This track focuses on the latest methodologies and frameworks in knowledge engineering, emphasizing their application in social sciences and humanities. Researchers are encouraged to present innovative approaches that enhance knowledge representation and retrieval.

This session explores the integration of artificial intelligence techniques in social research, highlighting case studies and practical applications. Participants will discuss the implications of AI on data collection, analysis, and interpretation in the social sciences.

This track examines the role of ontologies in structuring knowledge and facilitating information sharing on the semantic web. Contributions should address the development, implementation, and evaluation of ontological frameworks in various domains of social science.

This session invites papers that utilize data analytics to derive insights into social phenomena and human behavior. Researchers will present methodologies that leverage big data to inform policy-making and societal development.

This track focuses on innovative techniques for knowledge discovery in large datasets, particularly within the context of social sciences. Contributions should highlight the challenges and solutions in extracting meaningful patterns and insights from complex data.

This session explores the application of machine learning algorithms in humanities research, including text analysis and cultural data interpretation. Participants will discuss the potential and limitations of these technologies in enhancing scholarly inquiry.