Semantic Technologies: Enabler for Knowledge 4.0

  • Achim Rettinger
  • Stefan Zander
  • Maribel Acosta
  • York Sure-VetterEmail author
Part of the Progress in IS book series (PROIS)


Semantic technologies are a key enabler for Knowledge 4.0. Specifically, knowledge graphs have caused significant practical implications for managing knowledge in the digital economy. While most semantic technologies originate from the vision of representing the existing Web in a machine-processable format, it’s most notable success so far are large cross-domain knowledge graphs. They are created by collaborative human modelling and linking of structured and semi-structured data. So far, they exhibit only little but still very powerful semantics, which have shown benefits for numerous applications. This chapter introduces the latest innovations in modelling knowledge using knowledge graphs and explains how those knowledge graphs enable value creation by making unstructured content, like text documents accessible by machines and humans. Finally, we show how semantic technologies help to make hard- and software components in cyber physical systems interoperable.


  1. Acosta, M., Simperl, E., Flöck, F., & Vidal, M.-E. (2015). HARE: A hybrid SPARQL engine to enhance query answers via crowdsourcing. In Proceedings of the 8th International Conference on Knowledge Capture. New York: ACM.Google Scholar
  2. Acosta, M., Simperl, E., Flöck, F., & Vidal, M.-E. (2017). Enhancing answer completeness of SPARQL queries via crowdsourcing. Web Semantics: Science, Services and Agents on the World Wide Web.Google Scholar
  3. Acosta, M., Vidal, M.-E., Lampo, T., Castillo, J., & Ruckhaus, E. (2011). ANAPSID: An adaptive query processing engine for SPARQL endpoints. The Semantic Web–ISWC 2011, 18–34.Google Scholar
  4. Arenas, M., Bertails, A., Prud, E., & Sequeda, J. (2012). A direct mapping of relational data to RDF. W3C Recommendation. See
  5. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). Dbpedia: A nucleus for a web of open data. The Semantic Web, 722–735.Google Scholar
  6. Auer, S., Scerri, S., Versteden, A., Pauwels, E., Charalambidis, A., & Konstantopoulos, S., et al. (2017). The BigDataEurope platform–Supporting the variety dimension of big data. In International Conference on Web Engineering. Berlin: Springer.Google Scholar
  7. Baader, F. (2003). The description logic handbook: Theory, implementation and applications. Cambridge: Cambridge University Press.Google Scholar
  8. Bao, J. (2012, December). OWL 2 Web Ontology Language document overview. W3C Recommendation. World Wide Web Consortium, 201(2).Google Scholar
  9. Björkelund, A., Malec, J., Nilsson, K., & Nugues, P. (2011). Knowledge and skill representations for robotized production. IFAC Proceedings Volumes, 44(1), 8999–9004.CrossRefGoogle Scholar
  10. Charalambidis, A., Troumpoukis, A., & Konstantopoulos, S. (2015). SemaGrow: Optimizing federated SPARQL queries. In Proceedings of the 11th International Conference on Semantic Systems. New York: ACM.Google Scholar
  11. Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., & Murphy, K., et al. (2014). Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM.Google Scholar
  12. Drath, R., Luder, A., Peschke, J., & Hundt, L. (2008). AutomationML-the glue for seamless automation engineering. In IEEE International Conference on Emerging Technologies and Factory Automation, 2008. ETFA 2008. New York: IEEE.Google Scholar
  13. Färber, M., Bartscherer, F., Menne, C., & Rettinger, A. (2016). Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semantic Web(Preprint), 1–53.Google Scholar
  14. Franklin, M. J., Kossmann, D., Kraska, T., Ramesh, S., & Xin, R. (2011). CrowdDB: Answering queries with crowdsourcing. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. New York: ACM.Google Scholar
  15. Görlitz, O., & Staab, S. (2011). Splendid: Sparql endpoint federation exploiting void descriptions. In Proceedings of the Second International Conference on Consuming Linked Data-Volume 782, Scholar
  16. Kovalenko, O., Wimmer, M., Sabou, M., Lüder, A., Ekaputra, F. J., & Biffl, S. (2015). Modeling automationml: Semantic web technologies vs. model-driven engineering. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). New York: IEEE.Google Scholar
  17. Krötzsch, M., Simancik, F., & Horrocks, I. (2014). Description logics. IEEE Intelligent Systems, 29, 12–19.CrossRefGoogle Scholar
  18. Krötzsch, M., Vrandecic, D., & Völkel, M. (2006). Semantic mediawiki. In International Semantic Web Conference. Berlin: Springer.Google Scholar
  19. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., & Mendes, P. N. (2015). DBpedia–A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 6(2), 167–195.Google Scholar
  20. Lenzerini, M. (2002). Data integration: A theoretical perspective. In Proceedings of the Twenty-first ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. New York: ACM.Google Scholar
  21. Marcus, A., Wu, E., Karger, D. R., Madden, S., & Miller, R. C. (2011). Crowdsourced databases: Query processing with people, Cidr.Google Scholar
  22. Motik, B., Grau, B. C., Horrocks, I., Wu, Z., Fokoue, A., & Lutz, C. (2009). OWL 2 web ontology language profiles. W3C Recommendation, 27, 61.Google Scholar
  23. Park, H., & Widom, J. (2013). Query optimization over crowdsourced data. Proceedings of the VLDB Endowment, 6(10), 781–792.CrossRefGoogle Scholar
  24. Paul, C., Rettinger, A., Mogadala, A., Knoblock, C. A., & Szekely, P. (2016). Efficient graph-based document similarity. In International Semantic Web Conference. Berlin: Springer.Google Scholar
  25. Rudolph, S. (2011). Foundations of description logics. In Reasoning Web. Semantic Technologies for the Web of Data (pp. 76–136). Berlin: Springer.Google Scholar
  26. Schleipen, M., Pfrommer, J., Aleksandrov, K., Stogl, D., Escaida, S., Beyerer, J., & Hein, B. (2014). Automationml to describe skills of production plants based on the ppr concept. In 3rd AutomationML User Conference.Google Scholar
  27. Schlenoff, C., Prestes, E., Madhavan, R., Goncalves, P., Li, H., & Balakirsky, S., et al. (2012). An IEEE standard ontology for robotics and automation. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE.Google Scholar
  28. Schwarte, A., Haase, P., Hose, K., Schenkel, R., & Schmidt, M. (2011). Fedx: Optimization techniques for federated query processing on linked data. In International Semantic Web Conference. Berlin: Springer.Google Scholar
  29. Singhal, A. (2012). Introducing the knowledge graph: Things, not strings. 2016.
  30. Souripriya, D., Seema, S., & Richard, C. (2012). R2RML: RDB to RDF Mapping Language, W3C Recommendation.Google Scholar
  31. Tenorth, M., & Beetz, M. (2013). KnowRob: A knowledge processing infrastructure for cognition-enabled robots. The International Journal of Robotics Research, 32(5), 566–590.CrossRefGoogle Scholar
  32. Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.CrossRefGoogle Scholar
  33. Welty, C., Barker, K., Aroyo, L., & Arora, S. (2012). Query driven hypothesis generation for answering queries over nlp graphs. In International Semantic Web Conference. Berlin: Springer.Google Scholar
  34. Zander, S., & Awad, R. (2015). Expressing and reasoning on features of robot-centric workplaces using ontological semantics. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE.Google Scholar
  35. Zander, S., & Hua, Y. (2016, Feburary). Utilizing ontological classification systems and reasoning for cyber-physical systems. In Karlsruhe Service Summit Research Workshop.Google Scholar
  36. Zander, S., Merkle, N., & Frank, M. (2016). Enhancing the utilization of IoT devices using ontological semantics and reasoning. Procedia Computer Science, 98, 87–90.CrossRefGoogle Scholar
  37. Zhang, L., & Rettinger, A. (2014). X-LiSA: cross-lingual semantic annotation. Proceedings of the VLDB Endowment, 7(13), 1693–1696.CrossRefGoogle Scholar
  38. Zhang, L., Thalhammer, A., Rettinger, A., Färber, M., Mogadala, A., & Denaux, R. (2017). The xLiMe system: Cross-lingual and cross-modal semantic annotation, search and recommendation over live-TV, news and social media streams. Web Semantics: Science, Services and Agents on the World Wide Web.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Achim Rettinger
    • 1
  • Stefan Zander
    • 2
  • Maribel Acosta
    • 1
  • York Sure-Vetter
    • 1
    Email author
  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.Darmstadt University of Applied SciencesDarmstadtGermany

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