We develop the foundations for the next generation of information extraction, data analysis and neuro-symbolic programming systems. Our research extends ideas from data management, artificial intelligence, programming languages and logic. Extracting value from data increasingly requires sophisticated algorithms to represent, query, process, analyze and interpret data. We develop the foundations of data processing systems and neuro-symbolic programming, with a focus on extracting information from graph structures. These graph structures are obtained from raw data that may be more or less structured, noisy, uncertain or incomplete. Challenges include robust, efficient and scalable processing of large graphs obtained from such data. We study and build new information extraction methods, as well as new robust and scalable programming methods for rich graph data structures.
One one hand, we develop the algebraic and logical foundations of advanced data programming languages (extended relational algebras, algorithms, compilers) for more expressive and efficient query languages, in particular through aspects such as recursion, types, provenance, etc.
On the other hand, we develop neuro-symbolic programming with graphs, including the integration of logic with neural networks, scaling, and support for rich knowledge and property graphs.