Workshop at ISWC 2023 on
Deep Learning for Knowledge Graphs
More Details!


Over the past years there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important tasks. KGs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. On the other hand, Deep Learning methods have also become an important area of research, achieving some important breakthrough in various research fields, especially Natural Language Processing (NLP) and Image Recognition.

In order to pursue more advanced methodologies, it has become critical that the communities related to Deep Learning, Knowledge Graphs, and NLP join their forces in order to develop more effective algorithms and applications. This workshop, in the wake of other similar efforts at previous Semantic Web conferences such as ESWC2018 as DL4KGs and ISWC2018, ESWC2019, ESWC 2020, ISWC2021, ISWC2022 aims to reinforce the relationships between these communities and foster inter-disciplinary research in the areas of KG, Deep Learning, and Natural Language Processing.

Topics of Interest

New Approaches for Combining Deep Learning and Knowledge Graphs
  • Representation Learning over Knowledge Graphs
  • Dynamic knowledge graphs
  • Large Language Models for Knowledge Graphs
  • Knowledge Graphs for Large Language Models
  • Prompt engineering and knowledge graphs
Applications of combining Deep Learning and Knowledge Graphs
  • Knowledge Graph Completion
  • Knowledge Graph Construction
  • Knowledge Graph Alignment
  • Recommender Systems
Knowledge-Based Natural Language Processing
  • Text Classification
  • Natural Language Understanding
  • Question Answering
  • Entity Linking

Keynote Speakers

Andrea Nuzzolese

Towards cognitive-based knowledge engineering

In recent years, a number of methodologies and tools have emerged in literature for building large knowledge graphs. Most of the focus on knowledge engineering (KE) techniques, i.e. the collection of activities for eliciting, capturing, conceptualising and formalising knowledge. However, KE tasks are extremely time-consuming and require a significant cognitive effort by knowledge engineers. More recently large-language models have shown promising results in many tasks related to knowledge engineering, such as knowledge extraction, ontology matching, schema induction, etc. An example is ChatGPT that can be used smoothly for generating knowledge graphs even starting from scenarios or competency questions. Following this research trend, neurosymbolic AI has been used for integrates neural and symbolic AI architectures to address the weaknesses of each in the context of KE, providing a robust AI capable of reasoning, learning, and generating knowledge graphs. But still a number of open issues remain open. One of them is about preserving the cognitive-soundness of traditional KE methodologies that were entirely human-centred. Prompting LLM with human metacognitive processes is a novel research direction that aims at instilling critical elements of human "thinking about thinking" into LLMs. The hypothesis is that metacognitive prompting can enhance the quality and cognitive-soundness of knowledge graphs resulting from KE processes.
Andrea Giovanni Nuzzolese is a Senior Researcher at the Institute of Cognitive Sciences and Technologies of the National Research Council. He obtained his Ph.D. in Computer Science in 2014 from the University of Bologna, with a focus on software methodologies and architectures for extracting Knowledge Patterns from the Web. His research interests include knowledge engineering, ontology design, linked data, the Semantic Web, knowledge extraction from heterogeneous formats, natural language understanding, and social and assistive robotics. He has participated as a researcher in the "Interactive Knowledge Stack2" (IKS) project (EU FP7) and the MARIO project (EU H2020). Currently, he is the scientific coordinator for ISTC-CNR in the Water Health Open Knowledge (WHOW) project (2019-EU-IA-0089 EU CEF Telecom - Public Open Data), aimed at designing and implementing a framework to facilitate the creation of a vast ecosystem of data related to water consumption and quality, health parameters, and disease prevalence for advanced analysis and the development of innovative services. He is the lead researcher for ISTC-CNR in the Hybrid Human Artificial Collective Intelligence in Open-Ended Domains (HACID) project (101070588 HORIZON-CL4-2021-DIGITAL-EMERGING-01-10), focused on developing a new hybrid collective intelligence for decision support for professionals dealing with complex and open problems, promoting engagement, equity, and trust. He also leads research units and is a member of the executive board of the Fostering Open Science in Social Science Research (FOSSR) project (MUR PNRR IR IR0000008), which aims to become an Italian Open Science Cloud, similar to the European Open Science Cloud project, integrating innovative services developed by the project for data collection, management, and economic and social change data analysis, following the principle of equity. He is a program committee member for semantic web and ontology engineering conferences and workshops. Additionally, he serves as a reviewer for scientific journals such as the Semantic Web Journal and the Journal of Web Semantics. He is a co-founder of BUP Srl, an innovative startup dedicated to designing and implementing solutions for creating and managing knowledge graphs.

Raphaël Troncy

CIMPLE: Hybridizing Knowledge Graph and LLM for Explaining Factors Contributing to Misinformation

In this talk, we will present some of the results of the CIMPLE project that makes use of generative AI to counter information manipulation. We present the CIMPLE Knowledge Graph that collects factchecks as well as claims and social media posts that convey some misinformation. We introduce the notion of factors that include political leaning, emotion, sentiment, the usage of propaganda and persuasion techniques or the support of conspiracy theories. We show to what extent LLM can be used to label misinformation and we present preliminary experiments that leverage knowledge graph embeddings to either identify misinformation spreaders, or to provide a finer-grained characterization of the phenomenon.
Raphaël Troncy is an Associate Professor at the Data Science Department of EURECOM, France since 2009, leading the Data-2-Knowledge team. His main research interest concerns the use of semantic web technologies for data integration, information extraction from text, tables and multimedia documents, and recommender systems. He has applied his research in diverse sectors such as Creative Industries, Cultural Heritage, Tourism, Automotive, ICT and Energy where the goal has been to produce specialized Knowledge Graphs and AI-based application for discovering and interacting with the data. He was General Chair of The Web Conference 2022 and he has published more than 250 papers.

Workshop Program and Proceedings

9:20 - 9:30 Welcome and Opening
9:30 - 10:40 Session 1
  • 9:30 - 10:10 Keynote - Andrea Nuzzolese - Towards cognitive-based knowledge engineering
  • 10:10 -10:40 Location Query Answering Using Box Embeddings - Eleni Tsalapati, Markos Iliakis, Manolis Koubarakis (paper)

10:40 - 11:20 Coffee Break
11:20 - 12:40 Session 2
  • 11:20 - 11:50 Knowledge Graph Injection for Reinforcement Learning - Robert Wardenga, Liubov Kovriguina, Dmitrii Pliukhin, Daniil Radyush, Ivan Smoliakov, Yuan Xue, Henrik Müller, Aleksei Pismerov, Dmitry Mouromtsev, Daniel Kudenko (paper)
  • 11:50 - 12:20 Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle? - Johannes Frey, Lars-Peter Meyer, Natanael Arndt, Felix Brei, Kirill Bulert (paper)
  • 12:20 - 12:40 Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain - Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato, Angelo Salatino, Luca Secchi (paper)

12:40 - 14:00 Lunch
14:00 - 15:00 Session 3
  • 14:00 - 14:30 Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals - Patryk Preisner, Heiko Paulheim (paper)
  • 14:30 - 15:00 NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods - Irene Li, Boming Yang (paper)

15:10 - 16:00 Coffee Break
16:00 - 17:00 Session 4
  • 16:00 - 16:20 Enhancing Scholarly Understanding: A Comparison of Knowledge Injection Strategies in Large Language Models - Vincenzo De Leo, Andrea Cadeddu, Alessandro Chessa, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato, Angelo Salatino, Luca Secchi (paper)
  • 16:20 - 17:00 Keynote - Raphaël Troncy

Submission Details

Papers must comply with the CEUR-WStemplate (single column)
Papers are submitted in PDF format via the workshop’s Open Review submission pages

Submissions can fall in one of the following categories:
  • Full research papers (8-10 pages)
  • Short research papers (4-6 pages)
  • Position papers (2 pages)
  • Lightning talks (1 page abstract)

Accepted papers (after blind review of at least 3 experts) will be published by CEUR–WS.

At least one of the authors of the accepted papers must register for the workshop (pre-conference only option) to be included into the workshop proceedings.

Important Dates

  • Full, Short and Position paper submission deadline: July 24th, 2023July 31st, 2023
  • Notification of Acceptance: August 28th, 2023
  • Camera-ready paper due: September 4th, 2023
  • ISWC 2022 Workshop day: November 6th-7th, 2023
Read CFP

Organizing Committee

Mehwish Alam

Telecom Paris, Institut Polytechnique de Paris, France

Davide Buscaldi

Labortoire d'Informatique Paris Nord (LIPN), Paris, France

Michael Cochez

Vrije University of Amsterdam, the Netherlands

Francesco Osborne

Knowledge Media Institute (KMi), The Open University, UK

Diego Reforgiato Recupero

University of Cagliari, Cagliari, Italy

Program Committee

  • Basil Ell, Universität Bielefeld
  • Rima Türker, Karlsruhe Institute of Technology
  • Ernesto Jimenez-Ruiz, City, University of London
  • Danilo Dessi, GESIS
  • Daniele Dell'Aglio, Aalborg University
  • Fabian Hoppe, Karlsruhe Institute of Technology
  • Gustavo de Assis Costa, Federal Institute of Education, Science and Technology of Goiás
  • Catia Pesquita, Faculdade de Ciencias, Universidade de Lisboa
  • Pierre Monnin, Orange-labs
  • Afshin Sadeghi, Fraunhofer Institute IAIS, Fraunhofer IAIS
  • Vasilis Efthymiou, Foundation for Research and Technology - Hellas
  • Andreea Iana, University of Mannheim
  • Claudia d'Amato, University of Bari
  • Maribel Acosta, Technische Universität München
  • Paul Groth, University of Amsterdam
  • Enrico Daga, KMI - Open University
  • Angelo Salatino, KMI - Open University
  • Simone Angioni, University of Cagliari
  • Nicolas Hubert, Université de Lorraine
  • Agnese Chiatti, Polytechnic Institute of Milan
  • Heiko Paulheim, University of Mannheim
  • Sahar Vahdati, InfAI

Volunteer to be the part of program committee of DL4KG by filling in this form.