Abstract
Available domain ontologies are increasing over the time. However there is still a huge amount of data stored and managed with RDBMS. This complementarity could be exploited both for discovering knowledge patterns that are not formalized within the ontology but that are learnable from the data, and for enhancing reasoning on ontologies by relying on the combination of formal domain models and the evidence coming from data. We propose a method for learning association rules from both ontologies and RDBMS in an integrated way. The extracted patterns can be used for enriching the available knowledge (in both format) and for refining existing ontologies. We also propose a method for automated reasoning on grounded knowledge bases (i.e. knowledge bases linked to RDBMS data) based on the standard Tableaux algorithm which combines logical reasoning and statistical inference thus making sense of the heterogeneous data sources.
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Notes
- 1.
The concepts “male”, “parent”, “big city”, “medium-sized town”, and the relations “lives_in” are used in the ontology.
- 2.
We call patterns like that in (1) a hybrid pattern since it is composed by elements (that is concept names, role names and attributes) coming from different data sources.
- 3.
An example of random choice within Tableaux algorithm occurs when processing a concepts disjunction, i.e. \(C \sqcup D\), that is when it has to be decided whether an individual \(x\) belongs to concept the \(C\) or \(D\).
- 4.
The restriction of \(g\) to the subsets of \(\mathbf {D}\) and \(\varSigma _I\) can be considered if a mapping for all objects does not exist.
- 5.
The case of a negated concept within the disjunction is treated similarily.
- 6.
If a most conservative behavior of the heuristic has to be considered, only the assertion concerning the disjunct \(C\) (resp. \(D\)) is added to \(\mathcal {I}_r\) while the additional items in the right hand side of the selected rule are not taken into account.
- 7.
We are working for building an heterogeneous dataset for empirically evaluating the proposed approach.
- 8.
Concept names of the considered ontology could be considered as well as new query concepts that are built starting (by using the constructors of the chosen DL language [2]) from the concept names that are formalized in the ontology.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press (1993)
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American, Singapore (2001)
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R., Ruzzi, M., Savo, D.F.: The mastro system for ontology-based data access. Semant. Web J. 2(1), 43–53 (2011)
d’Amato, C.: Similarity-based learning methods for the semantic web. Ph.D. Thesis (2007)
d’Amato, C., Bryl, V., Serafini, L.: Data-driven logical reasoning. In: Bobillo, F., et al. (eds.) Proceedings of International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2012). CEUR-Workshop Proceedings, pp. 51–62. CEUR (2012)
d’Amato, C., Bryl, V., Serafini, L.: Semantic knowledge discovery from heterogeneous data sources. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 26–31. Springer, Heidelberg (2012)
d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: an inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)
Donini, F.M., Lenzerini, M., Nardi, D., Schaerf, A.: \({\cal AL}\)-log: integrating datalog and description logics. J. Intell. Inf. Syst. 10, 227–252 (1998)
Galárraga, L., Teflioudi, C., Suchanek, F., Hose, K.: Amie: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 20th International World Wide Web Conference (WWW 2013). ACM (2013)
Grimm, S., Motik, B., Preist, C.: Variance in e-business service discovery. In: Proceedings of the ISWC Workshop on Semantic Web Services (2004)
Hand, D.J., Smyth, P., Mannila, H.: Principles of Data Mining. MIT Press, Cambridge (2001)
Józefowska, J., Lawrynowicz, A., Lukaszewski, T.: The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. Theory Pract. Logic Program. 10(3), 251–289 (2010)
Lisi, F.A.: Al-quin: an onto-relational learning system for semantic web mining. Int. J. Semant. Web Inf. Syst. 7, 1–22 (2011)
Rettinger, A., Lösch, U., Tresp, V., d’Amato, C., Fanizzi, N.: Mining the semantic web: statistical learning for next generation knowledge bases. Data Min. Knowl. Discov. 24(3), 613–662 (2012)
Spaccapietra, S., Parent, C.: View integration: a step forward in solving structural conflicts. IEEE Trans. Knowl. Data Eng. 6(2), 258–274 (1994)
Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011)
Witten, I., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufman, San Francisco (2011)
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d’Amato, C., Bryl, V., Serafini, L. (2014). Semantic Knowledge Discovery and Data-Driven Logical Reasoning from Heterogeneous Data Sources. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web III. URSW URSW URSW 2012 2011 2013. Lecture Notes in Computer Science(), vol 8816. Springer, Cham. https://doi.org/10.1007/978-3-319-13413-0_9
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