Data and Knowledge Management: Feeding artificial intelligence with data (DKM)

• Official homepage (with schedule)

Description

Reasoning about data or knowledge is a fundamental task that data management systems aim to support. In this course we discuss how to represent and reason about semantic information and introduce the basic concepts of Semantic Web. We also discuss other methods that use statistical and machine learning methods for this purpose. We will also cover topics of information extraction: how to extract (and learn) structured models from unstructured data (in particular, text). We complete the course with some algorithmic techniques that enable to use the models with large-scale data.

Keywords

• Semantic web, ontologies, reasoning, knowledge graphs
• Vector semantics, word embeddings, text processing with neural networks
• Information extraction
• Topic modeling
• Similarity search in high dimensions, locality sensitive hashing
• Bloom filters

Prerequisites

Basic notions in databases, machine learning, probability

Learning outcomes

Competences: Semantic Web, word2vec, information extraction, algorithms for large-scale data

Teachers

Zoltan Miklos (responsible), Sébastien Ferré