Key research themes
1. How can semantic wikis be designed to support collaborative knowledge engineering effectively?
This research area addresses the challenges and methodologies for developing semantic wikis as collaborative platforms that empower domain experts and knowledge engineers to jointly construct and maintain knowledge bases. It matters because traditional knowledge engineering often relies on specialists, while collaborative knowledge engineering (CKE) requires accessible, flexible tools that accommodate domain experts without deep computational backgrounds, thus enhancing knowledge quality, usability, and community engagement.
2. What architectures and methodologies enable scalable and efficient knowledge graph construction from heterogeneous data sources?
Constructing knowledge graphs (KGs) from diverse data formats—structured, semi-structured, and unstructured—is a major challenge due to heterogeneity, complex pipelines, and required domain semantics. Research focuses on developing unified architectural frameworks, data transformation meta-models, and virtual KG systems to streamline KG construction, enhance maintainability, and allow flexible, SPARQL-based querying over integrated data. This theme is essential for realizing the Semantic Web vision and enabling data integration across domains.
3. How can logic-based and rule-based methods improve knowledge base management and business process modeling?
Research explores integrating logic programming, answer set programming (ASP), and declarative rule languages to enhance the expressiveness, reasoning capabilities, and quality assurance of knowledge bases and business process models. These approaches allow rich semantic modeling, interplay of multiple knowledge paradigms, validation against meta-models, and support for complex querying and simulation. This theme advances the capability to formally represent and manipulate knowledge and complex workflows in dynamic domains.