligence (AI) research as Research Fellow at the Open University UK, Assistant Professor at MODUL University Vienna, Key Expert in Semantic Technologies at Siemens and FWF Elise-Richter Fellow at the Vienna University of Technology.

Prof. Sabou leads the Semantic Systems research group, which performs foundational and applied research at the intersection of the Semantic Web, Machine Learning and Human Computation research areas. Her group’s research topics range from knowledge engineering (knowledge graphs and their evaluation, data integration) to the development of novel intelligent systems that combine both symbolic and sub-symbolic AI techniques, i.e., neuro-symbolic systems. This foundational research underpins an active involvement in applied research in terms of developing advanced functionalities (e.g., system explainability and auditability) in application areas ranging from tourism and cultural heritage to mission critical domains enabled by complex cyber-physical (social) systems such as smart grids, smart buildings, smart factories (as part of Industry 4.0-5.0). Increasingly, the group addresses topics in the area of Digital Humanism such as the auditing of AI systems and the involvement of human stakeholders in the design of intelligent systems. Prof. Sabou is an accomplished academic (close to 150 peer-reviewed papers, h-index 46) and takes an active role in the Semantic Web research community as an editorial board member for two journals (SWJ, NAI) and conference organiser.

16:35 17:50
Paper Session II
Paper I: Extracting licence information from web resources with a Large Language Model
Enrico Daga, Jason Carvalho and Alba Catalina Morales Tirado. (12min + 3Q&A)
Paper II: ChatGPT vs. Google Gemini: Assessing AI Frontiers for Patent Prior Art Search Using European Search Reports
Renukswamy Chikkamath, Ankit Sharma, Christoph Hewel and Markus Endres. (12min + 3Q&A)
Paper IV: Investigating Environmental, Social, and Governance (ESG) Discussions in News: A Knowledge Graph Analysis Empowered by AI
Simone Angioni, Sergio Consoli, Danilo Dessì, Francesco Osborne, Diego Reforgiato and Angelo Salatino. (12min + 3Q&A)
17:45 18:00
Closing --- Presentations


Workshop Chairs

Program Committee


For general inquiries on the workshop, please send an email to: semtech4stld24@easychair.org