Chaired by: Heather Hedden (Hedden Information Management), Andreas Blumauer (Semantic Web Company)
Take your chance and become a certified Semantic Web & Knowledge Engineering specialist. PoolParty Academy provides interested SEMANTiCS participants with a certification voucher. The training regularly costs 800 EUR (900 USD). Up to 20 SEMANTiCS participants can complete the certification for free. Start the program 09th of September 2019 in Karlsruhe and join a growing community of industrial Semantic Web professionals (https://www.linkedin.com/groups/4059165).
Slidedecks
Agenda
How Semantic Knowledge Models and Machine Learning Enable Semantic Artificial Intelligence Applications
Module 1: Taxonomies and Ontologies - Theory and Practice
(09:00 - 12:30)
This module provides the fundamentals to the use, standards, design, and creation of knowledge organization systems (KOS) as knowledge models, which include taxonomies, thesauri, and ontologies. Hands-on exercises will focus on best practices for the creation of concepts and relationships. Learning goals:
- Understand the purposes and benefits of taxonomies and ontologies
- Select the appropriate kind knowledge organization system for its implementation
- Understand the structures of taxonomies, hierarchical and faceted
- Create taxonomies, with terms and relationships, according to best practices
- Apply the SKOS standard for interoperability of knowledge organization systems
- Understand the structure and standards (RDF and OWL) of ontologies
- Create ontologies according to standards
- Understand how taxonomies and ontologies relate to linked data and the Semantic Web
- Identify stumbling blocks in the implementation of taxonomies and ontologies
Module 2: Knowledge graphs
(13:30 - 15:30)
Learning goals:
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
Module 3: Semantic AI Applications
(15:30 - 17:00)
Learning goals:
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
