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LINDASearch: a faceted search system for linked open datasets

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Abstract

The importance of Linked Data lies on the fact that its practices and principles have been adopted by an increasing number of data providers, resulting in the creation of a data space on the Web containing billions of RDF Triples and accessible worldwide throughout the Internet. RDF datasets can be queried by tools and applications for searching and gathering information. However and due to the huge amount and types of dataset, the selection and reuse of data resources is not easy task. Therefore, a metasearch system for open Linked Data projects called LINDASearch (LINDASearch stands for Linked Data Search) is introduced. LINDASearch provides a middleware architecture in order to provide information about the most known Open Linked Data Projects such as DBpedia, The GeoNames geographical database, LinkedGeoData, FOAF profiles, Global Health Observatory, Linked Movie DataBase (LinkedMDB) and World Bank Linked Data. This paper describes the LINDASearch’s architecture as well as its functionality through one case study divided in two scenarios in order to show the architecture’s functionality and present the results obtained from each scenario.

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References

  1. Allemang, D., & Hendler, J. (2011). Semantic web for the working ontologist: Effective modeling in RDFS and OWL. Amsterdam: Elsevier.

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O., et al. (2001). The semantic web. Scientific American, 284(5), 28–37.

    Article  Google Scholar 

  3. Berners-Lee, T. (2009). Linked Data. Linked Data, 2009. [Online]. https://www.w3.org/DesignIssues/LinkedData.html. Accessed 16 Mar 2019.

  4. Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology, 1(1), 1–136.

    Article  Google Scholar 

  5. Bizer, C., Heath, T., & Berners-Lee, T. (2011). Linked data: The story so far. In Semantic services, interoperability and web applications: emerging concepts, IGI Global, pp. 205–227.

  6. Anam, S., Kang, B. H., Kim, Y. S., & Liu, Q. (2015). Linked data provenance: State of the art and challenges. In 3rd Australasian web conference (AWC 2015), 2015, vol. 166, pp. 19–28.

  7. Rodríguez-Enríquez, G., Alor-Hernández, C. A., Cortés-Robles, G. (2013). LiDIA: A integration architecture to query linked open data from multiple datasets. In Workshops proceedings in the Mexican international conference on computer science (ENC 2013), pp. 64–68.

  8. Freitas, A., Oliveira, J. G., O’Riain, S., da Silva, J. C. P., & Curry, E. (2013). Querying linked data graphs using semantic relatedness: A vocabulary independent approach. Data & Knowledge Engineering, 88, 126–141.

    Article  Google Scholar 

  9. Lashkari, F., Ensan, F., Bagheri, E., & Ghorbani, A. A. (2017). Efficient indexing for semantic search. Expert Systems with Applications, 73, 92–114.

    Article  Google Scholar 

  10. Achichi, M., Bellahsene, Z., Ellefi, M. B. & Todorov, K. (2019). Linking and disambiguating entities across heterogeneous RDF graphs. Journal of Web Semantics, 55, 108–121. https://doi.org/10.1016/j.websem.2018.12.003

    Article  Google Scholar 

  11. Groth, P., Loizou, A., Gray, A. J. G., Goble, C., Harland, L., & Pettifer, S. (2014). API-centric linked data integration: The open PHACTS discovery platform case study. Web Semantics: Science, Services and Agents on the World Wide Web, 29, 12–18.

    Article  Google Scholar 

  12. Özacar, T. (2016). A tool for producing structured interoperable data from product features on the web. Information Systems, 56, 36–54.

    Article  Google Scholar 

  13. König, M., Dirnbek, J., & Stankovski, V. (2013). Architecture of an open knowledge base for sustainable buildings based on linked data technologies. Automation in Construction, 35, 542–550.

    Article  Google Scholar 

  14. Radzimski, M., Sánchez-Cervantes, J. L., Garcia-Crespo, A., & Temiño-Aguirre, I. (2014). Intelligent architecture for comparative analysis of public companies using semantics and XBRL data. International Journal of Software Engineering and Knowledge Engineering, 24(05), 801–823.

    Article  Google Scholar 

  15. Ermilov, I., Martin, M., Lehmann, J., & Auer, S. (2013). Linked open data statistics: Collection and exploitation. In P. Klinov & D. Mouromtsev (Eds.), Knowledge engineering and the semantic web SE—19 (Vol. 394, pp. 242–249). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  16. Bikakis, N., Tsinaraki, C., Stavrakantonakis, I., Gioldasis, N., & Christodoulakis, S. (2015). The SPARQL2XQuery interoperability framework. World Wide Web, 18(2), 403–490.

    Article  Google Scholar 

  17. Fionda, V., Gutierrez, C., & Pirrò, G. (2014). The swget portal: Navigating and acting on the web of linked data. Web Semantics: Science, Services and Agents on the World Wide Web, 26, 29–35.

    Article  Google Scholar 

  18. Fionda, V., Gutierrez, C., & Pirró, G. (2013). Semantic navigation on the web of data: Specification of routes, web fragments and actions. In Proceedings of the 21st international conference on world wide web, 2012, pp. 281–290.

  19. Bottoni, P., & Ceriani, M. (2014). SWOWS and dynamic queries to build browsing applications on linked data. Journal of Visual Languages & Computing, 25(6), 738–744.

    Article  Google Scholar 

  20. Ouksili, H., Kedad, Z., Lopes, S., & Nugier, S. (2018). Pattern oriented RDF graphs exploration. Data & Knowledge Engineering, 113, 171–183.

    Article  Google Scholar 

  21. Bellini, P., Nesi, P., & Venturi, A. (2014). Linked open graph: Browsing multiple SPARQL entry points to build your own LOD views. Journal of Visual Languages & Computing, 25(6), 703–716.

    Article  Google Scholar 

  22. Xu, B., & Zhuge, H. (2014). Automatic faceted navigation. Future Generation Computer Systems, 32, 187–197.

    Article  Google Scholar 

  23. Xu, B., & Zhuge, H. (2014). Faceted navigation through keyword interaction. World Wide Web, 17(4), 671–689.

    Article  Google Scholar 

  24. Kim, H.-J., Zhu, Y., Kim, W., & Sun, T. (2014). Dynamic faceted navigation in decision making using semantic web technology. Decision Support Systems, 61, 59–68.

    Article  Google Scholar 

  25. Zhu, Y., Jeon, D., Kim, W., Hong, J. S., Lee, M., Wen, Z., & Cai, Y. (2013) The dynamic generation of refining categories in ontology-based search. In Semantic technology, pp. 146–158.

  26. Marie, N., Gandon, F., & Ribière, M. (2013). Exploratory search on the top of DBpedia chapters with the discovery hub application. In P. Cimiano, M. Fernández, V. Lopez, S. Schlobach, & J. Völker (Eds.), The semantic web: ESWC 2013 satellite events SE—45 (Vol. 7955, pp. 287–288). Berlin: Springer.

    Chapter  Google Scholar 

  27. Fafalios, P., Salampasis, M., & Tzitzikas, Y. (2013). Exploratory patent search with faceted search and configurable entity mining. In Proceedings integration IR technologies for professional search (in conjuction with ECIR 2013).

  28. Dimitrova, V., Lau, L., Thakker, D., Yang-Turner, F., & Despotakis, D. (2013). Exploring exploratory search: A user study with linked semantic data. In Proceedings of the 2nd international workshop on intelligent exploration of semantic data, pp. 2:1—2:8.

  29. Stadler, C., Martin, M., Auer, S. (2014). Exploring the web of spatial data with facete. In Proceedings of the companion publication of the 23rd international conference on world wide web companion, pp. 175–178.

  30. Ferré, S., & Hermann, A. (2011). Semantic search: Reconciling expressive querying and exploratory search. In The semantic web—ISWC 2011, pp. 177–192.

  31. Ferré, S., & Hermann, A. (2012). Reconciling faceted search and query languages for the semantic web. International Journal of Metadata, Semantic Ontolology, 7(1), 37–54.

    Article  Google Scholar 

  32. Ferré, S. (2014). Expressive and scalable query-based faceted search over SPARQL endpoints. In P. Mika, T. Tudorache, A. Bernstein, C. Welty, C. Knoblock, D. Vrandečić, P. Groth, N. Noy, K. Janowicz, & C. Goble (Eds.), The semantic web—ISWC 2014 SE—28 (Vol. 8797, pp. 438–453). Berlin: Springer.

    Chapter  Google Scholar 

  33. Arenas, M., Cuenca Grau, B., Kharlamov, E., Marciuska, S., & Zheleznyakov, D. (2014). Faceted search over ontology-enhanced RDF data. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pp. 939–948.

  34. Lukovnikov, D., Stadler, C., & Lehmann, J. (2014). LD viewer—linked data presentation framework. In Proceedings of the 10th international conference on semantic systems, pp. 124–131.

  35. Hoefler, P., Granitzer, M., Veas, E., & Seifert, C. (2014). Linked data query wizard: A novel interface for accessing SPARQL endpoints. In Proceedings of linked data web WWW, 2014.

  36. Latif, A., Afzal, M. T., Hoefler, P., Saeed, A. U., & Tochtermann, K. (2009). Turning keywords into URIs: Simplified user interfaces for exploring linked data. In Proceedings of the 2nd international conference on interaction sciences: Information technology, culture and human, pp. 76–81.

  37. Latif, A., Afzal, M. T., Saeed, A. U., Hoefler, P., Tochtermann, K. (2009). CAF-SIAL: Concept aggregation framework for structuring informational aspects of linked open data. In 2009 First international conference on networked digital technologies, pp. 100–105.

  38. CODE (2014). Commercially empowered linked open data ecosystems in research. [Online]. http://code-research.eu. Accessed 16 Mar 2019.

  39. Stegmaier, F., Seifert, C., Kern, R., Höfler, P., Bayerl, S., Granitzer, M., Kosch, H., Lindstaedt, S., Mutlu, B., Sabol, V., Schlegel, K., & Zwicklbauer, S. (2014) Unleashing semantics of research data. In Specifying big data benchmarks, pp. 103–112.

  40. Tunkelang, D. (2009). Faceted search. Synthesis Lectures on Information Concepts, Retrieval, and Services, 1(1), 1–80.

    Article  Google Scholar 

  41. Moreno-Vega, J., & Hogan, A. (2018). GraFa: Scalable faceted browsing for RDF graphs. In The semantic web—ISWC 2018, pp. 301–317.

  42. Shah, N., Willick, D., & Mago, V. (2018). A framework for social media data analytics using Elasticsearch and Kibana. Wireless Networks.

  43. Sattarian, M., Rezazadeh, J., Farahbakhsh, R., & Bagheri, A. (2019). Indoor navigation systems based on data mining techniques in internet of things: A survey. Wireless Networks, 25(3), 1385–1402.

    Article  Google Scholar 

  44. Schmachtenberg, M., Paulheim, H., & Bizer, C. (2014). Adoption of linked data best practices in different topical domains. In The 13th international semantic web conference (ISWC2014), lecture notes in computer science, vol. 8796, Riva del Garda-Trentino, Italy: Springer, pp. 245–260.

  45. Volz, J., Bizer, C., Gaedke, M., & Kobilarov, G. (2009). Silk-a link discovery framework for the web of data. LDOW, vol. 538.

  46. Bauer, M., Kaltenböck, F. (2016). Linked open data: The essentials the climate knowledge brokering edition, 1st ed. Vienna, Austria: edition mono/monochrom.

  47. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). DBpedia: A nucleus for a web of open data. In The semantic web, pp. 722–735.

  48. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., et al. (2009). DBpedia: A crystallization point for the web of data. Journal of Web Semantics, 7(3), 154–165.

    Article  Google Scholar 

  49. Law, D. (1997). DESMET: A methodology for evaluating software engineering methods and tools. Computing & Control Engineering Journal, 8(3), 120–126.

    Article  Google Scholar 

  50. Hildebrand, M., van Ossenbruggen, J., & Hardman, L. (2006). /facet: A Browser for heterogeneous semantic web repositories. In The semantic web—ISWC 2006, pp. 272–285.

  51. Marie, N., Gandon, F., Ribière, M., & Rodio, F. (2013). Discovery hub: On-the-fly linked data exploratory search. In Proceedings of the 9th international conference on semantic systems, pp. 17–24.

  52. Saaty, R. W. (1987). The analytic hierarchy process—: What it is and how it is used. Mathematical Modelling, 9(3), 161–176.

    Article  MathSciNet  MATH  Google Scholar 

  53. Saaty, T. L. (2005). Analytic hierarchy process. In Encyclopedia of biostatistics. American Cancer Society.

  54. Vidal, L.-A., Marle, F., & Bocquet, J.-C. (2011). Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert Systems with Applications, 38(5), 5388–5405.

    Article  Google Scholar 

  55. Lee, S., Kim, W., Kim, Y. M., & Oh, K. J. (2012). Using AHP to determine intangible priority factors for technology transfer adoption. Expert Systems with Applications, 39(7), 6388–6395.

    Article  Google Scholar 

  56. Subramanian, N., & Ramanathan, R. (2012). A review of applications of analytic hierarchy process in operations management. International Journal of Production Economics, 138(2), 215–241.

    Article  Google Scholar 

  57. Stefaner, M., & Muller, B. (2007). Elastic lists for facet browsers. In 18th international workshop on database and expert systems applications (DEXA 2007), pp. 217–221.

  58. Levenshtein, V. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707–710.

    MathSciNet  Google Scholar 

  59. Beek, W., Schlobach, S., & van Harmelen, F. (2016). A contextualised semantics for owl:sameAs. In The semantic web. Latest advances and new domains, pp. 405–419.

  60. Correndo, G., Penta, A., Gibbins, N., & Shadbolt, N. (2012). Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud. In Database and expert systems applications, pp. 215–230.

  61. Halpin, H., Hayes, P. J., McCusker, J. P., McGuinness, D. L., & Thompson, H. S. (2010). When owl:sameAs Isn’t the same: An analysis of identity in linked data. In The semantic web—ISWC 2010, 2010, pp. 305–320.

  62. Kagawa, K., Tamagawa, S., & Yamaguchi, T. (2014). An automatic sameAs link discovery from wikipedia. In Semantic technology, pp. 399–413.

  63. Sánchez-Cervantes, J. L., Hernández-Chan, G. S., Radzimski, M., Gómez-Berbís, J. M., & García-Crespo, Á. (2013). Discovering and linking financial data on the web. In IARIA, 2013, the second international conference on data analytics (data analytics 2013), pp. 36–40.

  64. Ting, K. M. (2010). Precision and recall. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (p. 781). Boston, MA: Springer.

    Google Scholar 

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Acknowledgements

Authors are grateful to the Tecnológico Nacional de Mexico (TecNM) for supporting this work. This research paper was also sponsored by the Mexico’s National Council of Science and Technology (CONACYT) and the México’s Secretariat of Public Education (SEP) through the PRODEP program.

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Authors and Affiliations

  1. CONACYT-Instituto Tecnológico de Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, C.P. 94320, Orizaba, Mexico

    José Luis Sánchez-Cervantes

  2. Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, C.P. 94320, Orizaba, Mexico

    Luis Omar Colombo-Mendoza & Giner Alor-Hernández

  3. Universidad Autónoma de Ciudad Juárez, Av. Plutarco Elias Calles 1210, Col. Foviste Chamizal, C.P. 31310, Ciudad Juárez, Mexico

    Jorge Luis García-Alcaráz

  4. Computer Science Department, Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28911, Madrid, Spain

    José María Álvarez-Rodríguez

  5. ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid Campus de Montegancedo, Boadilla del Monte, 28660, Madrid, Spain

    Alejandro Rodríguez-González

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  1. José Luis Sánchez-Cervantes
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Correspondence to Giner Alor-Hernández.

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Sánchez-Cervantes, J.L., Colombo-Mendoza, L.O., Alor-Hernández, G. et al. LINDASearch: a faceted search system for linked open datasets. Wireless Netw 26, 5645–5663 (2020). https://doi.org/10.1007/s11276-019-02029-z

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