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3-Day Workshop: Semantic Data Management


The goal of this workshop is to gain a profound understanding of semantic data management as a basis for strategic and technological decisions to improve data quality, interoperability and reusability as well as concrete architectures, integration and implementation at the enterprise level.


The workshop imparts the semantic harmo-nization and validation of data from different sources, their transformation, provision, main-tenance and reuse, construction and fields of application of consistent knowledge bases as enterprise assets, tools and platforms as well as practical migration strategies.

Semantic Data

Harmonization and Compatibility

(Improve Quality, Re-Use Knowledge)

Target Group

The workshop addresses IT decision-makers, product and project managers, division and department heads, business analysts and developers in the strategic part, as well as database and software architects, designers and developers in the technical part.

Daily Schedule

Each of the 3 workshop days is divided into 4 modules of 90 min each. Each day covers a completed topic area, provides basics, details and practical implementation and concludes with concrete learning goals. Sufficient breaks for processing and recovery as well as for questions and answers and the concluding open evening for discussions and a get-to-gether ensure a sustainable learning success.



Alexander Schulze

Osvaldo Aguilar

Day 1

Current Status, Goals, Strategies

Target Group

  • IT decision-makers, project and product managers, division and department heads, business analysts and developers



  • Knowledge of business and work models, strategic and technological goals, corporate organization and processes, and existing IT infrastructures



  • Definition of problem and solution spaces, understanding of the basics of semantics, strengths and weaknesses of semantic databases, predestined application areas


Understanding of the Problem Space

Module 1 - Presentation, expectations, goal coordination, success criteria

  • Getting to know the stakeholders, workshop participants, organization, business units, teams

  • Strategic and technological goals, business models, requirements and tasks

  • Metrics, KPIs and success criteria, time lines, roadmap, milestones

  • Internal and external influencing factors, opportunities and risks, expectations, vision


Module 2 - Current status analysis, technical, organizational, human resources


  • Existing prior knowledge, existing / required expertise, resources, manpower

  • Existing environments, tools, data resources, processes, interfaces

  • Existing relationships, dependencies, hurdles, implementation options

  • Market, internal and external needs, innovation potential and investment awareness


Introduction to the Solution Space

Module 3 - Introduction to Semantic Technologies, Graphs, Ontologies

  • Terminologies and definitions around semantic technologies and databases

  • Introduction to graph theory, ontology concepts and knowledge representation

  • Open World Assumption, RDF/OWL, OWL profiles, graph vs. SQL and NoSQL databases

  • From data to knowledge, predestined fields of application, advantages/disadvantages, added value


Module 4 - Information, Integration and Interoperability, Methodology. Approaches

  • New way of thinking: Semantics and ontologies, concepts of taxonomies, individuals and properties

  • Compatibility through harmonization vs. unification through standardization,

  • static integration with ETL vs. semantic golden Source with OWL

  • Data integration and interoperability, reusability and quality improvement

  • Real life examples for the use of semantic databases in the service sector

Day 2

Technology, Functionality, Tools, Implementation

Target Group

  • Software developers, designers, architects, app and database developers from the technical side, project managers from the conceptual and commercial side



  • Basic understanding of traditional database and programming models, practical experience in the integration of data and applications are helpful



  • Understanding of graph databases, taxonomies and ontologies, understanding of available products and services, languages and tools, practical implementation of a semantic database


Semantic Data Management,
Theory and Basics

Module 1 - Tools, APIs, Modeling, Classes, Individuals, Properties

  • Overview Platforms: Apache Fuseki/Jena, Ontotext GraphDB, Stardog, Amazon Neptune

  • Tools: Stanford (Web-)Protégé, Innotrade Enapso Dash, API's

  • Provision: query and retrieval of knowledge databases, SPARQL vs. SQL

  • Conformance: W3C compliance, REST API, SOAP Services, JSON vs. XML

Module 2 - Ontologies and SPARQL Practice, Inference, Reasoner, Constraints, Business Rules

  • Semantic data management: Extending, changing and deleting knowledge

  • Error Analysis: Restrictions (Constraints) and Rules (Business Rules) in Ontologies

  • Quality management: integrity and consistency of semantic databases, quality vs. effort

  • Logical reasoning and automatic learning: concepts and practice for inference and reasoners


Ontologies in Operation,
Practical Workshop


Module 3 - Establishing a
Semantic Knowledge Base

  • Best Practices: Modeling, taxonomies (class hierarchies), quality and consistency vs. performance

  • SPARQL: W3C compliant language for query and maintenance of ontologies, syntax and query logic

  • Development and Debugging: Traversal and Visualization of Graphs

  • Interactive discussion of concrete, development-related questions and problems in the company


Module 4 - Semantic Data Management
at Enterprise Level


  • Practices: In-/Outbound transformation and validation, caching, big data vs. semantic data

  • Big Knowledge: Named graphs, linking multiple ontologies and complex queries

  • App-Practice: Ontologies in the Web environment: GraphDB with NodeJS backend and ExtJS front end

  • Enterprise-level knowledge management: practical experience reports and examples

Day 3

Non-Functional Aspects, Implementation, Perspectives

Target Group

  • IT decision-makers, product and project managers, software and database developers, DB administrators, DevOps, division and department heads, business analysts and developers



  • Basic understanding of semantic technologies and the operative operation of IT environments, experience in data analytics and IT architectures are helpful



  • Concrete options for the implementation of strategic, technological and operational goals, important practical aspects around semantic data management at enterprise level


Mission, Implementation,

Module 1 - Concrete implementation of strategic goals using semantics


  • Reusability of knowledge and interoperability in heterogeneous app environments, organization of ontologies, modularization, structuring, hierarchies and dependencies

  • Quality assurance: problem identification, validation, transformation, monitoring, reporting

  • Flexibility: synonyms and pattern search, semantic contexts, internationalization

  • Impact Analysis, Implicit Documentation, Auto-API Generation, Knowledge-based User Interfaces

  • Analytics: reporting and KPIs, charts and trends, assistance for forecasts and recommendations


Module 2 - Approaches and implementation
of non-functional aspects


  • Security: authentication & authorization, rights & roles, encryption, façades, audits

  • DevOps: Scaling, High Availability, Performance, Virtualization

  • Cloud: ontologies and graph databases-as-a-service, cloud vs. on-premise

  • Cross-cutting concerns: e.g. version and variant management, change and approval management, tags/labels, attachments, comment history, etc.


In-Depth Study, Practical Reports,
ase Studies, Perspectives


Module 3 -
In-depth study, practical reports

  • Practical implementation: In JavaScript (node.js), the Enapso GraphDB Client (npm)

  • Extension of graph databases, language range, real-time synchronization, GraphDB plug-ins

  • Practice: Lessons learned from previous projects in research and industry, reporting and KPIs

  • Heterogeneous quality improvements: data quality, code quality, service quality, operational quality

Module 4 -
Case Studies, Perspectives

  • Practice: Application of semantic technologies to concrete case studies of the company

  • (ideally by prior arrangement)

  • Perspective: Interactive development of a concrete solution approach for a company-specific use case (ideally by prior arrangement)

  • Discussion: Questions and answers, visions and perspectives

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