Intelligent handling of imperfect data – INTENDED

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 both to the need to integrate data across multiple heterogeneous sources and the ubiquity of data quality issues (e.g. missing or incorrect facts). The ambition of the INTENDED chair is to take active part in the paradigm shift towards explainable & trustable AI by developing intelligent, knowledge-based methods for handling imperfect data, enabling confident and informed decision making.

The starting point for the INTENDED project is ontology-based data access (OBDA), a promising declarative approach to data integration that exploits semantic knowledge and automated reasoning to bridge 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.

To tackle this limitation, the INTENDED research program will (i) develop pragmatic methods for inconsistency-tolerant OBDA to treat more expressive settings, involving richer ontology languages, mappings, and temporal information, currently beyond the reach of the SOA (ii) exploit qualitative & quantitative reliability information for facts and constraints (provided e.g. by rule mining and information extraction tools) to refine query results and annotate them with confidence scores, (iii) address a wider range of data problems and achieve better overall results by developing a holistic approach that tightly integrates existing data cleaning methods (e.g. entity linking, statistical analysis), and (iv) 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. Throughout, we will ensure that the developed approaches have clear semantics and that it is possible to trace back query results to see how different pieces of data and knowledge contributed to a given answer.

To validate our approach, we will implement and test the most promising algorithms and make them available to the scientific community. Moreover, we will demonstrate the practical interest through a hospital usecase, aimed at adding semantic search facilities and reliability indicators to an interface currently being developed for displaying relevant information on incoming emergency care patients.

INTENDED gathers an interdisciplinary team of experts in all related fields (AI, databases, and medecine) with significant experience in ontology-based data access, inconsistency-tolerant query answering, and data and knowledge integration for public health. The project is perfectly in line with the new « Data and Knowledge » theme, part of the restructuring of the PI’s host team at LaBRI, and will develop new collaborations between the PI and Bordeaux Population Health on health data integration, which is strategic for U Bordeaux.

While healthcare is the privileged application, the chair’s results have much broader applicability, and in particular are highly relevant for enterprise data integration, given the increasing interest by companies in using semantics to get more value from their data. Opportunities to valorize the project’s achievements via partnerships with public and private sector organizations will be explored.

INTENDED also includes an ambitious training program, which aims to introduce ontologies and Semantic Web standards (OWL, RDF, SPARQL) to a wide range of student populations, equipping U Bordeaux graduates with unique skills of high relevance to academic research, healthcare, and the private sector.