Crowd Sourced Semantic Enrichment (CroSSE) for knowledge driven querying of digital resources

  • Giacomo Cavallo
  • Francesco Di Mauro
  • Paolo Pasteris
  • Maria Luisa SapinoEmail author
  • K. Selcuk Candan


Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and semantic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which allows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations. We describe CroSSE and SESQL with examples taken from our SmartGround EU project.


Semantic enrichment Crowd-sourcing Data integration framework 



The research is partially supported by EU grants #641988 and #690817 and NSF grant #1633381. We thank project partners, especially our colleagues from the Earth Sciences Department at the University of Torino, P. Rossetti, G. Dino, and G. Biglia.


  1. Minc. (2017). A social platform for fostering educational interactions.Google Scholar
  2. Adali, S., Candan, K., Papakonstantinou, Y., Subrahmanian, V. (1996). Query processing in the sims information mediator. In SIGMOD (pp. 137–148).Google Scholar
  3. Arens, Y., Knoblock, C., Hsu, C. (1996). Query processing in the sims information mediator. In AAAI.Google Scholar
  4. Beckett, D. (ed.) (2004). RDF/XML Syntax Specification (Revised),. W3C Recommendation.Google Scholar
  5. Caldarola, E.G., Picariello, A., Rinaldi, A.M. (2015). An approach to ontology integration for ontology reuse in knowledge based digital ecosystems. In MEDES.Google Scholar
  6. Calì, A., Gottlob, G., Pieris, A. (2010). Advanced processing for ontological queries. Proc. VLDB Endow., 3(1-2), 554–565.CrossRefGoogle Scholar
  7. Candan, K.S., Cao, H., Qi, Y., Sapino, M.L. (2008). System support for exploration and expert feedback in resolving conflicts during integration of metadata. VLDB Journal, 17(6), 22–119.CrossRefGoogle Scholar
  8. Cavallo, G., Di Mauro, F., Pasteris, P., Sapino, M.L., Candan, K.S. (2018). Contextually-enriched querying of integrated data sources. In ICDE18 Workshops.Google Scholar
  9. Davulcu, H., Freire, J., Kifer, M., Ramakrishnan, I. (1999). A layered architecture for querying dynamic web content. In SIGMOD.Google Scholar
  10. Davulcu, H., Kifer, M., Yang, G., Ramakrishnan, I. (2000). Design and implementation of the physical layer in webbases: the xrover experience. In DOOD.Google Scholar
  11. Di Mauro, F., Pasteris, P., Sapino, M.L., Candan, K.S., Dino, G.A., Rossetti, P. (2016). Crowdsourced semantic enrichment for participatory e-government. In Proceedings of the 8th International Conference on Management of Digital EcoSystems, MEDES.Google Scholar
  12. Di Pinto, F., Lembo, D., Lenzerini, M., Mancin, R., Poggi, A., Rosati, R., Ruzzi, M., Savo, D.F. (2013). Optimizing query rewriting in ontology-based data access. In Proceedings of the 16th international conference on extending database technology, EDBT (pp. 561–572).Google Scholar
  13. Garcia-Molina, H., Papakonstantinou, Y., Quass, D., Rajararnan, A., Sagiv, Y., Ullman, J., Vassalos, V., Widom, J. (1997). The tsimmis approach to mediation: Data models and languages. JIIS, pp. 2.Google Scholar
  14. Gottlob, G., Orsi, G., Pieris, A. (2011). Ontological queries: rewriting and optimization. In 2011 IEEE 27th International Conference on Data Engineering (pp. 2–13).Google Scholar
  15. Kambhampati, S., Lambrecht, E., Nambiar, U., Nie, Z., Senthil, G. (2004). Optimizing recursive information gathering plans in emerac. JIIS, 2(2), 119–153.zbMATHGoogle Scholar
  16. Kandogan, E., Roth, M., Schwarz, P.M., Hui, J., Terrizzano, I.G., Christodoulakis, C., Miller, R.J. (2015). Labbook: Metadata-driven social collaborative data analysis. In International Conference on Big Data.Google Scholar
  17. Levy, A. (1998). The information manifold approach to data integration. IEEE Intelligent Systems, 13, 12–16.Google Scholar
  18. Lim, L., Wang, H., Wang, M. (2013). Semantic queries by example. Proceedings of the 16th International Conference on Extending Database Technology (EDBT 2013).Google Scholar
  19. Maedche, A., Staab, S., Studer, R., Sure, Y., Volz, R. (2002). Seal – tying up information integration and web site management by ontologies. IEEE Data Engin. Bulletin, 25(1), 10–17.Google Scholar
  20. Mortensen, J.M., Musen, M. A., Noy, N. F. (2013a). Developing crowdsourced ontology engineering tasks: an iterative process. In Proceedings of the 1st international workshop on crowdsourcing the semantic web, CrowdSem (pp. 79–88).Google Scholar
  21. Mortensen, J.M., Alexander, P.R., Musen, M.A., Noy, N.F. (2013b). Crowdsourcing ontology verification. In Proceedings of the 4th International Conference on Biomedical Ontology, ICBO (pp. 40–45).Google Scholar
  22. Munir, K., & Anjum, M.S. (2017). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116–126.CrossRefGoogle Scholar
  23. Munir, K., Odeh, M., Mcclatchey, R. (2012). Ontology-driven relational query formulation using the semantic and assertional capabilities of OWL-DL. Knowledge-Based Systems, 35, 144–159.CrossRefGoogle Scholar
  24. Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A. (2012). Ontology engineering in a networked world, chapter 2. Berlin: Springer.CrossRefGoogle Scholar
  25. Taylor, N.E., & Ives, Z.G. (2006). Reconciling while tolerating disagreement in collaborative data sharing. In Proceedings SIGMOD06.Google Scholar
  26. Tekli, J., Chbeir, R., Traina, A.J., Traina, C., Yetongnon, K., Ibanez, C.R., Assad, M.A., Kallas, C. (2018). Full-fledged semantic indexing and querying model designed for seamless integration in legacy rdbms. Data and Knowledge Engineering, 117, 133–173.CrossRefGoogle Scholar
  27. Xiao, G., Calvanese, D., Kontchakov, R., Lembo, D., Poggi, A., Rosati, R., Zakharyaschev, M. (2018). Ontology-based data access: a survey. In Proceedings IJCAI-18.Google Scholar
  28. Xu, L., & Embley, D.W. (2002). Combining the best of global-as-view and local-as-view for data integration. In ISTA (pp. 123–136).Google Scholar
  29. Ziegler, P., & Dittrich, K.R. (2007). Data integration - problems, approaches, and perspectives. In J. Krogstie, A.L. Opdahl, S. Brinkkemper (Eds.), Conceptual modelling in information systems engineering, chapter 3 (pp. 39–58). Berlin: Springer.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of TorinoTorinoItaly
  2. 2.CIDSEArizona State UniversityTempeUSA

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