The expansion of the Web and of our capacity of producing and storing information have had a profound impact on the way we organize, manipulate and share data.We have seen an increased specialization of database back-ends and data models to respond to modern application needs: text indexing engines organize unstructured data, standards and models were created to support the Semantic Web, Big Data requirements stimulated an explosion of data representation and manipulation models. This complex and heterogeneous environment demands unified strategies that enable data integration and, especially, cross-application, expressive querying. Here we present a new approach for the integration of structured and unstructured data within organizations. Our solution is based on the Complex Data Management System (CDMS), a system being developed to handle data typical of complex networks. The CDMS enables a relationship-centric interaction with data that brings many advantages to the institutional data integration scenario, allowing applications to rely on common models for data querying and manipulation. In our framework, diverse data models are integrated in a unifying RDF graph. A novel query model allows the combination of concepts from information retrieval, databases, and complex networks into a declarative query language that extends SPARQL. This query language enables flexible correlation queries over the unified data, enabling support for a wide range of applications such as CMSs, recommendation systems, social networks, etc. We also introduce Mappers, a data management mechanism that simplifies the integration of heterogeneous data and that is integrated in the query language for further flexibility. Experimental results from real data demonstrate the viability of our approach.8990 2654Alves, H., Santanchè, A., Abstract framework for social ontologies and folksonomized ontologies (2012) SWIM, , ACMAmer-Yahia, S., Case, P., Rölleke, T., Shanmugasundaram, J., Weikum, G., Report on the DB/IR panel (2005) SIGMOD Record 34(4), pp. 71-74Auer, S., Dietzold, S., Lehmann, J., Hellmann, S., Aumueller, D., Triplify lightweight linked data publication from relational databases (2009) Proceedings of the 18th International Conference on World Wide Web, , WWW (2009)Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O., Open information extraction from the web (2007) IJCAI, pp. 2670-2676Berners-Lee, T., Giant global graph (2007) Online posting, , http://dig.csail.mit.edu/breadcrumbs/node/215Bizer, C., D2rq-treating non-rdf databases as virtual rdf graphs (2004) Proceedings of the 3rd International Semantic Web Conference (ISWC2004)Blanco, R., Lioma, C., Graph-based term weighting for information retrieval (2012) Inf. Retr, 15 (1), pp. 54-92Blei, D.M., Ng, A.Y., Jordan, M.I., Latent dirichlet allocation (2003) J. Mach. Learn. Res, 3 (4-5), pp. 993-1022Chaudhuri, S., Ramakrishnan, R., Weikum, G., Integrating DB and IR technologies: What is the sound of one hand clapping? (2005) CIDR, pp. 1-12Costa, L., Oliveira, O., Jr., Travieso, G., Rodrigues, F., Boas, P., Antiqueira, L., Viana, M., Rocha, L., Analyzing and modeling real-world phenomena with complex networks: A survey of applications (2011) Adv. Phys, 60, pp. 329-412Costa, L.D.F., Rodrigues, F.A., Travieso, G., Boas, P.R.V., Characterization of complex networks: A survey of measurements (2007) Adv. Phys, 56 (1), pp. 167-242Crestani, F., Application of spreading activation techniques in information retrieval (1997) Artif. Intell. Rev, 11 (6), pp. 453-482Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Yates, A., Web-scale information extraction in Know-It All (2004) WWW, 100p. , 26 MarchGetoor, L., Diehl, C.P., Link mining: A survey (2005) SIGKDD Explor. Newsl, 7 (2), pp. 3-12Gomes, L., Jr., Costa, L., Santanchè, A., Querying complex data (2013) Technical Report IC-13-27, , Institute of Computing, University of Campinas, OctoberGomes, L., Jr., Jensen, R., Santanchè, A., Query-based inferences in the Complex Data Management System (2013) Structured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG-ICML)Gomes, L., Jr., Jensen, R., Santanchè, A., Towards query model integration: Topology-aware, ir-inspired metrics for declarative graph querying (2013) Graph Q-EDBTHan, J., Kamber, M., Data Mining: Concepts and Techniques (2006) Morgan Kaufmann, , San FranciscoHassanzadeh, O., Consens, M., (2009) Linked movie data base. In: Proceedings of the 2nd Workshop on Linked Data on the Web (LDOW2009)Ilyas, I.F., Beskales, G., Soliman, M.A., A survey of top-k query processing techniques in relational database systems. ACM Comput (2008) Surveys 40(4), 11 (1-11), p. 58Imhoff, C., Galemmo, N., Geiger, J.G., Mastering Data Warehouse Design: Relational and Dimensional Techniques (2003) Wiley, , ChichesterJarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P., Fundamentals of Data Warehouses (2003) Springer, , HeidelbergKimelfeld, B., Sagiv, Y., Finding and approximating top-k answers in keyword proximity search (2006) PODSLuo, Y., Wang, W., Lin, X., Zhou, X., Wang, J., Li, K., SPARK2: Top-k keyword query in relational databases (2011) TKDE 23(12), pp. 1763-1780Markovitch, S., Gabrilovich, E., Computing semantic relatedness using wikipediabased explicit semantic analysis (2007) IJCAINgonga Ngomo, A.-C., Heino, N., Lyko, K., Speck, R., Kaltenböck, M., SCMS-Semantifying content management systems (2011) ISWC 2011, Part II. LNCS, 7032, pp. 189-204. , Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) Springer, HeidelbergRodriguez, M.A., Neubauer, P., The graph traversal pattern (2010) Co RR, p. 1001. , abs/1004Rodriguez, M.A., Pepe, A., Shinavier, J., The dilated triple (2010) Emergent Web Intelligence: Advanced Semantic Technologies, pp. 3-16. , Badr, Y., Chbeir, R., Abraham, A., Hassanien, A.-E. (eds.) Springer, LondonSarawagi, S., Information extraction. Found (2008) Trends Databases 1(3), pp. 261-377Schenk, S., Staab, S., newblock Networked graphs: A declarative mechanism for SPARQL rules, SPARQL views and RDF data integration on the web (2008) WWWSchwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M., Fed X A federation layer for distributed query processing on linked open data (2011) ESWC 2011, Part II. LNCS, 6644, pp. 481-486. , Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) Springer, HeidelbergSheth, A., Larson, J., Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Comput (1990) Surveys 22(3), pp. 183-236Weikum, G., Kasneci, G., Ramanath, M., Suchanek, F., Database and informationretrieval methods for knowledge discovery. Commun (2009) ACM 52(4), pp. 56-64White, S., Smyth, P., Algorithms for estimating relative importance in networks (2003) SIGKDD