Knowledge Graphs and Semantic Web technologies (2024)

Welcome to Knowledge Graphs and Semantic Web technologies

Organizer: Daniele Dell’Aglio, Matteo Lissandrini

Lecturers: Ernesto Jiménez-Ruiz, University of London, UK

ECTS: 2

Date/Time: 25-26 April 2024

Deadline: 4 April 2024

Max no. Of participants: 20

Description: The course will provide a practical combination of data management technology, knowledge representation and artificial intelligence. More specifically, the course will cover the following points:

(i) Knowledge Graphs and Knowledge representation. Although they are getting increased popularity, knowledge graphs are not new. Knowledge graphs have their roots in the areas of knowledge representation and Semantic Web. This course will provide an overview of ontology-based knowledge graphs following World Wide Web Consortium (W3C) standards.

(ii) Inference and Reasoning Engines. Reasoners are an important component of the Semantic Web, as they enable the inference of (implicit) knowledge from known axioms, rules and facts. Graph databases, in particular triplestores, are key engines to perform efficient reasoning over knowledge graphs.

(iii) Ontology and entity alignment. Ontology alignment is key to enable interoperability in the Semantic Web. Ontology alignment is also a useful technique in classical data integration, especially when dealing with the semantic heterogeneity problem.

(iv) Applications to Data Science. Gaining semantic understanding of arbitrary data sources (e.g., tabular data in the form of csv files) will be very valuable for data integration, data cleaning, data mining, machine learning and knowledge discovery tasks. For example, understanding what the data is can help assess what sorts of transformations are appropriate on the data.

(v) Machine learning and knowledge graphs. Machine learning and knowledge graphs (and ontologies) can complement each other. Machine learning approaches, especially neural networks, are typically noise tolerant and can generalize the inference power of ontologies and knowledge graphs. On the other hand, ontologies and knowledge graphs provide sound reasoning capabilities and can provide enhanced explainability to (black-box) machine learning models.

Prerequisites: Familiarity with python or Java.

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.