Published Versions 1 Vol 2 (4) : 487–512 2020
Download
KnowID: An Architecture for Efficient Knowledge-DrivenInformation and Data Access
: 2019 - 10 - 08
: 2020 - 04 - 24
: 2020 - 04 - 26
307 7 0
Abstract & Keywords
Abstract: Modern information systems require the orchestration of ontologies, conceptual data modeling techniques, and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs. A major question in delivering such systems, is which components to design and put together to make up the required “knowledge to data” pipeline, as each component and process has trade-offs. In this paper, we introduce a new knowledge-to-data architecture, KnowID. It pulls together both recently proposed components and we add novel transformation rules between EER and the Abstract Relational Model to complete the pipeline. KnowID’s main distinctive architectural features, compared to other ontology-based data access approaches, are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.
Keywords: Extended entity-relationship diagrams; Abstract relational model; Ontology-based data access
Acknowledgements
We thank David Toman for feedback on an earlier draft. We also thank the 20 students of the 7 capstone projects for exploring the solution space in implementing the transformation rules.
[1]
T. Catarci & G. Santucci. Query by diagram: A graphical environment for querying databases. ACMSIGMOD Record 23(2)(1994), 515. doi:10.1145/191843.191976.
[2]
A. C. Bloesch & T.A. Halpin. ConQuer: A conceptual query language. In: Proceedings of ER’96: 15th International Conference on Conceptual Modeling, 1996, pp. 121–133. doi:10.1016/0169-023X(95)00005-D.
[3]
G. Xiao, L. Ding, B. Cogrel & D. Calvanese. Virtual knowledge graphs: An overview of systems and use cases. Data Intelligence 1 (2019), 201–223. doi:10.1162/dint a 00011.
[4]
M. Stonebraker & I.F. Ilyas. Data integration: Tthe current status and the way forward. IEEE Data Engineering 41(2)(2018), 3–9. Available at: http://sites.computer.org/debull/A18june/p3.pdf.
[5]
D. Calvanese, B. Cogrel, S. Komla-Ebri, R. Kontchakov, D. Lanti, M. Rezk, M. Rodriguez-Muro, & G. Xiao. Ontop: Answering SPARQL queries over relational databases. Semantic Web Journal 8(3)(2017), 471–487. doi:10.3233/SW-160217.
[6]
A. Poggi, D. Lembo, D. Calvanese, G. De Giacomo, M. Lenzerini & R. Rosati. Linking data to ontologies. In: Spaccapietra S. (eds) Journal on Data Semantics X, 2008, pp. 133–173. doi:10.1007/978-3-540-77688-8_5.
[7]
L. Al-Jadir, C. Parent & S. Spaccapietra. Reasoning with large ontologies stored in relational databases: The Onto-MinD approach. Data & Knowledge Engineering 69(2010), 1158–1180. doi:10.1016/j.datak.2010.07.006.
[8]
F. Zhang, Z.M. Ma & W. Li. Storing owl ontologies in object-oriented databases. Knowledge-Based Systems 76(2015), 240–255. doi:10.1016/j.knosys.2014.12.020.
[9]
A. Borgida, D. Toman & G.E. Weddell. On referring expressions in information systems derived from conceptual modeling. In: Proceedings of ER’16, 2016, pp. 183–197. doi:10.1007/978-3-319-46397-1_14.
[10]
W. Ma, C.M. Keet, W. Oldford, D. Toman & G. Weddell. The utility of the abstract relational model and attribute paths in SQL. In: C. Faron Zucker, C. Ghidini, A. Napoli & Y. Toussaint (eds.) Proceedings of the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW’18)), 2018, pp. 195–211. doi:10.1007/978-3-030-03667-6_13.
[11]
M. Junkkari, J. Vainio, K. Iltanenan, P. Arvola, H. Kari & J. Kekäläinen. Path expressions in SQL: A user study on query formulation. Journal of Database Management 22(3)(2016), 22. doi:10.4018/JDM.2016070101.
[12]
C M. Keet. An introduction to ontology engineering. Available at: http://open.uct.ac.za/bitstream/handle/11427/28312/OEbook.pdf?sequence=1&isAllowed=y.
[13]
P. R. Fillottrani & C.M. Keet. Dimensions affecting representation styles in ontologies. In: The 1st Iberoamerican Conference on Knowledge Graphs and Semantic Web (KGSWC’19), 2019, pp. 186–200. doi:10.1007/978-3-030-21395-4_14.
[14]
F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi & P.F. Patel-Schneider (eds.). The Description Logics Handbook – Theory and Applications. 2nd Edition. Cambridge: Cambridge University Press, 2010. isbn: 9780521150118.
[15]
B. Motik, P.F. Patel-Schneider & B. Parsia. OWL 2 web ontology language structural specification and functional style syntax, W3c recommendation, W3C. Available at: http://www.w3.org/TR/owl2-syntax/.
[16]
D. Toman & G.E. Weddell. Fundamentals of physical design and query compilation, synthesis lectures on data management. Williston, VT: Morgan & Claypool Publishers, 2011. doi:10.2200/S00363ED1V01Y201105DTM018.
[17]
D. Calvanese, T.E. Kalayci, M. Montali, A. Santoso & W. van der Aalst. Conceptual schema transformation in ontology-based data access. In: C.F. Zucker, C. Ghidini, A. Napoli & Y. Toussaint (eds.) Proceedings of the 21st International Conference on Knowledge Engineering and Knowledge Management, 2018, pp. 50–67. doi:10.1007/978-3-030-03667-6_4.
[18]
E. Botoeva, D. Calvanese, B. Cogrel, J. Corman & G. Xiao. A generalized framework for ontology-based data access. In: C. Ghidini, B. Magnini, A. Passerini & P. Traverso (eds.) Proceedings of AI*IA’18, 2018, pp. 166–180. doi:10.1007/978-3-030-03840-3_13.
[19]
E. Kharlamov, D. Hovland, M.G. Skaeveland, D. Bilidas, E. Jiménez-Ruiz, G. Xiao, A. Soylu, D. Lanti, M. Rezk, D. Zheleznyakov, M. Giese, H. Lie, Y. Ioannidis, Y. Kotidis, M. Koubarakis & A. Waaler. Ontology based data access in statoil. Web Semantics: Science, Services and Agents on the World Wide Web 44 (2017), 3–36. doi:10.1016/j.websem.2017.05.005.
[20]
A. Artale, D. Calvanese, R. Kontchakov & M. Zakharyaschev. DL-Lite without the unique name assumption. In: Proceedings of the 22nd International Workshop on Description Logic (DL 2009), 2009, pp. . Available at: http://ceur-ws.org/Vol-477/paper\_11.pdf.
[21]
N.E. Fuchs, K. Kaljurand & T. Kuhn. Discourse representation structures for ACE 6.6, Technical Report, ifi-2010.0010, Department of Informatics, University of Zurich, Switzerland, 2010. Available at: http://attempto.ifi.uzh.ch/site/pubs/papers/drs_report_66.pdf.
[22]
D. Calvanese, C.M. Keet, W. Nutt, M. Rodríguez-Muro & G. Stefanoni. Web-based graphical querying of databases through an ontology: the WONDER system. In: S.Y. Shin, S. Ossowski, M. Schumacher, M. J. Palakal & C.C. Hung (eds.) Proceedings of ACM Symposium on Applied Computing (ACM SAC’10), 2010, pp. 1389–1396. doi:10.1145/1774088.1774384.
[23]
A. Soylu, E. Kharlamov, D. Zheleznyakov, E.J. Ruiz, M. Giese, M. Skjaeveland, D. Hovland, R. Schlatte, S. Brandt, H. Lie & I. Horrocks. Optiquevqs: A visual query system over ontologies for industry. Semantic Web 9(5)(2018), 627–660. doi:10.3233/SW-180293.
[24]
D. Toman & G.E. Weddell. On adding inverse features to the description logic CFD8nc. In: PRICAI 2014: Trends in Artificial Intelligence - 13th Pacific Rim International Conference on Artificial Intelligence, 2014, pp. 587–599. doi:10.1007/978-3-319-13560-1_47.
[25]
J. S. Jacques, D. Toman & G.E. Weddell. Object-relational queries over CFDInc knowledge bases: OBDA for the SQL-Literate. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2016, pp. 1258–1264. Available at: http://ceur-ws.org/Vol-1577/paper\_10.pdf
[26]
T. Halpin & T. Morgan. Information modeling and relational databases. 2nd Edition. San Francisco, CA: Morgan Kaufmann, 2008. isbn: 9780123735683.
[27]
P.R. Fillottrani, C.M. Keet & D. Toman. Polynomial encoding of ORM conceptual models in . In: D. Calvanese & B. Konev (eds.) Proceedings of the 28th International Workshop on Description Logics (DL’15), 2015, pp. 401–414. Available at: http://ceur-ws.org/Vol-1350/paper-50.pdf
[28]
C.M. Keet & P.R. Fillottrani. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering 98(2015), 30–53. doi: 0.1016/j.datak.2015.07.004.
[29]
P. R. Fillottrani & C.M. Keet. Evidence-based languages for conceptual data modeling profiles. In: T. Morzy et al. (eds.) The 19th Conference on Advances in Databases and Information Systems (ADBIS’15), 2015, pp. 215–229. doi:10.1007/978-3-319-23135-8_15.
[30]
R.H. Chiang, T.M. Barron & V.C. Storey. Reverse engineering of relational databases: Extraction of an EER model from a relational database. Data & Knowledge Engineering 12(2)(1994), 107 – 142. doi:10.1016/0169-023X(94)90011-6.
[31]
P. R. Fillottrani & C.M. Keet. Conceptual model interoperability: A metamodel-driven approach. In: A. Bikakis et al. (eds.) Proceedings of the 8th International Web Rule Symposium (RuleML’14), 2014, pp. 52–66. doi:10.1007/978-3-319-09870-8_4.
[32]
Z. C. Khan, C.M. Keet, P.R. Fillottrani & K. Cenci. Experimentally motivated transformations for intermodal links between conceptual models. In: J. Pokorný et al. (eds.) The 20th Conference on Advances in Databases and Information Systems (ADBIS’16), 2016, pp. 104–118. doi:10.1007/978-3-319-44039-2_8.
[33]
D. Calvanese, G. De Giacomo & M. Lenzerini. Identification constraints and functional dependencies in description logics. In: B. Nebel (ed.) Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2001), 2001, pp. 155–160.
[34]
B. Motik, B. C. Grau, I. Horrocks, Z. Wu, A. Fokoue & C. Lutz. OWL 2 Web Ontology Language Profiles, W3C recommendation, W3C (27 Oct. 2009). Available at:http://www.w3.org/TR/owl2-profiles/.
[35]
A. Artale, D. Calvanese, R. Kontchakov, V. Ryzhikov & M. Zakharyaschev. Reasoning over extended ER models. In: C. Parent, K.D. Schewe, V. C. Storey & B. Thalheim (eds.) Proceedings of the 26th International Conference on Conceptual Modeling (ER’07), 2007, pp. 277–292. doi:10.1007/978-3-540-75563-0_20.
[36]
J.H. Gennari, M.A. Musen, R.W. Fergerson, W.E. Grosso, M. Crubézy, H. Eriksson & N.F. Noy & S.W. Tu. The evolution of Protégé: an environment for knowledge-based systems development. International Journal of Human-Computer Studies 58 (1) (2003), 89–123. doi:10.1016/S1071-5819(02)00127-1.
[37]
Y. Nenov, R. Piro, B. Motik, I. Horrocks, Z. Wu & J. Banerjee. Rdfox: A highlyscalable RDF store. In: M. Arenas (ed.) Proceedings of the International Semantic Web Conference (ISWC’15), 2015, pp. 3–20. doi:10.1007/978-3-319-25010-6_1.
[38]
G. Gottlob, S. Kikot, R. Kontchakov, V. V. Podolskii, T. Schwentick & M. Zakharyaschev. The price of query rewriting in ontology-based data access. Artificial Intelligence 213 (2014) 42–59. doi:10.1016/j.artint.2014.04.004.
[39]
C. Lutz, D. Toman & F. Wolter. Conjunctive query answering in the description logic el using a relational database system. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’09), 2009, pp. 2070–2075. Available at: http://ijcai.org/Proceedings/09/Papers/341.pdf.
[40]
S. Das, S. Sundara & R. Cyganiak. R2RML: RDB to RDF mapping language. Available at: https://www.w3.org/TR/r2rml/.
[41]
Ó. Corcho, F. Priyatna & D. Chaves-Fraga. Towards a new generation of ontology based data access. Semantic Web 11 (1) (2020), 153–160. doi:10.3233/SW-190384.
[42]
P.R. Fillottrani, S. Jamieson & C.M. Keet. Connecting knowledge to data through transformations in KnowID: system description. Submitted to a journal, 2019.
Article and author information
Cite As
P.R. Fillottrani & C.M. Keet. KnowID: An architecture for efficient knowledge-driven information and data access. Data Intelligence 2(2020), 487–512. doi: 10.1162/dint_a_00060
Pablo Rubén Fillottrani
P.R. Fillottrani (prf@cs.uns.edu.ar) and C.M. Keet (mkeet@cs.uct.ac.za) jointly developed the theory. Both authors have mademeaningful and valuable contributions in revising and proofreading the resulting manuscript.
Pablo Rubén Fillottrani is a Professor with the Department of Computer Science and Engineering, Universidad Nacional del Sur, Bahía Blanca, Argentina, and independent researcher at Comisión de Investigaciones Cientficas de la Provincia de Buenos Aires. He is head of LISSI, Software Engineering and Information Systems R&D Lab. His research interests are in ontology engineering, semantic Web, knowledge representation and information integration with applications in digital government and softwareengineering. Pablo obtained his PhD at Universidad Nacional del Sur in 2001.
0000-0003-0906-867X
C. Maria Keet
P.R. Fillottrani (prf@cs.uns.edu.ar) and C.M. Keet (mkeet@cs.uct.ac.za) jointly developed the theory. C.M. Keet coordinated the writing of the manuscript and devised the examples. Both authors have made meaningful and valuable contributions in revising and proofreading the resulting manuscript.
mkeet@cs.uct.ac.za
C. Maria Keet is an Associate Professor with the Department of Computer Science, University of Cape Town, South Africa. Her research interests are in knowledge engineering, including ontology engineering, natural language generation, and ontology-driven conceptual modeling, which have resulted in over 100 publications. She wrote a textbook on ontology engineering.Maria obtained her PhD at the KRDB Research Centre, Free University of Bozen-Bolzano, Italy, in 2008. She also has worked as systems engineer in the IT industry for three and half years.
0000-0002-8281-0853
Publication records
Published: Dec. 17, 2020 (Versions1
References
Data Intelligence