Announcements

Knowledge is an international peer-reviewed open access journal regarding knowledge and knowledge-related technologies. The aim of Knowledge is to publish papers in the field of propositional knowledge, procedural knowledge, and acquaintance knowledge, social or economic, in both theory and application. We accept manuscripts on knowledge and knowledge-related technologies. Topics of interest include, but are not limited to:

• Knowledge Engineering and Technology
• Temporal and spatial database processing
• Intelligent information retrieval
• Modelling and object orientation
• Information, Data and Knowledge Management
• Artificial Intelligence techniques relating to knowledge and data management
• Data and knowledge sharing
• Uncertainty management
• Agent architectures and systems
• Knowledge discovery and data mining
• Knowledge-Based Education and Practice
• Knowledge-Based Economies and Societies

We welcome all distinguished scholars—across all fields—who contribute to the knowledge and knowledge-related technologies. We are devoted to providing a global forum for practitioners and scholars to disseminate their research to all intended audiences and further contribute to this field.

As an Editorial Board Member, you will be responsible for:

1. Providing suggestions for the journal's development, especially in the initial phase as well as in the long term.
2. Promoting Knowledge at academic events and increasing its visibility.
3. Suggesting (and optionally editing) special issues related to your field of research and recommending potential guest editors.
4. Prescreening submissions and making decisions on whether these papers should be rejected or accepted for publication.

If you are interested in this position or have any potential candidates for recommendation, please contact the Knowledge Editorial Office at knowledge@mdpi.com.

At MDPI, we strive to make your online publication process seamless and efficient. To achieve this, our team is continuously developing tools and features to make the user experience useful and convenient.

As the number of academic papers continues to grow, so does the need to analyze and work with them on a large scale. This prompted us to design a new feature aimed at helping researchers find journals that are relevant to their publication by matching their abstract topic. In this regard, we designed a similarity model that automatically identifies the most suitable academic journals for your paper.

We are pleased to introduce Journal Selector, a new feature that measures similarity in academic contexts. By simply entering the title and/or abstract into our Journal Selector, the author will see a list of the most related scientific journals published by MDPI. This method helps authors select the correct journals for their papers, highlighting the time of publication and citability.

The methodology is known as representation learning, where words are represented as vectors in hyperspace. Representation helps us differentiate between different concepts within articles, and in turn, helps us identify similarities between them.

We used an advanced machine learning model to better capture the semantic meanings of words. This helps the algorithm make better predictions by leveraging scientific text representation. In turn, this ensures high precision, helping authors decide which journal they should submit their paper to.

The goal is to support authors to publish their work in the most suitable journal for their research, as fast as possible, accelerating their career progress.

Contact: Andrea Perlato, Head of Data Analytics, MDPI (email)