This module will teach students the basics of semantic information extraction.
It will cover the concepts, methods, and algorithms to extract factual information from text in order to construct a coherent knowledge base.
This includes some NLP (Part-of-Speech tagging, Dependency Parsing, etc.), and the techniques and concepts of entity disambiguation, instance extraction, the extraction from semi-structured sources (Wrapper Induction, Wikipedia-based approaches), the extraction from unstructured sources (e.g., by Pattern-based approaches), and the extraction by Soft Reasoning (Markov Logic, MAX SAT, etc.).
We will also cover the design of extraction approaches in general (Evaluation, Iteration, etc.), and the alignment of knowledge bases in the Linked Open Data framework.
Propositional & First Order Logic Basics of the Web (HTTP, HTML, (Web forms), XML, ...) Basics of the Semantic Web (knowledge representation, RDF, OWL,...) Graph Theory Java programming
It will cover the concepts, methods, and algorithms to extract factual information from text in order to construct a coherent knowledge base.
This includes some NLP (Part-of-Speech tagging, Dependency Parsing, etc.), and the techniques and concepts of entity disambiguation, instance extraction, the extraction from semi-structured sources (Wrapper Induction, Wikipedia-based approaches), the extraction from unstructured sources (e.g., by Pattern-based approaches), and the extraction by Soft Reasoning (Markov Logic, MAX SAT, etc.).
We will also cover the design of extraction approaches in general (Evaluation, Iteration, etc.), and the alignment of knowledge bases in the Linked Open Data framework.
Propositional & First Order Logic Basics of the Web (HTTP, HTML, (Web forms), XML, ...) Basics of the Semantic Web (knowledge representation, RDF, OWL,...) Graph Theory Java programming
- Enseignant responsable de l'UE: Fabian Suchanek