Semantic Web Technologies for Business and Industry Challenges

The term semantic technologies represents a fairly diverse family of technologies that have been in existence for a long time and seek to help derive meaning from information. Some examples of semantic technologies include natural language processing (NLP), data mining, artificial intelligence (AI), category tagging, and semantic search. Semantic technology reads and tries to understand language and words in its context. Technically, this approach is based on different levels of analysis: morphological and grammatical analysis; logical, sentence and lexical analysis, in other words: natural language analysis. The major focus of this talk will be an overview of Semantic Technologies; what differentiates Semantic Technologies and its organisational needs. The most relevant Semantic Web concepts and methodologies will be discussed that helps us to develop an understanding for which use cases this technology approach provides substantial advantages.

Semantic Web standards are used to describe metadata but also have great potential as a general data format for data communication and data integration. Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning can be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. From this talk you will get acquainted with specialised terminology, which enables us to dive deeper into the semantic technologies field. A general view of tools and methods to develop semantic applications with getting to know in which business domains it can be useful to embrace semantic solutions.

Learn different knowledge modelling approaches to understand which applications require taxonomies or ontologies as underlying knowledge graph. Also, will discuss on the value of consistent metadata of different types and how they add up to a semantic layer around our digital assets. We will see how metadata schemes and their respective values can be managed with controlled vocabularies and taxonomies. Linked Data structure and its serialisation formats such as Resource Description Framework (RDF) and its subject-predicate-object expressions with the fundamental learning of graph based data.

The most important linguistic concepts that get applied for text mining operations will be discussed along with different word forms, homographs, lemmatisation, ambiguity and more. We will also see how linguistic concepts in combination with knowledge modelling are used for semantic text mining. Get an overview on how Semantic Data Integration provides the conversion of data into RDF with a step-by step process on how different data types can be transformed into RDF. SPARQL: The query language of the Semantic Web will be demonstrated with a concrete example on how to make use of multi-facetted data. Semantic Web Architecture for organisations gives an overview on how semantic technology components play together to solve a business use case.

Semantic technologies are algorithms and solutions that bring structure and meaning to information. Semantic Web technologies specifically are those that adhere to a specific set of W3C open technology standards that are designed to simplify the implementation of not only semantic technology solutions, but other kinds of solutions as well.

 
 

Outline/Structure of the Talk

Semantic Technologies (2 mins)
Knowledge Graphs (3 mins)
Linked Data based on Semantic Web Standards (2 mins)
Linguistic Concepts for Text Mining operations (2 mins)
Semantic Text Mining & Data Integration (2 mins)
Query language of the Semantic Web (2 mins)
Semantic Web Architecture (3 mins)
Applications (2 mins)
Q&A (2 min)

Learning Outcome

  • Learn all relevant Semantic Web methodologies and concepts.
  • Learn to relate business challenges with semantic technology solutions.
  • Learn specialised technical vocabulary.
  • Learn the uses and applications of knowledge graphs
  • Learn the Semantic Web methods and technologies in general.

Target Audience

Knowledge Engineers, Data Scientists, Machine Learners, Data Analyst

Prerequisites for Attendees

  • Basic knowledge in Information Management
  • Interest for innovative technologies

Slides


Video


schedule Submitted 1 year ago

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