Call for papers: Special Issue on

Neuro-Symbolic Artificial Intelligence and the Semantic Web

Approaches in Artificial Intelligence (AI) based on machine learning, and in particular those employing artificial neural networks, differ fundamentally from approaches that leverage knowledge bases to perform logical deduction and reasoning. The former are connectionist or subsymbolic AI systems able to solve complex tasks over unstructured data using supervised or unsupervised learning, including problems which cannot reasonably be hand-coded by humans. Subsymbolic methods are generally robust against noise in training or input data and have recently, in the wake of deep learning, been shown to exceed human performance in tasks involving video, audio, and text processing. The latter are symbolic systems that thrive under the presence of large amounts of structured data, including for agent planning, constraint solving, data management, integration and querying, and other traditional application areas of knowledge-based systems and formal semantics. Classical rule-based systems, ontologies, and knowledge graphs that power search and information retrieval across the Web are also types of symbolic AI systems.

Symbolic and subsymbolic systems are rather complementary to each other. For example, the key strengths of subsymbolic systems are weaknesses of symbolic ones, and vice versa. Symbolic systems are brittle, i.e., susceptible to data noise or minor flaws in the logical encoding of a problem, which stands in contrast to the robustness of connectionist approaches. But subsymbolic systems are generally black boxes in the sense that the systems cannot be inspected in ways that provide insight into their decisions (despite some recent progress on this in the wake of the explainable AI effort) while symbolic knowledge bases can in principle be inspected to interpret how a decision follows from input. Most importantly, symbolic and subsymbolic systems contrast in the types of problems and data they excel at. Scene recognition from images appears to be a problem which in general lies outside the capabilities of symbolic systems, for example, while complex planning scenarios appear to be outside the scope of current deep learning approaches.

For this special issue, we welcome manuscripts falling broadly within the scope of Neuro-Symbolic Artificial Intelligence and the Semantic Web. A more detailed description of the general topic can be found in the 2020 SWJ paper “Neural-Symbolic Integration and the Semantic Web.”

Topics relevant to this special issue include - but are not limited to - the following, understood in a context of Neuro-Symbolic Artificial Intelligence and the Semantic Web:

• Machine learning for ontology construction, alignment or population
• Machine learning for knowledge graph construction or improvements, including integration and co-reference resolution
• Machine learning for applications of knowledge graph and ontology technology, e.g. for information systems, question answering, information integration.
• Embeddings for knowledge graphs and ontologies, and their applications.
• Explainable AI approaches that make use of ontologies or knowledge graphs.
• Deep deductive reasoning, i.e. training deep learning system to reason over knowledge representation languages relevant for the Semantic Web.
• Machine learning for link prediction in knowledge graphs.
• Machine learning for inductive reasoning, i.e., learning rules and constraints from knowledge graphs.
• Machine learning for data cleaning and repair.

Deadline

• Submission deadline: 1st of August, 2022. Papers submitted before the deadline will be reviewed upon receipt.

Author Guidelines

Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors.

We welcome any submission type as described in http://www.semantic-web-journal.net/authors#types. While there is no upper limit, paper length must be justified by content.

Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Nero-Symbolic Artificial Intelligence" special issue. All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.

Also note that the Semantic Web journal is open access.

Finally please note that submissions must comply with the journal’s Open Science Data requirements, which are detailed in the corresponding blog post.

Guest editors

The guest editors can be reached at swj-nesy-si@googlegroups.com .

Monireh Ebrahimi, IBM, USA
Pascal Hitzler, Kansas State University, USA
Md Kamruzzaman Sarker, University of Hartford, USA
Daria Stepanova, Bosch Center for AI, Germany

Guest editorial board

to be expanded

Mehwish Alam, FIZ Kalsruhe, Germany
Marjan Alirezaie, Örebro University, Sweden
Claudia d’Amato, Università degli Studi di Bari, Italy
Federico Bianchi, Bocconi University, Italy
Aaron Eberhart, Kansas State University, USA
Freddy Lecue, CortAIx Thales, Montreal, Canada and INRIA, France
Dagmar Gromann, University of Vienna, Austria
Paul Groth, University of Amsterdam, The Netherlands
Frank van Harmelen, VU Amsterdam, The Netherlands
Filip Ilievski, University of Southern California, USA
Gengchen Mai, Stanford, USA
Deborah McGuinness, RPI, USA
Alessandra Mileo, Dublin City University, Ireland
Pasquale Minervini, University College London, UK
Amadeo Napoli, INRIA, France
Axel-Cyrille Ngonga Ngomo, University of Paderborn, Germany
Alessandro Oltramari, Bosch Research, USA
Heiko Paulheim, University of Mannheim, Germany
Achim Rettinger, Trier University, Germany
Harald Sack, FIZ Karlsruhe, Germany
Luciano Serafini, Fondazione Bruno Kessler, Italy
Vered Shwartz, Allen Institute for AI and University of Washington, USA
Annette ten Teije, VU Amsterdam, The Netherlands
Jacopo Urbani, VU Amsterdam, The Netherlands