This thesis presents a method for learning a domain theory automatically from a corpus of parsed sentences. What is meant by a ‘domain theory ’ is a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest. As language users we implicitly draw on such theories in various disambiguation tasks, such as anaphora resolution and prepositional phrase attachment, and formal encodings of domain theories can be used for this purpose in natural language processing. Domain theories may also be objects of interest in their own right, that is, as the output of a knowledge discovery process, providing previously unobserved information to aid with the understanding of the domain. The learning paradigm employed is Inductive Logic Programming (ILP), which generalises over examples from the domain to obtain more general patterns covering the majority of the input instances. ILP was preferred over other machine learning techniques due to the expressive power of the language specifications guiding the search for general patterns andthefactthatitallowstheinclusion

Data mining is an instance of the inductive methodology. Many philosophical considerations for induction can also be carried out for data mining. In particular, the justi cation of induction has been a long-standing problem in epistemology. This article is a recast of the problem in the context of data mining. We formulate the problem precisely in the rough set-based decision logic and discuss its implications for the research of data mining.