Department Seminar Series

Ontology Alignment and the two DL

5th December 2023, 14:00 add to calenderELEC 205 E5 Electrical Engineering, Lecture Room E5
Dr. Ernesto Jiménez-Ruiz
City, University of London


The Ontology Matching community has been very active since the first steps of the Semantic Web. The Ontology Alignment Evaluation Initiative (OAEI) has been running annually since 2004. The objective of the OAEI is to perform a systematic evaluation of ontology matching systems to conduct a comparison on the same basis and to enable the reproducibility of the results. The OAEI includes several tracks of different nature and in a diverse set of domains, each of them including one or more matching tasks.

Despite the amazing evaluation and system development efforts around the Ontology Matching community, there are still several challenges that need to be tackled from both the evaluation and system sides: (i) better connection with real-world needs and user involvement/satisfaction, (ii) discovery of mappings beyond atomic subsumption and equivalence, (iii) combination with machine learning methods, and (iv) awareness of the logical compatibility of the ontologies.

In the presentation I will give an overview of the OAEI and the above challenges with a special focus on challenges (iii) and (iv), i.e., the two DLs (Deep Learning and Description Logics). While Deep Learning techniques are introducing elegant solutions with impressive results, the Ontology Matching community should not forget about the need of computing alignment sets that preserve the logical consistency (possibly with only intended entailments) of the integrated ontology (assuming that the alignment is interpreted as a set of Description Logic axioms).
add to calender (including abstract)


I am a Lecturer in Artificial Intelligence (AI) and Senior Tutor for Research at City, University of London affiliated to the Adaptive Computer Systems and Machine Learning group. I am also one of the chairs of the Alan Turing Institute Interest Group on Knowledge Graphs.

I previously held a Senior Research Associate position at The Alan Turing Institute in London (UK), a Research Assistant position at the University of Oxford and a part-time research position in the Centre for Scalable Data Access (SIRIUS) at the University of Oslo, Norway. My home university (Universitat Jaume I, Castellon, Spain) awarded a “Premio extraordinario de doctorado” (roughly translated as a Extraordinary Doctoral Award) to my doctoral thesis (Engineering category 2010-2011) for my research conducted within the Temporal Knowledge Bases Group.

My research, over the past 15 years, has covered several areas within AI. Currently, I focus on the intersection between Knowledge Representation (e.g., Knowledge Graphs) and Machine Learning with a special interest in creating more reliable and robust AI solutions. Knowledge graphs play a key role in AI and Data Science to enhance data-driven techniques with, among others, context and explainability.