Research group The research interests of DDM group include data federation and privacy preservation by applying techniques of text mining, natural language processing, machine learning, and semantic web.
The research group of Deep Data Mining was established to develop algorithms and implement prototype for multi-sources heterogeneous information federation and privacy preservation on multimodal data.
Regarding datatypes to integrate, we consider data from structured (e.g, records in DB), semi-structured (e.g, XML, JSON) and unstructured sources (e.g, news, social media). In a broad view of the core techniques, our group applies technologies of database, data mining, natural language processing, machine learning, and ontology based semantic web technology. As application-driven research, we aim to realize general data integration framework to adapt multiple applications (e.g, information retrieval, recommendation systems, online advertisements) and meanwhile acquire the unique characteristics of domain-data to boost the integration accuracy on specialized domains (e.g, social networks, demographic, review data).
Getting intelligent computer programmes to recognize and express intentions is complex, new thesis shows.
Lisa Hed and Lena Kallin Westin are awarded the Faculty of Science and Technology's Pedagogical Prize 2022.
PhD Timotheus Kampik shows mathematical reasoning methods for autonomous intelligent systems.
