Combination of Symbolic and Sub-symbolic Methods and their Applications (CSSA)

Co-located with ECML/PKDD2021

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Brief Introduction

Since the beginning of the 2000s, there has been an increasing number of studies and standards proposed for generating large scale symbolic representations of knowledge (known as Knowledge Graphs (KGs)) out of heterogeneous resources such as text, images, etc. Moreover, there have been many advances in symbolic reasoning, as well as their applications to various fields. Recently, sub-symbolic methods have gained momentum. These methods aim at generating distributed representations from several resources such as text or symbolic representations (Graph Neural Networks, KG embeddings, etc.). These sub-symbolic methods for symbolic representations mainly focus on the task of KG completion. However, they have also recently been used for various tasks, e.g., in Natural Language Processing (NLP). The future perspective for these methods would be a combination of these approaches, leading to a form of neurosymbolic reasoning. Advances in the real world applications related to these methods will also serve as a stepping stone in the proving their practicality.

Keynote Speaker

Frank van Harmelen

Vrije Universiteit Amsterdam, the Netherlands.

Biography: Frank van Harmelen has a PhD in Artificial Intelligence from Edinburgh University, and has been professor of AI at the Vrije Universiteit Amsterdam since 2001, where he leads the research group on Knowledge Representation. He was one of the designers of the knowledge representation language OWL, which is now in use by companies such as Google, the BBC, New York Times, Amazon, Uber, Airbnb, Elsevier and Springer Nature among others. He co-edited the standard reference work in his field (The Handbook of Knowledge Representation), and received the Semantic Web 10-year impact award for his work on the Sesame RDF triple store. He is a Fellow of the European Association for Artificial Intelligence, member of the the Dutch Royal Academy of Sciences (KNAW), and of the Academia Europaea. He is adjunct professor at Wuhan University and Wuhan University of Science and Technology in China.

Title: TBD


Keynote Speaker

Luis Lamb

Secretary of State: Innovation, Science and Technology, Rio Grande do Sul, Brazil & UFRGS University, Brazil.

Biography: Luis C. Lamb is a Full Professor and Secretary of Innovation, Science and Technology of the State of Rio Grande do Sul, Brazil. He was formerly Vice President for Research (2016-2018) and Dean of the Institute of Informatics (2011-2016) at the Federal University of Rio Grande do Sul (UFRGS), Brazil. He holds both the Ph.D. in Computer Science from Imperial College London (2000) and the Diploma of the Imperial College, MSc by research (1995) and BSc in Computer Science (1992) from UFRGS, Brazil. His research interest includes neurosymbolic AI, the integration of learning and reasoning, and AI fairness. He co-authored two research monographs: Neural-Symbolic Cognitive Reasoning, with Garcez and Gabbay (Springer, 2009) and Compiled Labelled Deductive Systems, with Broda, Gabbay, and Russo (IoP, 2004). His research has led to publications at flagship AI and neural computation conferences and journals. He was co-organizer of two Dagstuhl Seminars on Neurosymbolic AI: the Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning (2014) and Dagstuhl Seminar 17192: Human-Like Neural-Symbolic Computing (2017) and several workshops on neural-symbolic learning and reasoning at AAAI and IJCAI.

Title: Neurosymbolic AI: An Overview

Abstract:The integration of learning and reasoning has been the subject of growing research interest in AI. However, both areas have been developed under clearly different technical foundations, by separate research communities. Neurosymbolic AI aims at integrating neural learning with symbolic methods typically used in computational logic and knowledge representation. In this talk, we present an overview of the evolution of neurosymbolic AI methods, with attention to developments towards integrating machine learning and reasoning into a unified foundation that contributes to explainable AI. We concluded by illustrating how advances in neural-symbolic computing can lead to the construction of richer AI systems.


Sara Maglicane

University of Amsterdam/MIT-IBM Watson AI Lab.

Achim Rettinger

Trier University, Germany.

Submission Details

Papers must comply with the LNCS style and should be submitted via EasyChair. Submissions can fall in one of the following categories:

  • Full research papers (8-12 pages)
  • Short research papers (6 pages)
  • Position/vision papers (4 pages)
  • Lightning talks (2 page abstract)

Authors are encouraged to submit negative (i.e., failing) results with strong contribution and an analysis of the results.
Accepted papers (after blind review) will be published as CEUR Proceedings.
At least one of the authors of the accepted papers must register for the workshop for the paper to be included into the workshop proceedings.

Read CFP!


Mehwish Alam

FIZ Karlsruhe
Leibniz Institute for Information Infrastructure

  • Paul Groth

    University of Amsterdam
    the Netherlands

  • Pascal Hitzler

    Kansas State University
    Manhattan, U.S.A.

  • Heiko Paulheim

    University of Mannheim

  • Harald Sack

    FIZ Karlsruhe
    Leibniz Institute for Information Infrastructure