Knowledge-Guided Machine learning

1st European Knowledge-Guided Machine learning workshop - September 22 2023, Turin, Italy

About this Workshop

We are glad to announce the 1st european Knowledge-Guided Machine learning workshop, to be held in Turin, Italy in ECML-PKDD 2023.
All the information about the conference can be found at the official website.

Even though machine learning (ML) and deep learning (DL) algorithms have achieved amazing results in many commercial and business applications, data-driven models have so far met with limited success in many scientific domains. Limitations of data-driven models arise due to their intrinsic black-box nature, their large data requirements, their inability to produce physically consistent results, and the lack of generalizability to out-of-sample scenarios. More generally, the popularity and success of ML-based systems has put the spotlight on issues such as explainability, bias, fairness, and sustainability. There is thus a growing interest in developing Knowledge Guided Machine Learning (KGML) approaches that can leverage decades (sometimes centuries) of accumulated scientific knowledge and combine them with ML techniques to reap the benefits of both approaches. Addressing these issues naturally leads to systems in which representation of prior and domain knowledge in various forms, from physical and simulation models to symbolic and logical representations, plays a central role and must be integrated into ML models and pipelines. On the other hand, ML also offers solutions for long-standing challenges in the field of Knowledge Representation (KR), for instance related to efficient, neurally-guided, noise-tolerant and ampliative inference, knowledge acquisition, efficient reasoning, and limitations of existing physical and symbolic models. The synergy between ML and KR has the potential to lead to new advancements in fundamental AI challenges including, but not limited to, learning symbolic generalizations from raw (multi-modal) data, using knowledge to facilitate data-efficient learning, speeding up inference, supporting interpretability of learned outcomes and integration of symbolic planning and reinforcement learning.

The workshop aims to provide researchers and practitioners with a dedicated forum for discussing new ideas and research results at the intersection of machine learning and knowledge representation, both from a theoretical and application standpoint.


Program New

The workshop will be held on September 22nd, 2023, from 9:00 a.m. to 13:00 a.m. (Italian time) at Room 4i of the Main Campus of the Politecnico di Torino ( map here).

Papers denoted with are Extended abstracts from recently published works. The associated full papers are linked below.

Schedule (Italian time)
9:00 a.m. - 9:05 a.m. WELCOME, Introduction by the chairs
9:05 a.m. - 9:20 a.m.
  • Towards Automatically Refining Low-Quality Domain Knowledge: A Case Study in Healthcare
  • Towards Knowledge-Guided Deep Temporal Dynamics Modeling for Power Engineering
Pawel Bielski, Sönke Jendral, Lena Witterauf et al.
9:20 a.m. - 9:35 a.m. SEEDOT: Tool for Enhancing Sentiment Lexicon with Machine Learning
Vittorio Haardt, Lorenzo Malandri, Fabio Mercorio, Luca Porcelli
9:35 a.m. - 9:50 a.m. Lorentz-invariant augmentation for high-energy physics
Simone Monaco, Sebastiano Barresi, Daniele Apiletti
9:50 a.m. - 10:05 a.m. Unsupervised Ontology- and Taxonomy Construction through Hyperbolic Relational Domains and Ranges
Filip Cornell, Yifei Jin, Jussi Karlgren, Sarunas Girdzijauskas
10:05 a.m. - 10:20 a.m. Discovering SpatioTemporal Warning Contexts from Non-Emergency Call Reports
Luca Cagliero, Andrea Avignone, Silvia Chiusano
10:20 a.m. - 10:35 a.m. A Filter-based Neural ODE Approach for Modelling Natural Systems with Prior Knowledge Constraints
Cecília Coelho, M. Fernanda P. Costa, Luís L. Ferrás
10:35 a.m. - 10:45 a.m. Prior Knowledge Meets Neural ODEs: A Two-stage Training Method for Improved Explainability
Cecília Coelho, M. Fernanda P. Costa, Luís L. Ferrás Full paper
11:00 a.m. - 11:30 a.m. Coffee Break
11:30 a.m. - 12:00 a.m. Keynote - Neurosymbolic AI: Some pain, a lot of gain!
Tarek R. BesoldBio
When introducing neurosymbolic integration to researchers, one is often met with the question: “But why bother? Can’t I just machine learn it?”. Surprisingly often – particularly, when considering the current popularity of ML (and particularly DL) in academic research and the popular press – the answer is “No, you can’t.”. In this talk, we will have a look at the main motivation(s) for why integrating between symbolic and subsymbolic computation is often desirable, if not unavoidable. We will have a look at several use cases (taken from a variety of domains ranging from biomedical research to multisensory perception and context-adaptive robot control) which as of today appear to be almost impossible to solve without making recourse to both ML- and logic-based approaches. We will identify some of the reasons why a combination between data and knowledge is required, and will consider some of the most common ways of how to combine symbolic and subsymbolic aspects in solving the corresponding challenges.
12:00 a.m. - 12:15 a.m. Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu et al. Full paper
12:15 a.m. - 12:30 a.m. Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini
12:30 a.m. - 12:45 a.m. Symbolic Regression via Control Variable Genetic Programming
Nan Jiang, Yexiang Xue Full paper
12:45 a.m. - 13:00 a.m. Panel and Closing remarks

Keynote Speakers

Tarek R. Besold

Senior Research Scientist - Sony AI Barcelona

Tarek R. Besold is a Senior Research Scientist at Sony AI in Barcelona leading the Gastronomy Flagship Project, as well as an affiliated researcher with the Philosophy & Ethics group at Eindhoven University of Technology. His work covers topics at the intersection between AI, cognitive science, and real-world applications. He also serves as a start-up advisor and consulting AI policy expert, and as chairman of the German DIN/DKE Standards Working Committee on AI NA 043-01-42 GA.

Call for papers

Authors are invited to submit articles combining aspects of domain knowledge representation and ML, including the use of knowledge-aware methods for solving ML challenges (e.g. knowledge-guided or explainable learning), the use of ML methods for solving-knowledge representation challenges (e.g. efficient inference, knowledge base completion), the integration of learning and reasoning at the modeling or solving side, and the application of combined knowledge-driven ML approaches to solve real-world problems and accelerate scientific discovery.

We welcome papers on a wide range of topics, including but not limited to:

  • Novel techniques for incorporating prior knowledge into machine learning models
  • Learning with structured knowledge
  • Solving differential equations with machine learning in physics
  • Transfer learning, multi-task learning, and domain adaptation with knowledge
  • Interpretable and explainable machine learning with knowledge
  • Human-in-the-loop and interactive machine learning with knowledge guidance
  • Knowledge distillation and model compression
  • Discovery of Scientific Laws from Data with machine learning techniques
  • Learning symbolic knowledge, such as ontologies and knowledge graphs, action theories, common-sense knowledge, spatial and temporal theories, preference models and causal models
  • Logic-based, logical and relational learning algorithms
  • Knowledge-driven natural language understanding
  • Knowledge-driven decision making
  • Machine-learning driven reasoning algorithms
  • Neural-symbolic learning
  • Symbolic reinforcement learning
  • Expressive power of learning representations
  • Architectures that combine data-driven techniques and formal reasoning
  • Applications of knowledge guided machine learning in real-world scenarios

Important dates

All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth").

  • Submission deadline: 12th June 2023 18th June 2023
  • Notification of acceptance: 12th July 2023
  • Camera-ready deadline: 24th July 2023
  • Workshop date: 22th September 2023

Submission Guidelines

Submissions should present original results not currently published or under review elsewhere. This workshop accepts:

  • Short papers (up to 6 pages including references) describing early-stage original research results.
  • Long papers (up to 12 pages including references) describing original research results, including new algorithms, empirical studies, or in-depth comparisons with existing approaches.
  • Authors of recently published papers in top-tier conferences and journals such as JAIR, MLJ, AIJ, IEEE TNNLS, AAAI, IJCAI, NeurIPS and ICML are invited to submit their work to the workshop. The authors should clearly indicate in the submission form the venue where the paper has been published or is under review. You are asked to submit a 2-page extended abstract (including references).

All submissions must be written in English and formatted according to the Springer LNCS style [Paper template]. The submissions must be in PDF format.

The Workshops and Tutorials will be included in a joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science, in 1-2 volumes, organised by focused scope and possibly indexed by WOS.
Authors will have the faculty to opt-in or opt-out.


You can contact the organizers at

  • Simone Monaco - Politecnico di Torino, Italy
  • Lia Morra - Politecnico di Torino, Italy
  • Daniele Apiletti - Politecnico di Torino, Italy
  • Anuj Karpatne - Virginia Tech, USA

Program committee members

  • Pietro Barbiero - University of Cambridge
  • Samy Badreddine - Sony AI
  • Tania Cerquitelli - Politecnico di Torino
  • Gabriele Ciravegna - Université Côte d'Azur
  • Sean Current - The Ohio State University
  • Arka Daw - Virginia Tech
  • Ivan Donadello - Free University of Bozen-Bolzano
  • Mohannad Elhamod - Virginia Tech
  • Eleonora Giunchiglia - Tu Wien, Austria
  • Xiaowei Jia - University of Pittsburgh
  • Zhe Jiang - University of Florida
  • Ernesto Jimenez Ruiz - City University London
  • Emile van Krieken - Vrije Universiteit Amsterdam
  • Jonghyun Harry Lee - University of Hawaii at Mānoa
  • Gianluigi Lopardo - Université Côte d'Azur
  • Francesco Manigrasso - Politecnico di Torino
  • Abdullah Al Maruf - Virginia Tech
  • Kevin Mottin - Université Côte d'Azur
  • Seung-Hoon Na - Jeonbuk National University
  • Dhruv Patel - Stanford University
  • Alessandro Rizzo - Politecnico di Torino
  • Laura von Rueden - Fraunhofer IAIS
  • Paulo Shakarian - Arizona State University
  • Somya Sharma Chatterjee - University of Minnesota
  • Michael Steinbach - University of Minnesota
  • Kshitij Tayal - University of Minnesota
  • Tillman Weyde - City University London