Ontologies, reasoning, and enterprise knowledge representation.
24
Courses
2
Subcategories
359h+
Total Hours
All levels
Difficulty Range
Step 0 Introduction au Knowledge Management
Discover the foundations of Knowledge Management: how organizations capture, organize, share, and leverage collective knowledge. This introductory course covers KM frameworks, ontologies, taxonomies, and the role of AI in modern knowledge systems.
Description Logics & OWL
Expressivity-tractability trade-offs in description logics and OWL 2 profiles for ontology design.
Query Answering Complexity
Computational complexity of conjunctive query answering over ontologies and databases.
Datalog, Fixed-Point Semantics & Rule Learning
Datalog programming, fixpoint semantics, and inductive rule learning for knowledge bases.
Ontology Learning & Inductive Logic Programming
Automated ontology construction from data using ILP, concept learning, and ontology enrichment methods.
SPARQL/CRPQ Semantics & Homomorphism Theory
Formal semantics of SPARQL and conjunctive regular path queries with homomorphism-based evaluation.
Inconsistency & Non-Monotonic Reasoning
Handling inconsistent knowledge bases via paraconsistent logics, belief revision, and non-monotonic reasoning.
Knowledge Compilation & Probabilistic Circuits
Compile knowledge into tractable representations (OBDDs, SDDs) for efficient probabilistic inference.
Probabilistic Relational Models & Markov Logic Networks
Combine first-order logic and probability for relational learning using Markov logic and related frameworks.
Probabilistic Databases & Uncertain Reasoning
Query evaluation over probabilistic databases: tuple-independent, block-independent, and general models.
Graph Spectral Theory & Laplacians
Spectral properties of graph Laplacians and their applications to clustering, embedding, and GNNs.
KG Embeddings: Identifiability & Generalization
Theoretical analysis of knowledge graph embedding models: expressivity, identifiability, and generalization bounds.