Aims & Scope
Neurosymbolic Artificial Intelligence is an open access and transparently peer-reviewed research journal covering a wide range of topics related to neurosymbolic AI.
In the field of artificial intelligence (AI), recent advances in deep learning and big data have resulted in artificial neural networks attaining industrial relevance in a wide range of applications. Neural networks are now the state-of-the-art in language modeling, speech and image classification, sensor data and graph analytics, time series forecasting, and many more tasks requiring the processing of unstructured large data. By contrast, symbolic AI relies on the formalization of knowledge bases and rule-based algorithmic approaches, modeling sound and well-understood reasoning based on expert knowledge. This offers better explanations of AI via knowledge representations that can be inspected to interpret how decisions follow from inputs. However, this is challenged by unstructured large data. Neural and symbolic approaches to AI also provide deeper insights into resolving problems at which they excel. For example, deep learning excels at scene recognition, but it does not achieve state-of-the-art performance at planning, rich deductive reasoning, or complex symbol manipulation.
Neurosymbolic AI is an emerging field of AI aiming to build rich computational AI models, systems and applications by combining neural and symbolic learning and reasoning. It seeks to combine the complementary strengths of neural and symbolic AI while overcoming their respective weaknesses, either in the form of principled integration between both paradigms and forms of representation or in the form of hybrid systems combining neural and symbolic components in one architecture.
Neurosymbolic Artificial Intelligence relies on an open and transparent peer-review process. Submitted manuscripts are posted on the journal's website and are publicly available. In addition to solicited reviews selected by members of the editorial board, public reviews and comments are welcome from any researcher and can be uploaded using the journal’s website. All reviews and responses from the authors are posted on the journal homepage. All involved reviewers and editors will be acknowledged in the final printed version. While we strongly encourage reviewers to participate in the open and transparent review process, it is still possible to submit anonymous reviews.
The journal Neurosymbolic Artificial Intelligence furthermore is a proponent of Open Science Data and requires, whenever possible, that authors provide relevant data and software for evaluation and replication.
Accepted paper types:
- Research articles
- Survey articles
Key features
Open Access
The journal is gold open access and articles are published under the CC BY license.
Open and Transparent Reviewing
All submitted papers are, after a cursory check, made publicly available as pre-prints. Reviews will be actively solicited by the handling editor. When a decision on a paper is reached, all reviews are also made available publicly. The name of the handling editor as well as the names of the reviewers, who have not opted out to be identified, will be mentioned in the published articles.
Speedy Reviewing
Neurosymbolic Artificial Intelligence is committed to provide authors with peer-review feedback in a timely manner.
Pre-Prints
All submitted papers are made available as pre-prints before the reviewing starts, so reviewers and readers are free to not only read but also share submitted papers.
Neurosymbolic Artificial Intelligence wishes to promote an environment in which annotated data are produced and shared with the wider research community. The journal therefore requires authors to ensure that any data used or produced in their submitted workare represented using community-based data formats and metadata standards. These data should furthermore be made openly available and freely reusable, unless privacy concerns apply.
Editorial Board
Editors-in-Chief
Dr. Tarek R. Besold
Senior Research Scientist
Sony AI
Sony Europe B.V.
Avenida Diagonal 640
08017 Barcelona
Spain
E-mail: tarek.besold@googlemail.com
Prof. Artur d'Avila Garcez
School of Science & Technology
Department of Computer Science
City, University of London
A304A, College Building
Northampton Square
London EC1V 0HB
United Kingdom
E-mail: a.garcez@city.ac.uk
Prof. Dr. Pascal Hitzler
Department of Computer Science
Kansas State University
2184 Engineering Hall
1701D Platt St.
Manhattan, KS 66506, USA
E-mail: phitzler@googlemail.com
Editorial Board
Mehwish Alam
Télécom Paris, Institut Polytechnique de Paris
Paris, France
Marjan Alirezaie
Flybits Inc
Toronto, Canada
Vaishak Belle
University of Edinburgh & Alan Turing Institute
Edinburgh, United Kingdom
Federico Bianchi
Stanford University
Stanford, CA, USA
Qingxing Cao
Sun Yat-sen University
Guangzhou, China
Roberto Confalonieri
University of Padua
Padua, Italy
Claudia d’Amato
University of Bari
Bari, Italy
Leilani Gilpin
University of California, Santa Cruz
Santa Cruz, CA, USA
Eleonora Giunchiglia
TU Wien
Vienna, Austria
Marco Gori
University of Siena
Sienna, Italy
Dagmar Gromann
University of Vienna
Vienna, Austria
Frank van Harmelen
Vrije Universiteit Amsterdam
Amsterdam, The Netherlands
Janna Hastings
University of Zurich & University of St. Gallen
Switzerland
Filip Ilievski
Information Sciences Institute
University of Southern California
Los Angeles, CA, USA
Ernesto Jimenez-Ruiz
City, University of London
London, United Kingdom
Luis Lamb
Federal University of Rio Grande do Sul
Porto Alegre, Brazil
Juanzi Li
Tsinghua University, China
Tsinghua, China
Bo Liu
Department of Computer Science
George Mason University
Fairfax, VA, USA
Alessandra Mileo
Insight Centre for Data Analytics
Dublin City University
Dublin, Ireland
Pasquale Minervini
University of Edinburgh
Edinburgh, UK
Raghava Mutharaju
IIIT-Delhi
Delhi, India
Alessandro Oltramari
Bosch Technology and Research Center & Bosch Center for Artificial Intelligence
Pittsburgh, PA, USA
Catia Pesquita
LASIGE, Faculdade de Ciências
Universidade de Lisboa
Lisbon, Portugal
Luc de Raedt
KU Leuven, Belgium and Örebro University, Sweden
Leuven, Belgium
Francesca Rossi
IBM Research
Yorktown Heights, NY, USA
Alessandra Russo
Imperial College London
London, United Kingdom
Marta Sabou
Vienna University of Economics and Business (WU).
Vienna, Austria
Md Kamruzzaman Sarker
Department of Computing Sciences
University of Hartford
West Hartford, CT, USA
Steven Schockaert
Cardiff University
Cardiff, United Kingdom
Danny Silver
Acadia University
Nova Scotia, Canada
Gustav Sir
Czech Technical University
Prague, Czech Republic
Ron Sun
Cognitive Science Department
Rensselaer Polytechnic Institute
Troy, NY, USA
Annette Ten Teije
Vrije Universiteit Amsterdam
Amsterdam, The Netherlands
Ilaria Tiddi
Vrije Universiteit Amsterdam
Amsterdam, The Netherlands
Benedikt Wagner
Data Scientist / (Explainable) AI Consultant
London, United Kingdom
Stefan Wermter
University of Hamburg
Hamburg, Germany
Author Guidelines
Submission of Manuscripts
All submissions must be made through https://neurosymbolic-ai-journal.com/. Please read the peer review policy of the journal prior to submitting your work.
Required files
For initial submission a .pdf file of the article is sufficient. After an article has been accepted for publication an editable file of the text, such as MsWord or LateX, is required. When preparing a paper for submission in LaTeX you can use our LaTeX template or our MsWord template.
Copyright of your article
Authors submitting a manuscript do so on the understanding that they have read and agreed to the terms of the IOS Press Author Copyright Agreement.
Article sharing
IOS Press adopted Sage’s Article Sharing Policy from 8th of July 2024.
Please go to: Sage’s Author Archiving and Re-Use Guidelines | SAGE Publications Ltd for details. If your manuscript was submitted prior to 8th of July 2024, please contact editorial@iospress.nl with details of your enquiry.
Kudos
Authors of published articles (non-prepress, final articles) will be contacted by Kudos. Kudos is a service that helps researchers maximize the impact and visibility of their research. It allows authors to enrich their articles with lay metadata, add links to related materials and promote their articles through the Kudos system to a wider public. Authors will receive no more than three emails: one invitation and a maximum of two reminders to register for the service and link the published article to their profile. Using and registering for Kudos remains entirely optional. For more information, please have a look at our authors section.
Promoting your work
Would you like some pointers on how to help your research achieve a wider reach and greater impact? Please consult our Promotional Toolkit for Authors for tips.
Please visit the IOS Press Authors page for further information.
Abstracted/Indexed in
Neurosymbolic Artificial Intelligence is a new journal and will be applied for indexation at relevant services in due course.
Open Access
Neurosymbolic Artificial Intelligence is an open access journal.
The current APC for the journal is waived, a discount from the full rate of US$1600.
Peer Review
Neurosymbolic Artificial Intelligence Peer Review Policy
Neurosymbolic Artificial Intelligence relies on an open and transparent peer review process. Papers submitted to the journal are quickly pre-screened by the Editors-in-Chief and if deemed suitable for formal review they are immediately published as pre-prints on the journal’s website. Please visit our reviewer guidelines for further information about how to conduct a review.
Reasons to reject a paper in the pre-screening process could be because the work does not fall within the aims and scope, the writing is of poor quality, the instructions to authors were not followed or the presented work is not novel.
Papers that are suitable for review are posted on the journal's website and are publicly available. In addition to reviews solicited by members of the editorial board, public reviews and comments are welcome by any researcher and can be uploaded using the journal website. All reviews and responses from the authors are posted on the website as well.
Reviewers are by default identified by name although all reviewers do have the option to remain anonymous. All review reports are made openly available under CC-BY licenses after a decision has been made for the submission (independent of whether the decision was accept or reject). In addition to solicited reviews, any researcher is welcome to submit additional reviews and comments for papers that are under review. Editors and non-anonymous reviewers will be mentioned in the published articles.
Each paper that undergoes peer review is assigned a handling editor who will be responsible for inviting reviewers to comment on the paper.
The reviewer of a paper is asked to submit one of the following overall recommendations:
- Accept. The article is accepted as is, or only minor problems must be addressed by the authors that do not require another round of reviewing but can be verified by the editorial and publication team.
- Minor revisions required. The authors are required to make moderate changes to their manuscript. The manuscript becomes acceptable for publication if the changes proposed by the reviewers and editors are successfully addressed. The revised manuscript will be examined by the Editors-in-Chief and possibly sent back to all (or a selection of) reviewers for a second round of reviews. Authors are requested to provide a letter to the reviewers detailing the improvements made for the resubmission.
- Major revisions required. The manuscript cannot be accepted for publication in its current form. However, a major revision which addresses all issues raised by the reviewers may be acceptable for publication. The revised manuscript will undergo a full second round of review. Authors are requested to provide a letter to the reviewers detailing the improvements made for the resubmission.
- Reject. The work cannot be published based on the lack of interest, lack of novelty, insufficient conceptual advance or major technical and/or interpretational problems.
Reviewers are requested to evaluate a paper with respect to the following criteria:
- Significance. Does the work address an important problem within the research fields covered by the journal?
- Background. Is the work appropriately based on and connected to the relevant related work?
- Novelty. For research papers: Does the work provide new insights or new methods of a substantial kind? For survey papers: Is the topic covered in a comprehensive and well balanced manner, are the covered approaches accurately described and compared, and are they placed in a convincing common framework?
- Technical quality. For research papers: Are the methods adequate for the addressed problem, are they correctly and thoroughly applied, and are their results interpreted in a sound manner? Are the experiments replicable and has all relevant data for replication be made available? For survey papers: Is the topic covered in a comprehensive and well balanced manner, are the covered approaches accurately described and compared, and are they placed in a convincing common framework?
- Presentation. Are the text, figures, and tables of the work accessible, pleasant to read, clearly structured, and free of major errors in grammar or style?
- Length. Is the length of the manuscript appropriate for what it presents?
- Data availability. Are all used and produced data openly available in established data repositories, as mandated by FAIR data and the data availability guidelines?
Finally, reviewers are asked to answer the following points:
- Summary of paper in a few sentences
- Reasons to accept
- Reasons to reject
- Further comments (optional)
Accept or reject decisions are made by the Editors-in-Chief, whose decision is final
