Accompanying the growth of the internet and the consequent diversification of applications and data processing needs, there has been a rapid proliferation of data and query models. While graph models such as RDF have been successfully used to integrate data from diverse origins, interaction with the integrated data is still limited by inflexible query models that cannot express concepts from multiple paradigms. In this paper we analyze data and query models typical of modern data-driven applications. We then propose an integrated query model aimed at covering a broad range of applications, allowing expressive queries that capture elements from diverse data models and querying paradigms. We employ graphs models to integrate data from structured and unstructured sources. We also reinterpret as graph analysis tasks several ranking metrics typical of information retrieval (IR) systems. The metrics allow flexible correlation of data elements based on topological properties of the underlying graph. The new query model is materialized in a query language named in*(in star). We present experiments with real data that demonstrate the expressiveness and practicability of our approach. © 2013 ACM. 185194Auer, S., Dietzold, S., Lehmann, J., Hellmann, S., Aumueller, D., Triplify: Light-weight linked data publication from relational databases Proceedings of the 18th International Conference on World Wide Web, WWW '09, 2009Bizer, C., D2rq - Treating non-rdf databases as virtual rdf graphs Proceedings of the 3rd International Semantic Web Conference (ISWC2004), 2004Blanco, 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) Journal of Machine Learning Research, 3 (4-5), pp. 993-1022Brin, S., Page, L., The anatomy of a large-scale hypertextual Web search engine (1998) Computer Networks and ISDN Systems, 30 (1-7), pp. 107-117Crestani, F., Application of Spreading Activation Techniques in Information Retrieval (1997) Artificial Intelligence Review, 11 (6), pp. 453-482Gomes Jr., L., Santanchè, A., (2013) The Web Within: Leveraging Web Standards and Graph Analysis to Enable Application-level Integration of Institutional Data, , Technical Report IC-13-01, Institute of Computing, University of Campinas, JanuaryHassanzadeh, O., Consens, M., Linked movie data base Proceedings of the 2nd Workshop on Linked Data on the Web (LDOW2009), 2009Ilyas, I.F., Beskales, G., Soliman, M.A., A survey of top-k query processing techniques in relational database systems (2008) ACM Computing Surveys, 40 (4), pp. 11:1-11:58. , OctJensen, R., Silveira, P.S.P., Ortega, N.R.S., De Moraes Lopes, M.H.B., Software application that evaluates the diagnostic accuracy of nursing students (2012) Intl Journal of Nursing Knowledge, 23, pp. 163-171Kasneci, G., Suchanek, F.M., Ifrim, G., Ramanath, M., Weikum, G., NAGA: Searching and Ranking Knowledge (2008) 2008 IEEE 24th International Conference on Data Engineering, pp. 953-962. , IEEE, AprKimelfeld, 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 wikipedia-based explicit semantic analysis (2007) IJCAIMihalcea, R., Radev, D., (2011) Graph-based Natural Language Processing and Information Retrieval, 26. , Cambridge University PressRodriguez, M.A., Neubauer, P., The graph traversal pattern (2010) CoRR, , abs/1004.1001Rodriguez, M.A., Pepe, A., Shinavier, J., The Dilated Triple (2010) Emergent Web Intelligence: Advanced Semantic Technologies, pp. 3-16. , Springer London, JuneSarawagi, S., Information extraction (2008) Foundations and Trends in Databases, 1 (3), pp. 261-377Varadarajan, R., Hristidis, V., Raschid, L., Vidal, M.-E., Ibáñez, L.D., Rodríguez-Drumond, H., Flexible and efficient querying and ranking on hyperlinked data sources (2009) EDBT, pp. 553-564Weikum, G., Kasneci, G., Ramanath, M., Suchanek, F., Database and information-retrieval methods for knowledge discovery (2009) Communications of the ACM, 52 (4), pp. 56-64. , apr 2009, AprWhite, S., Smyth, P., Algorithms for estimating relative importance in networks Proc of SIGKDD, 2003