{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:28:06Z","timestamp":1781101686141,"version":"3.54.1"},"reference-count":48,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,7,1]]},"abstract":"<p>Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.<\/p>","DOI":"10.4018\/jswis.2011070101","type":"journal-article","created":{"date-parts":[[2012,1,13]],"date-time":"2012-01-13T11:23:39Z","timestamp":1326453819000},"page":"1-22","source":"Crossref","is-referenced-by-count":11,"title":["AL-QuIn"],"prefix":"10.4018","volume":"7","author":[{"given":"Francesca A.","family":"Lisi","sequence":"first","affiliation":[{"name":"Universit\u00e0 degli Studi di Bari \u201cAldo Moro\u201d, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jswis.2011070101-0","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 207-216). New York, NY: ACM Press.","DOI":"10.1145\/170036.170072"},{"key":"jswis.2011070101-1","unstructured":"Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). San Francisco, CA: Morgan Kaufmann."},{"key":"jswis.2011070101-2","doi-asserted-by":"crossref","unstructured":"Assmann, U., Henriksson, J., & Maluszynski, J. (2006). Combining safe rules and ontologies by interfacing of reasoners. In J. Alferes, J. Bailey, W. May, & U. Schwertel (Eds.), Proceedings of the 4th International Conference on Principles and Practice of Semantic Web Reasoning (LNCS 4187, p. 33-47).","DOI":"10.1007\/11853107_3"},{"key":"jswis.2011070101-3","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511711787"},{"key":"jswis.2011070101-4","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(96)00004-5"},{"key":"jswis.2011070101-5","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(88)90001-X"},{"key":"jswis.2011070101-6","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-83952-8","author":"S.Ceri","year":"1990","journal-title":"Logic programming and databases"},{"key":"jswis.2011070101-7","unstructured":"Clark & Parsia. (2011). Pellet: OWL 2 reasoner for Java. Retrieved from http:\/\/clarkparsia.com\/pellet\/"},{"key":"jswis.2011070101-8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68856-3"},{"key":"jswis.2011070101-9","first-page":"438","article-title":"Condensed representations for inductive logic programming","author":"L.De Raedt","year":"2004","journal-title":"Principles of knowledge representation and reasoning"},{"key":"jswis.2011070101-10","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009863704807"},{"key":"jswis.2011070101-11","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008687430626"},{"key":"jswis.2011070101-12","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-04599-2","author":"S.D\u017eeroski","year":"2001","journal-title":"Relational data mining"},{"key":"jswis.2011070101-13","author":"A.G\u00f3mez-P\u00e9rez","year":"2004","journal-title":"Ontological engineering"},{"key":"jswis.2011070101-14","unstructured":"Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large databases. In Proceedings of the 21st International Conference on Very Large Data Bases (pp. 420-431). San Francisco, CA: Morgan Kaufmann."},{"issue":"5","key":"jswis.2011070101-15","article-title":"Mining multiple-level association rules in large databases.","volume":"11","author":"J.Han","year":"1999","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"jswis.2011070101-16","unstructured":"Horrocks, I., Patel-Schneider, P., Boley, H., Tabet, S., Grosof, B., & Dean, M. (2004). SWRL: A semantic web rule language combining OWL and RuleML. Retrieved from http:\/\/www.w3.org\/Submission\/SWRL\/"},{"key":"jswis.2011070101-17","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2003.07.001"},{"key":"jswis.2011070101-18","unstructured":"Hustadt, U., Motik, B., & Sattler, U. (2004). Reducing -Description Logic to Disjunctive Datalog Programs. In D. Dubois, C. Welty, & M. A. Williams (Eds.), Principles of knowledge representation and reasoning (pp. 152\u2013162). Menlo Park, CA: AAAI Press."},{"key":"jswis.2011070101-19","doi-asserted-by":"publisher","DOI":"10.1017\/S1471068410000098"},{"key":"jswis.2011070101-20","doi-asserted-by":"crossref","unstructured":"Kietz, J. (2003). Learnability of description logic programs. In S. Matwin & C. Sammut (Eds.), Proceedings of the International Conference on Inductive Logic Programming (LNCS 2583, pp. 117-132).","DOI":"10.1007\/3-540-36468-4_8"},{"key":"jswis.2011070101-21","doi-asserted-by":"crossref","unstructured":"Knublauch, H., Fergerson, R. W., Noy, N. F., & Musen, M. A. (2004). The Prot\u00e9g\u00e9 OWL Plugin: An open development environment for semantic web applications. In S. A. McIlraith, D. Plexousakis, & F. van Harmelen (Eds.), In Proceedings of the Semantic Web Conference (LNCS 3298, p. 229-243).","DOI":"10.1007\/978-3-540-30475-3_17"},{"key":"jswis.2011070101-22","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(98)00048-4"},{"key":"jswis.2011070101-23","doi-asserted-by":"publisher","DOI":"10.1017\/S1471068407003195"},{"key":"jswis.2011070101-24","doi-asserted-by":"publisher","DOI":"10.1017\/S1471068410000116"},{"key":"jswis.2011070101-25","doi-asserted-by":"crossref","unstructured":"Lisi, F., & Esposito, F. (2004). Efficient evaluation of candidate hypotheses in -log. In R. Camacho, R. King, & A. Srinivasan (Eds.), Proceedings of the 14th International Conference on Inductive Logic Programming (LNCS 3194, pp. 216-233).","DOI":"10.1007\/978-3-540-30109-7_18"},{"key":"jswis.2011070101-26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01891-6_1"},{"key":"jswis.2011070101-27","doi-asserted-by":"crossref","unstructured":"Lisi, F., & Malerba, D. (2003a). Bridging the gap between horn clausal logic and description logics in inductive learning. In A. Cappelli & F. Turini (Eds.), Proceedings of the 8th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence (LNCS 2829, pp. 49-60).","DOI":"10.1007\/978-3-540-39853-0_5"},{"key":"jswis.2011070101-28","doi-asserted-by":"crossref","unstructured":"Lisi, F., & Malerba, D. (2003b). Ideal refinement of descriptions in -log. In T. Horvath & A. Yamamoto (Eds.), Proceedings of the 13th International Conference on Inductive Logic Programming (LNCS 2835, pp. 215-232).","DOI":"10.1007\/978-3-540-39917-9_15"},{"key":"jswis.2011070101-29","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000023151.65011.a3"},{"key":"jswis.2011070101-30","unstructured":"Maedche, A., & Staab, S. (2000). Discovering conceptual relations from text. In Proceedings of the 14th European Conference on Artificial Intelligence (pp. 321-325). Amsterdam, The Netherlands: IOS Press."},{"key":"jswis.2011070101-31","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-24750-0_9","article-title":"Ontology learning","author":"A.Maedche","year":"2004","journal-title":"Handbook on ontologies"},{"key":"jswis.2011070101-32","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009796218281"},{"key":"jswis.2011070101-33","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(82)90040-6"},{"key":"jswis.2011070101-34","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2005.05.001"},{"key":"jswis.2011070101-35","doi-asserted-by":"crossref","DOI":"10.1007\/3-540-62927-0","volume":"Vol. 1228","author":"S.Nienhuys-Cheng","year":"1997","journal-title":"Foundations of inductive logic programming"},{"key":"jswis.2011070101-36","unstructured":"Nijssen, S., & Kok, J. (2001). Faster association rules for multiple relations. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (pp. 891-896). San Francisco, CA: Morgan Kaufmann."},{"key":"jswis.2011070101-37","doi-asserted-by":"crossref","unstructured":"Nijssen, S., & Kok, J. N. (2003). Efficient frequent query discovery in FARMER. In N. Lavrac, D. Gamberger, H. Blockeel, & L. Todorovski (Eds.), Proceedings of the 7th European Conference on Knowledge Discovery in Databases (LNCS 2838, pp. 350-362).","DOI":"10.1007\/978-3-540-39804-2_32"},{"key":"jswis.2011070101-38","doi-asserted-by":"publisher","DOI":"10.1007\/BF00117105"},{"key":"jswis.2011070101-39","doi-asserted-by":"crossref","unstructured":"Rettinger, A., Nickles, M., & Tresp, V. (2009). Statistical relational learning with formal ontologies. In W. L. Buntine, M. Grobelnik, D. Mladenic, & J. Shawe-Taylor (Eds.), Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Part II (LNCS 5782, pp. 286-301).","DOI":"10.1007\/978-3-642-04174-7_19"},{"key":"jswis.2011070101-40","doi-asserted-by":"crossref","unstructured":"Rosati, R. (2005). Semantic and computational advantages of the safe integration of ontologies and rules. In F. Fages & S. Soliman (Eds.), Proceedings of the Third International Workshop on Principles and Practice of Semantic Web Reasoning (LNCS 3703, pp. 50-64).","DOI":"10.1007\/11552222_6"},{"key":"jswis.2011070101-41","unstructured":"Rosati, R. (2006). +log: Tight integration of description logics and disjunctive datalog. In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (pp. 68-78). Menlo Park, CA: AAAI Press."},{"key":"jswis.2011070101-42","doi-asserted-by":"crossref","unstructured":"Rouveirol, C., & Ventos, V. (2000). Towards Learning in CARIN-. In J. Cussens & A. Frisch (Eds.), Proceedings of the 10th International Conference on Inductive Logic Programming (LNCS 1866, pp. 191-208).","DOI":"10.1007\/3-540-44960-4_12"},{"key":"jswis.2011070101-43","doi-asserted-by":"crossref","unstructured":"Ruckhaus, E., Kolovski, V., Parsia, B., & Cuenca Grau, B. (2006). Integrating Datalog with OWL: Exploring the -log Approach. In S. Etalle & M. Truszczynski (Eds.), Proceedings of the 22nd International Conference on Logic programming (LNCS 4079, p. 455-456).","DOI":"10.1007\/11799573_46"},{"key":"jswis.2011070101-44","doi-asserted-by":"crossref","unstructured":"Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., & Ferilli, S. (1998). A logic framework for the incremental inductive synthesis of Datalog theories. In N. Fuchs (Ed.), Proceedings of 7th International Workshop on Logic Program Synthesis and Transformation (LNCS 1463, pp. 300-321).","DOI":"10.1007\/3-540-49674-2_16"},{"key":"jswis.2011070101-45","unstructured":"Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In Proceedings of the 21st International Conference on Very Large Data Bases (pp. 407-419). San Francisco, CA: Morgan Kaufmann."},{"key":"jswis.2011070101-46","unstructured":"Stanford University. (2011). What is prot\u00e9g\u00e9-OWL? Retrieved from http:\/\/protege.stanford.edu\/overview\/protege-owl.html"},{"key":"jswis.2011070101-47","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2006.02.001"}],"container-title":["International Journal on Semantic Web and Information Systems"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=62560","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T15:34:36Z","timestamp":1742312076000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jswis.2011070101"}},"subtitle":["An Onto-Relational Learning System for Semantic Web Mining"],"short-title":[],"issued":{"date-parts":[[2011,7,1]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2011,7]]}},"URL":"https:\/\/doi.org\/10.4018\/jswis.2011070101","relation":{},"ISSN":["1552-6283","1552-6291"],"issn-type":[{"value":"1552-6283","type":"print"},{"value":"1552-6291","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,7,1]]}}}