Knowledge-Guided Machine learning

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.

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

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.