Artificial Intelligence Knowledge Representation

Dear Colleagues,

There is an urgent need to support explicit and structured knowledge sharing and exchange in relation to artificial intelligence (AI).
Innovations in the field of artificial intelligence (AI) have been advancing rapidly, with novel concepts and new terminology as well as novel technical capabilities being developed by researchers and brought to market every day.
AI is becoming pervasive and possibly subtle, sometimes thoroughly embedded in architectures, powering and controlling technical systems at all levels, and, for this reason, it is relevant not only to the technical community, but also to all classes of users and to the public in general.
Technical advances, as well as the increasing complexity of AI-powered technologies, exceed the ability of individuals and public sector organizations to understand and regulate their operation and deployment, leading to potentially inestimable risks.
Knowledge representation (KR) was introduced decades ago, in the early days of AI, by scholars such as MeDermott [1], Bobrow and Winograd [2] and others. KR consists of techniques, constructs and artefacts required by computers to handle information expressed as concepts using natural language. Although primarily intended as a computational technique, KR can also be useful to codify and support explicit and functional human knowledge exchange, and understanding and learning about a subject.

This Special Issue tackles AI KR as a possible path to increasing the understanding, communication, and knowledge of advances in artificial intelligence for both humans and machines. It aims to break new ground in addressing KR in relation to a vast range of AI-related innovations emerging from R&D. Therefore, the scope and impact of this Special Issue is potentially very high.

MISSION: The overall goal of this Special Issue is to explore and capture methods, tools and artefacts for the conceptualization and specification of domain knowledge in AI, with an emphasis on novel and emerging AI and KR techniques and tools, and with regard to both the technical and socio-technical aspects (such as technology, legislation, ethics, fair use, etc.) of AI.

The purpose of placing a scholarly spotlight on AI KR is not only to capture and advance the state-of-the-art at the convergence of these important areas of knowledge, but also to facilitate knowledge exchange and re-use to contribute to the reliability of AI. As such, the proposed outcomes of this Special Issue could be instrumental to research and the advancement of science and inquiry in all AI-related fields of research, as well as to increasing the level of public awareness in general, and to enabling and supporting learning and participation, responsible governance and the use of new intelligent technologies.

NOTIFICATION
Two awards (equivalent to a value of CHF 350) will be granted to authors who meet the following criteria:
1. Their submission is accepted with above average review scores;
2. They do not have funding to cover the cost of the publication (self-declaration);
3. They completed their PhD less than 5 years ago.
AWARD PROCEDURE: Upon submitting their papers, authors should add a text file entitled REQUEST FOR AWARD where they state that they meet the conditions above.
After all reviews are completed, two authors (with the highest review scores) will be notified that they have been granted the award. The bonus will be transferred to them later. Please NOTE that authors who do NOT receive an award CAN NOT withdraw their manuscripts at this stage, while they may benefit from discount vouchers on the publication fee.

Possible guiding questions include:

• How can KR support AI sensemaking and intelligent systems reliability?
• Which KR techniques and tools can be useful to support AI knowledge?
• Which are the most interesting, relevant and useful advances in either AI or KR?
• How can KR help to reduce the risks associated with AI?

Submissions should fall into one of the following categories:
(Original and unpublished research work)

• Novel scholarly article that informs any aspect of KR relating to AI;
• Tools, techniques, evaluations, reviews, updates;
• Long-term perspectives from the computing field that can help to illustrate and evaluate the state of the art in AI KR;
• Reports of innovative AI KR applications in the humanities, sciences, engineering, and business areas;
• Reports of successful AI KR research in new problem domains;
• Use cases, experience and demos of new innovative systems.

Topics of interest include (but are not limited to):

• KR for AI-based UI development
• AI-related ontologies, taxonomies, vocabularies and schemas
• KR for neuromorphic engineering and computing
• KR for mind-controlled robotics
• KR for brain–computer interfacing
• KR for NN data and spiking models
• KR for neural interfaces/intention capture
• Knowledge extraction and categorization for AI/ML
• Tagging in AI/ML
• Knowledge organization and structuring
• Knowledge graphs for AI vs. natural language representation
• AI representation syntaxes and languages including ML
• Metadata extraction and hierarchies
• NLP (natural language processing) and machine learning
• Conceptual identification and representation KR with multiple and different viewpoints
• Knowledge-based intelligent systems (reasoning)
• Automation and reasoning
• KR for streaming AI real-time data
• Natural-language generation
• KR in chatbots and conversational agents
• Kr for deep neural networks
• KR in conversational user interfaces
• KR and AI ethics
• KR and intelligent autonomy
• KR in computerized speech and vision

[1] McDermott, D. Assimilation of new information by a natural language understanding system. Technical Report AI TR-298; MIT Artificial Intelligence Laboratory: Cambridge, MA, USA, March 1974.
[2] Bobrow, D.G.; Winograd, T. An overview of KRL, a Knowledge Representation Language. Cognitive Sci. 1977, 1, 3–46.

Dr. Paola Di Maio
Dr. Mari Carmen Suárez-Figueroa
Guest Editors

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