Advances in Expert Systems for Natural Language Processing

Dear Colleagues,

While modern Natural Language Processing (NLP) has been dominated by data-driven and deep learning paradigms, the foundational principles of expert systems, such as explicit knowledge representation, rule-based inference, and transparent reasoning, are witnessing a renewed relevance. This Special Issue invites contributions that bridge symbolic and sub-symbolic approaches to overcome the interpretability gap in current NLP models and to integrate expert reasoning within the emerging paradigm of generative AI.

Expert systems, once the cornerstone of Artificial Intelligence, offer structured and auditable reasoning capabilities that can complement the opaque decision-making of neural and generative models. The fusion of these paradigms enables systems that are not only accurate but also accountable, interpretable, and aligned with human expert judgment. This issue highlights the resurgence of knowledge-based reasoning in NLP, encompassing hybrid architectures, neuro-symbolic integration, ontology-driven natural language understanding, and expert-guided generative systems capable of producing coherent, domain-relevant outputs with transparent reasoning paths.

The scope of this issue includes both methodological advances, such as rule-based reasoning engines integrated with transformers and generative models, hybrid symbolic and neural knowledge bases, or explainable decision pipelines, and applied research in domains such as healthcare, education, tourism, and general NLP. Example applications include clinical reasoning, patient-centered decision support, intelligent tutoring systems, explainable feedback generation, sentiment interpretation, cultural reasoning, policy discourse analysis, interpretable dialogue agents, context-aware retrieval, and knowledge-grounded generative models.

We welcome original research, reviews, and case studies that revisit expert system methodologies or redefine their role in the age of large language models and generative AI. Submissions demonstrating tangible interpretability, domain adaptation, or explainable inference mechanisms will receive particular emphasis.

Dr. Miguel Ángel Álvarez Carmona
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.