Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T11:49:09.908Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning multiple concepts in description logic through three perspectives

Published online by Cambridge University Press:  19 May 2021

Raphael Melo ,
Kate Revoredo Open the ORCID record for Kate Revoredo [Opens in a new window]  and
Aline Paes
Raphael Melo
Affiliation:
IBM Research, Brazil E-mail: [email protected]
Kate Revoredo
Affiliation:
Institute for Data, Process and Knowledge Management, Vienna University of Economics and Business (WU), Vienna, Austria E-mail: [email protected]
Aline Paes
Affiliation:
Institute of Computing, Universidade Federal Fluminense (UFF), Niteroi, RJ, Brazil E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 36 , 2021 , e8
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Antoniou, G. & Van Harmelen, F. 2004. Web ontology language: Owl. In Handbook on Ontologies, 67–92. Springer.Google Scholar
Baader, F. & Nutt, W. May 2010. Basic description logics. In The Description Logic Handbook, Baader, F., McGuinness, D. L., Nardi, D. & Patel-Schneider, P. F. (eds), 2nd edition. Cambridge University Press, 47–100.Google Scholar
Baader, F., Ganter, B., Sertkaya, B. & Sattler, U. 2007. Completing description logic knowledge bases using formal concept analysis. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2007, 7, 230–235.Google Scholar
Barforush, A. A. & Rahnama, A. 2012. Ontology learning: revisted. Journal of Web Engineering 11(4), 269–289.Google Scholar
Bengio, Y., Louradour, J., Collobert, R. & Weston, J. 2009. Curriculum learning. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 41–48.Google Scholar
Berners-Lee, T., Hendler, J. & Lassila, O. 2001. The semantic web. Scientific American 5(284), 34.CrossRefGoogle Scholar
Brachman, R. J. & Levesque, H. J. 2004. Knowledge Representation and Reasoning, 1st edition. Elsevier.Google Scholar
Bratko, I. 1999. Refining complete hypotheses in ILP. In Inductive Logic Programming, Dzeroski, S. & Flach, P. (eds.) 1634. LNCS. Springer Berlin Heidelberg, 44–55.Google Scholar
Bühmann, L., Lehmann, J. & Westphal, P. 2016. Dl-learner - A framework for inductive learning on the semantic web. Journal of Web Semantics 39, 15–24.CrossRefGoogle Scholar
Caruana, R. 1997. Multitask learning. Machine learning 28(1), 41–75.CrossRefGoogle Scholar
De Raedt, L. 2008. Logical and Relational Learning. Springer.CrossRefGoogle Scholar
De Raedt, L. & Lavrač, N, 1996. Multiple predicate learning in two inductive logic programming settings. Logic Journal of IGPL 4(2), 227–254.CrossRefGoogle Scholar
De Raedt, L., Lavrac, N. & Dzeroski, S. 1993. Multiple predicate learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1037–1042.Google Scholar
Distel, F. 2011. Learning Description Logic Knowledge Bases from Data Using Methods from Formal Concept Analysis. PhD thesis, TU Dresden.CrossRefGoogle Scholar
Domingos, P. 1998. Occam’s two razors: The sharp and the blunt. In KDD, 37–43.Google Scholar
Dong, T., Duc, C. L. & Lamolle, M. 2017. Tableau-based revision for expressive description logics with individuals. Journal of Web Semantics 45, 63–79.CrossRefGoogle Scholar
Fanizzi, N., d’Amato, C. & Esposito, F. 2008. Dl-foil concept learning in description logics. In Inductive Logic Programming, 5194. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 107–121. ISBN 978-3-540-85927-7.Google Scholar
Funk, M., Jung, J. C., Lutz, C., Pulcini, H. & Wolter, F. July 2019. Learning description logic concepts: When can positive and negative examples be separated? In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-2019, 1682–1688. International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2019/233. https://doi.org/10.24963/ijcai.2019/233.CrossRefGoogle Scholar
Gao, W., Guirao, J. L., Basavanagoud, B. & Wu, J. 2018. Partial multi-dividing ontology learning algorithm. Information Sciences 467, 35–58.CrossRefGoogle Scholar
Gutiérrez-Basulto, V., Jung, J. C., Lutz, C. & Schröder, L. 2017. Probabilistic description logics for subjective uncertainty. Journal of Artificial Intelligent Research 58, 1–66.CrossRefGoogle Scholar
Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P. F., Rudolph, S. et al. 2009. Owl 2 web ontology language primer. W3C Recommendation 27(1), 123, 2009.Google Scholar
Iannone, L., Palmisano, I. & Fanizzi, N. 2007. An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159.CrossRefGoogle Scholar
Janowicz, K. & Wilkes, M. 2009. SIM-DLA: A novel semantic similarity measure for description logics reducing inter-concept to inter-instance similarity. In European Semantic Web Conference. Springer, 353–367.Google Scholar
Jiang, L., Meng, D., Zhao, Q., Shan, S. & Hauptmann, A. G. 2015. Self-paced curriculum learning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2694–2700.Google Scholar
Konev, B., Ozaki, A. & Wolter, F. 2016. A model for learning description logic ontologies based on exact learning. In Proceedings of AAAI-16.Google Scholar
Konev, B., Lutz, C., Ozaki, A. & Wolter, F. 2017a. Exact learning of lightweight description logic ontologies. Journal of Machine Learning Research 18, 201:1–201:63.Google Scholar
Konev, B., Lutz, C., Ozaki, A. & Wolter, F. 2017b. Exact learning of lightweight description logic ontologies. The Journal of Machine Learning Research 18(1), 7312–7374.Google Scholar
Konys, A. 2018. Knowledge systematization for ontology learning methods. Procedia Computer Science 126, 2194–2207.CrossRefGoogle Scholar
Krötzsch, M., Simancik, F. & Horrocks, I. 2012. A description logic primer. CoRR, abs/1201.4089. http://arxiv.org/abs/1201.4089.Google Scholar
Lehmann, J. 2009. DL-learner: Learning concepts in description logics. Journal of Machine Learning Research 10, 2639–2642.Google Scholar
Lehmann, J. & Hitzler, P. 2010. Concept learning in description logics using refinement operators. Machine Learning 78(1–2), 203–250. ISSN 0885-6125.CrossRefGoogle Scholar
Lima, R., Espinasse, B. & Freitas, F. 2018. Ontoilper: an ontology-and inductive logic programming-based system to extract entities and relations from text. Knowledge and Information Systems 56(1), 223–255.CrossRefGoogle Scholar
Liu, Y., Nie, L., Han, L., Zhang, L. & Rosenblum, D. S. 2015. Action2activity: Recognizing complex activities from sensor data. In IJCAI, 1617–1623.Google Scholar
Liu, Y., Zheng, Y., Liang, Y., Liu, S. & Rosenblum, D. 2016. Urban water quality prediction based on multi-task multi-view learning. In IJCAI, 2576–2582.Google Scholar
Maedche, A. & Staab, S. 2001. Ontology learning for the semantic web. IEEE Intelligent Systems and Their Applications 16(2), 72–79.CrossRefGoogle Scholar
Maedche, A. & Staab, S. 2002. Measuring similarity between ontologies. In International Conference on Knowledge Engineering and Knowledge Management. Springer, 251–263.Google Scholar
Melo, R., Revoredo, K. & Paes, A. 2013a. Terminology learning through taxonomy discovery. In 2013 Brazilian Conference on Intelligent Systems, 169–174.Google Scholar
Melo, R., Revoredo, K. & Paes, A. 2013b. Learning multiple description logics concepts. In ILP 2013 Late Breaking Papers, 17–22.Google Scholar
Melo, R., Revoredo, K. & Paes, A. 2014. Syntactic compression of description logics terminologies. In Brazilian Conference on Intelligent Systems, 180–185.Google Scholar
Mitchell, T. 1997. Machine Learning. McGraw-Hill, New York.Google Scholar
Muggleton, S. 1991. Inductive logic programming. New Generation Computing 8(4), 295–318.CrossRefGoogle Scholar
Muggleton, S. 1992. Inductive Logic Programming. Morgan Kaufmann, 1992.Google Scholar
Muggleton, S. H. & Bryant, C. H. 2000. Theory completion using inverse entailment. In Inductive Logic Programming. Springer, 130–146.Google Scholar
Nikitina, N., Rudolph, S. & Glimm, B. 2012. Interactive ontology revision. Web Semantics: Science, Services and Agents on the World Wide Web 12, 118–130.CrossRefGoogle Scholar
Ozaki, A. 2020. Learning description logic ontologies: Five approaches. where do they stand? KI - Künstliche Intelligenz 34, 317–-327.Google Scholar
Revoredo, K., Ochoa-Luna, J. & Cozman, F. 2011. Learning probabilistic description logics: A framework and algorithms. In In proceedings of the MICAI, 7094. LNCS. Springer, 28–39.Google Scholar
Sánchez-Ruiz, A. A., Ontanón, S., González-Calero, P. A. & Plaza, E. 2011. Measuring similarity in description logics using refinement operators. In International Conference on Case-Based Reasoning. Springer, 289–303.Google Scholar
Sazonau, V. & Sattler, U. 2017. Mining hypotheses from data in OWL: Advanced evaluation and complete construction. In The Semantic Web - ISWC 2017 – 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I, 10587. LNCS. Springer, 577–593.Google Scholar
Sazonau, V., Sattler, U. & Brown, G. 2015. General terminology induction in OWL. In The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I, 9366. LCNS. Springer, 533–550.Google Scholar
Shamsfard, M. & Barforush, A. A. 2003. The state of the art in ontology learning: A framework for comparison. The Knowledge Engineering Review 18(4), 293–316. doi: 10.1017/S0269888903000687.CrossRefGoogle Scholar
Sommer, E. 1995. An approach to quantifying the quality of induced theories. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) 95-Workshop on Machine Learning and Comprehensibility. Citeseer.Google Scholar
Tran, A. C., Dietrich, J., Guesgen, H. W. & Marsland, S. 2017. Parallel symmetric class expression learning. Journal of Machine Learning Research 18, 64:1–64:34.Google Scholar
Völker, J., Fleischhacker, D. & Stuckenschmidt, H. 2015. Automatic acquisition of class disjointness. Journal of Web Semantics 35, 124–139.CrossRefGoogle Scholar
Wogulis, J. L. 1994. An Approach to Repairing and Evaluating First-Order Theories Containing Multiple Concepts and Negation. PhD thesis, UCI.Google Scholar
Zemlyachenko, V., Korneenko, N. & Tyshkevich, R. 1985. Graph isomorphism problem. Journal of Soviet Mathematics 29(4), 1426–1481. ISSN 0090-4104.CrossRefGoogle Scholar