The huge wealth of data available nowadays holds tremendous potential to improve our lives, whether it be by advancing scientific knowledge, improving patient care, or supporting more informed policymaking. However, obtaining relevant and reliable information from real-world data is difficult due to the need to integrate data across multiple heterogeneous sources. Moreover, even if this first hurdle is overcome, a perhaps even more daunting challenge arises: how to obtain reliable insights from imperfect data? Indeed, it is widely acknowledged that real-world data is plagued with quality issues, such as incompleteness (missing information) and errors (false or outdated information). How can we obtain meaningful answers to queries posed over such imperfect data, and how best to generate a high-quality version of the data?
The aim of the INTENDED chair is to develop intelligent, knowledge-based methods for handling imperfect data, thereby enabling confident and informed decision making.
Our starting point will be the ontology-based data access (OBDA) approach, which employs semantic knowledge and automated reasoning to bridging the gap between users’ information needs and how the relevant data is actually stored. While OBDA systems are growing in maturity, they too often fail to address the data quality issue, aside from issuing warnings when inconsistencies are discovered.
To enable the widespread adoption of OBDA, it is essential to equip OBDA systems with appropriate mechanisms for handling imperfect data. While this issue has begun to be explored, with some promising first results, we are still quite far from having robust and widely applicable techniques for handling data quality in OBDA.
❯ Develop pragmatic methods for inconsistency-tolerant OBDA to treat more expressive settings that are currently beyond the reach of the state-of-the-art
❯ Exploit qualitative & quantitative reliability information for facts and constraints to refine query results and annotate them with confidence scores
❯ Address a wider range of data quality issues and achieve better overall results by tightly integrating existing data cleaning methods
❯ Develop a customized user-sensitive approach by bringing users into the process, letting them give direction on how to address some types of errors, based upon their knowledge and how data will be used
❯ Enable confident decision making by ensuring that the developed approaches have clear semantics and that it is possible to trace back query results to see which parts of the data and knowledge contributed to a given answer and how (explainability)
❯ Explore how the developed approach can be applied in practice, by means of a use case on clinical data
❯ Demonstrate and promote the project results via implemented tools and experimental evaluation