Introduction to the special issue on multi-relational data mining and statistical relational learning

Abstract

Within the field of machine learning, it has long been recognized that the difficulty of learning is related, among other things, to the representational complexity of the knowledge that is input to the learner. Many machine learning approaches learn from examples that have a relatively simple, “standard”, representation, and that are drawn independently from an identical distribution (“i.i.d.”). In many application domains for machine learning, however, individuals may have a richer internal structure than the standard representation allows us to represent, and there may be relationships between them that invalidate the i.i.d.-assumption. As a result, standard learning techniques may not work very well in these domains. More specifically, the majority of learning approaches have considered cases where each individual data element is described using a fixed set of attributes, each of which has a simple domain that contains atomic (non-decomposable) values, which may be continuous or discrete. But data, or knowledge in general, is not always available in this restricted format, nor can it always easily be converted into it. Individual data elements might be described in a way that is inherently more complex, for instance, as sets or graphs. Such structures can be summarized by listing a number of properties (the size of a set, number of nodes or edges in a graph, or in general certain distributional characteristic of these sets or graphs), but such a summary is always an abstraction of the actual information and as such gives rise to loss of information. Note that this complex description need not describe the “internal structure” of an example. There are many cases where individuals have a simple internal structure, but are related to each other or to other external objects. If we want to take these relationships into account when learning, the description of such an individual includes its local environment and therefore becomes relational. Ignoring this relational structure not only causes potentially useful information to be ignored; it may actually be harmful to the learner. For instance, it has been shown that the violation of the i.i.d.-assumption causes the standard heuristics used by learners to be misguided.