The web has become an object of our daily life and the amount of information there is ever growing. Besides plain texts, multimedia such as graphics, audio or video have become a predominant part of the web's information traffic. But, how can we find useful information within this huge information space? How can we make use of the knowledge contained in those web documents?
Traditional search engines for example will reach the limits of their power, when it comes to understanding information content. The Semantic Web is an extension of the traditional web in the sense that information in the form of natural language text in the web will be complemented by its explicit semantics based on a formal knowledge representation. Thus, the meaning of information expressed in natural language can be accessed in an automated way and interpreted correctly, i.e. it can be “understood“ by machines. Semantic Web technologies enable the explicit representation of knowledge and its further processing to deduce new knowledge from implicitly hidden knowledge. Previously heterogeneous data can be mapped and combined based on common knowledge representation and schemata easily extended in a dynamic way. In this lecture, you will learn the fundamentals of Semantic Web technologies and how they are applied for knowledge representation in the Web of Data.
(1) Semantic Web Basic Architecture
RDF (Resource Description Framework), RDFS, RDFa, schema.org, microformats, SPARQL Protocol And RDF Query Language
(2) Knowledge Representation and Mathematical Logic
Ontologies, Propositional Logic, First Order Logic, Description Logics, OWL (Web Ontology Language), RIF (Rule Interchange Format)
(3) Applications in the ‚Web of Data‘
Semantic Web Programming, Ontology Engineering, Named Entity Resolution, Linked Data Engineering, Semantic Web APIs, RDF Stores, Semantic Search, Semantic Multimedia
This lecture applies blended learning with video lectures from openHPI and more in depth discussions in the lecture and lab course.