The Data, Knowledge, and Web Engineering group at Aalborg University is a leading research collective that focuses on data engineering, data science, quantum computing, and advanced machine learning methods.

We participate in a number of cross-disciplinary research efforts in different areas, including bio science, health, and sustainability assessment, and closely collaborate with other groups and departments.

Our joint ambition is to bring meaning to large amounts of heterogeneous data and exploit it in the best possible way for a broad range of use cases and applications.

DATA

Our research in data engineering and data science covers the entire big data value chain from data extraction, integration, management, exploration, searching, querying, analytics, data mining, information retrieval, recommender systems to machine-learning-driven approaches for interdisciplinary data science.

In particular, we contribute to the next generation of intelligent information systems by developing breakthrough technologies based on graphs and human-generated data.

KNOWLEDGE

Our research in knowledge engineering and knowledge-based systems covers extracting, predicting, managing, and exploring knowledge. We study and develop methods for:

• Extracting knowledge from diverse types of data
• Utilizing knowledge in prediction tasks including natural language understanding, translation, information retrieval, recommender systems, and social network analysis
• Managing, querying, analyzing, and exploring knowledge

In particular, we focus on methods for representation learning and embeddings, natural language understanding as well as knowledge graph management and querying in heterogeneous ecosystems and in consideration of provenance, personalization, user behavior analysis, and privacy.

WEB

Our research in Web science concerns both the Web as a subject of research as well as the Web as a technological infrastructure. We are actively advancing the state of the art in Web social networks analytics, recommender systems, Web data management and querying, online data streaming services as well as other Web science and engineering methods.

In particular, we focus on the use and development of decentralized knowledge graphs and Semantic Web technologies as well data management methods and architectures for heterogeneous and dynamic data on the Web.

RESEARCH APPROACH

The research approach is primarily constructive in nature: theoretically well-founded, purposeful artefacts such as frameworks, data structures, indexes, algorithms, languages, tools, and systems are prototyped and subjected to empirical study.

Further, the research is mostly driven by novel and challenging real-world applications, with primary application areas being web querying, bio science, and healthcare.

The research has impact on (at least) SDGs 3, 4, 12, 15, and 16.