Data Enrichment in Discovery Systems Using Linked Data

  • Dominique RitzeEmail author
  • Kai Eckert
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The Linked Data Web is an abundant source for information that can be used to enrich information retrieval results. This can be helpful in many different scenarios, for example to enable extensive multilingual semantic search or to provide additional information to the users. In general, there are two different ways to enrich data: client-side and server-side. With client-side data enrichment, for instance by means of JavaScript in the browser, users can get additional information related to the results they are provided with. This additional information is not stored within the retrieval system and thus not available to improve the actual search. An example is the provision of links to external sources like Wikipedia, merely for convenience. By contrast, an enrichment on the server-side can be exploited to improve the retrieval directly, at the cost of data duplication and additional efforts to keep the data up-to-date. In this paper, we describe the basic concepts of data enrichment in discovery systems and compare advantages and disadvantages of both variants. Additionally, we introduce a JavaScript Plugin API that abstracts from the underlying system and facilitates platform independent client-side enrichments.


Data Enrichment Semantic Web Recommendation Database Detail Page Database Tier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We like to thank Bernd Fallert for his great work on the implementation.


  1. Berners-Lee, T. (2006). Linked data - design issues.
  2. Blenkle, M., Ellis, R. & Haake, E. (2009). E-LIB Bremen – Automatische Empfehlungsdienste für Fachdatenbanken im Bibliothekskatalog/Metadatenpools als Wissensbasis für bestandsunabhängige Services. Bibliotheksdienst, 43(6), 618–627.CrossRefGoogle Scholar
  3. Boland, K., Ritze, D., Eckert, K., & Mathiak, B. (2012). Identifying references to datasets in publications. In P. Zaphiris, G. Buchanan, E. Rasmussen, & F. Loizides (Hrsg.) Proceedings of the Second International Conference on Theory and Practice of Digital Libraries (TDPL 2012), Paphos, Cyprus, September 23–27, 2012. Lecture notes in computer science (Vol. 7489, pp. 150–161). Berlin: Springer.Google Scholar
  4. Bonte, A., et al. (2011). Brillante Erweiterung des Horizonts: Eine multilinguale semantische Suche für den SLUB-Katalog. BIS, 4(4), 210–213.Google Scholar
  5. Danowski, P., & Pfeifer, B. (2007). Wikipedia und Normdateien: Wege der Vernetzung am Beispiel der Kooperation mit der Personennamendatei. In Bibliothek Forschung und Praxis (Vol. 31, pp. 149–156), Nr. 2 [ISSN 0341-4183].Google Scholar
  6. Geyer-Schulz, A., Hahsler, M., Neumann, A., & Thede, A. (2003). An integration strategy for distributed recommender services in legacy library systems. In Between data science and applied data analysis studies in classification, data analysis, and knowledge organization (pp. 412–420).Google Scholar
  7. Mönnich, M., & Spiering, M. (2008). Adding value to the library catalog by implementing a recommendation system. D-Lib Magazine, 14(5/6), May/June 2008.Google Scholar
  8. Rumpf, L. (2012). Open Catalog: Eine neue Präsentationsmöglichkeit von Bibliotheksdaten im Semantic Web? Perspektive Bibliothek, 1(1), 56–80.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Mannheim University LibraryMannheimGermany
  2. 2.University of MannheimMannheimGermany

Personalised recommendations