As Artificial Intelligence systems increasingly rely on both language understanding and structured knowledge, bridging Natural Language Processing and Knowledge Engineering has become a central challenge for the next generation of intelligent systems. The rapid evolution of Large Language Models and knowledge-based AI is creating new opportunities—and new challenges—in how machines acquire, represent, connect, and reason over knowledge. Understanding language alone is no longer enough: modern AI systems must also be able to organize knowledge, integrate it with linguistic information, and use it effectively to support reasoning. As a result, the relationship between language, knowledge, and reasoning has emerged as one of the key research frontiers in contemporary Artificial Intelligence.
The Knowledge and Natural Language Processing (KNLP) Track at the ACM Symposium on Applied Computing investigates methods and applications at the intersection of *Knowledge Engineering* and *Natural Language Processing*, with particular emphasis on approaches that combine these two areas.
KNLP is an emerging and highly interdisciplinary research field at the core of Artificial Intelligence. It brings together and complements scientific advances in Natural Language Processing, Knowledge Representation and Reasoning, Machine Learning, and related disciplines.
*Topics of Interest*
Topics of interest include, but are not limited to, the following.
*Natural Language Processing*
- NLP methods for knowledge extraction - NLP for ontology population and ontology learning - Sentiment analysis and opinion mining for knowledge-based applications - Interplay between natural language and ontologies - NLP for explainable knowledge - Machine translation techniques for multilingual knowledge - NLP for the Web - Bias detection and mitigation in small and large language models - Interaction between small or large language models and knowledge
*Knowledge*
- Knowledge-enhanced NLP - Knowledge for information retrieval - Knowledge-based sentiment analysis and opinion mining - Combining knowledge and deep learning for NLP - Knowledge technologies for the Web - Knowledge-enhanced agentic reasoning - Agent reasoning over knowledge graphs and ontologies - Knowledge-based agent personalization - Linked Data for NLP - Knowledge-based natural language explainability - Language-model-enhanced ontology and knowledge engineering methodologies and tools - Language-model-based agents for knowledge extraction, reasoning, and management - Ontology evaluation using small and large language models - Ontological knowledge representation and memorization in language models - Knowledge-based techniques for language models, including Retrieval-Augmented Generation, fact-checking, and bias mitigation - Question answering over knowledge graphs using small and large language models
*Real-World Applications Exploiting Knowledge and NLP*
- Knowledge and NLP systems for Big Data scenarios - Knowledge and NLP technologies supporting a diverse, equitable, and inclusive society - Deployment and evaluation of Knowledge and NLP systems in domains such as: - Digital Humanities and Social Sciences - eGovernment and public administration - Life sciences, healthcare, and medicine - News, media, and data-streaming environments
*Paper Submission*
We invite original research papers and experience reports addressing the topics listed above.
Submissions must not have been previously published or be under consideration for publication elsewhere. Papers must be submitted in PDF format using the official ACM SAC proceedings template.
Authors’ names and affiliations must be entered separately in the submission system and must not appear in the submitted manuscript. All submissions will undergo a *double-blind peer-review process* in accordance with ACM SAC regulations.
Submissions to the *Student Research Competition (SRC)* are also welcome. Prospective authors should consult the SAC 2027 SRC page for eligibility requirements and submission instructions.
*Submission Policy*
- All papers must initially be submitted as *regular papers*. There is no separate submission category for poster papers. - Papers will be evaluated according to their originality, technical contribution, presentation quality, and relevance to the Knowledge and Natural Language Processing Track. - Depending on the outcome of the review process and the overall acceptance-rate constraints, technically sound submissions that cannot be accepted as regular papers may be offered acceptance as posters. - Although there is no formal minimum page requirement, submissions shorter than *four full pages* that do not demonstrate a substantial contribution may be desk-rejected without external review.
*Submission Links*
- *Regular papers and SRC abstracts:* submission links are available through the ACM SAC 2027 website https://www.sigapp.org/sac/sac2027/ - *Author kit and templates:* formatting instructions and official templates are available through the ACM SAC 2027 website https://www.sigapp.org/sac/sac2027/
*Important Dates*
Please consult the official ACM SAC 2027 website https://www.sigapp.org/sac/sac2027/#important-dates for up-to-date deadlines and possible changes.
- *October 2, 2026:* Regular paper and SRC abstract submission - *November 13, 2026:* Author notification - *November 28, 2026:* Camera-ready copies of accepted papers and SRC submissions - *December 5, 2026:* Author registration deadline - *April 5–9, 2027:* Knowledge and Natural Language Processing Track at ACM SAC 2027
All deadlines follow the time zone specified on the official conference website.
*Further Information*
For further information, please visit the Knowledge and Natural Language Processing Track website https://knlp.fbk.eu/ and the ACM SAC 2027 conference website https://www.sigapp.org/sac/sac2027/.
Questions may be addressed to the KNLP Track Co-Chairs knlp@fbk.eu.