Recommended Reading
Cook, D., & Holder, L. (Eds.). (2007). Mining graph data. New York: Wiley.
De Raedt, L. (2008). From inductive logic programming to multi-relational data mining. Heidelberg: Springer.
Domingos, P., & Richardson, M. (2007). Markov logic: A unifying framework for statistical relational learning. In L. Getoor & B. Taskar (Eds.), Introduction to statistical relational learning (pp. 339–371). Cambridge, MA: MIT Press.
Emde, W., & Wettschereck, D. (1996). Relational instance based learning. In L. Saitta (Ed.), Proceedings of the 13th international conference on machine learning (pp. 122–130). San Francisco: Morgan Kaufmann.
Gärtner, T. (2003). A survey of kernels for structured data. SIGKDD Explorations, 5(1), 49–58.
Getoor, L., & Taskar, B. (Eds.). (2007). Introduction to relational statistical learning. Cambridge, MA: MIT Press.
Lavrac, N., Dzeroski, S., & Grobelnik, M. (1991). Learning nonrecursive definitions of relations with LINUS. In Y. Kodratoff (Ed.), Proceedings of the 5th European working session on learning. Lecture notes in computer science (Vol. 482, pp. 265–281). Berlin: Springer.
Michalski, R. S. (1983). A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 83–134). San Francisco: Morgan Kaufmann.
Muggleton, S. H., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20, 629–679.
Muggleton, S. H., & Feng, C. (1992). Efficient induction of logic programs. In S. Muggleton (Ed.), Inductive logic programming (pp. 291–298). London: Academic Press.
Poole, D. (2008). The independent choice logic and beyond. In L. De Raedt, P. Frasconi, K. Kersting, & S. Muggleton (Eds.), Probabilistic inductive logic programming: Theory and application. Lecture notes in artificial intelligence (Vol. 4911). Berlin: Springer.
Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5(3), 239–266.
Sato, T., & Kameya, Y. (2008). New advances in logic-based probabilistic modeling by PRISM. In L. De Raedt, P. Frasconi, K. Kersting, & S. Muggleton (Eds.), Probabilistic inductive logic programming: Theory and application. Lecture notes in artificial intelligence (Vol. 4911, pp. 118–155). Berlin: Springer.
Winston, P. H. (1975). Learning structural descriptions from examples. In P. H. Winston (Ed.), The psychology of computer vision (pp. 157–209). New York: McGraw-Hill.
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Horváth, T., Wrobel, S. (2011). Learning from Structured Data. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_458
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