Problem Definition
Relational learning refers to learning in a context where there may be relationships between learning examples, or where these examples may have a complex internal structure (i.e., consist of multiple components and there may be relationships between these components). In other words, the “relational” may refer to both an internal or external relational structure describing the examples. In fact, there is no essential difference between these two cases, as it depends on the definition of an example whether relations are internal or external to it. Most methods, however, are clearly set in one of these two contexts.
Learning from Examples with External Relationships
This setting considers learning from a set of examples where each example itself has a relatively simple description, for instance in the attribute-value format, and relationships may be present among these examples.
Example 1.Consider the task of web-page classification. Each web-page is described by a...
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Recommended Reading
Most of the topics covered in this entry have more detailed entries in this encyclopedia, namely “Inductive Logic Programming,” “Graph Mining,” “Relational Data Mining,” and “Relational Reinforcement Learning.” These entries provide a brief introduction to these more specific topics and appropriate references for further reading. Direct relevant references to the literature include the following. A comprehensive introduction to ILP can be found in De Raedt’s book (De Raedt, 2008) on logical and relational learning, or in the collection edited by Džeroski & Lavrač (2001) on relational data mining. Learning from graphs is covered by Cook & Holder (2007). Džeroski & Lavrač (2001) is also a good starting point for reading about multi-relational data mining, together with research papers on multi-relational data mining systems, for instance, Yin et al. (2006), who present a detailed description of the CrossMine system. Statistical relational learning in general is covered in the collection edited by Getoor & Taskar (2007), while De Raedt & Kersting (2003) and De Raedt et al. (2008) present overviews of approaches originating in logic-based learning. An overview of relational reinforcement learning can be found in Tadepalli et al. (2004).
Bratko, I. (2000). Prolog programming for artificial intelligence. Reading, MA: Addison-Wesley (3rd edition).
Cook, D. J., & Holder, L. B. (2007). Mining graph data. Hoboken, NJ: Wiley.
De Raedt, L. (2008). Logical and relational learning. Berlin: Springer.
De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. (2008). Probabilistic inductive logic programming. Berlin: Springer.
De Raedt, L., & Kersting, K. (2003). Probabilistic logic learning. SIGKDD Explorations, 5(1), 31–48.
Džeroski, S., De Raedt, L., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7–52.
Džeroski, S., & Lavrač, N., (Eds.). (2001). Relational data mining. Berlin: Springer.
Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998). Pharmacophore discovery using the inductive logic programming system PROGOL. Machine Learning, 30, 241–270.
Getoor, L., & Taskar, B. (2007). Introduction to statistical relational learning. Cambridge: MIT Press.
Horváth, T., Ramon, J., & Wrobel, S. (2006). Frequent subgraph mining in outerplanar graphs. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 197–206). New York: ACM.
Jensen, D., & Neville, J. (2002). Linkage and autocorrelation cause feature selection bias in relational learning. In Proceeding of the 19th International Conference on Machine Learning, University of New South Wales, Sydney (pp. 259–266). San Francisco, CA: Morgan Kaufmann.
Jensen, D., Neville, J., & Gallagher, B. (2004). Why collective inference improves relational classification. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia, PA (pp. 593–598). New York: ACM.
Kersting, K. (2006). An inductive logic programming approach to statistical relational learning. Amsterdam: IOS Press.
Krogel, M.-A., Rawles, S., Železný, F., Flach, P., Lavrač, N., & Wrobel, S. (2003). Comparative evaluation of approaches to propositionalization. In Proceedings of the 13th international conference on inductive logic programming, Szeged, Hungary (pp. 194–217). Berlin: Springer-Verlag.
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Muggleton, S. (1996). Stochastic logic programs. In L. De Raedt (Ed.), Advances in inductive logic programming (pp. 254–264). Amsterdam: IOS Press.
Richardson, M., & Domingos, P. (2006). Markov logic networks. Machine Learning, 62(1–2), 107–136.
Sato, T., & Kameya, Y. (1997). PRISM: A symbolic-statistical modeling language. In Proceedings of the 15th International joint conference on artificial intelligence (IJCAI 97), Nagoya, Japan (pp. 1330–1335). San Francisco, CA: Morgan Kaufmann.
Tadepalli, P., Givan, R., & Driessens, K. (2004). Relational reinforcement learning: An overview. In Proceeding of the ICML’04 Workshop on relational reinforcement learning, Banff, Canada (pp. 1–9).
Washio, T., & Motoda, H. (2003). State of the art of graph-based data mining. SIGKDD Explorations, 5(1), 59–68.
Yin, X., Han, J., Yang, J., & Yu, P. S. (2006). Efficient classification across multiple database relations: A CrossMine approach. IEEE Transactions on Knowledge and Data Engineering, 18(6), 770–783.
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Struyf, J., Blockeel, H. (2011). Relational Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_719
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