The use of statistical measures to constrain generalisa-tion in learning systems has proved successful in many domains, but can only be applied where large numbers of examples exist. In domains where few training examples are available, other mechanisms for constraining gener-alisation are required. In this paper, we propose a representation of background knowledge based on arguments for and ag...

Knowledge representations using semantic web technologies often provide information which translates to explicit term and predicate taxonomies in relational learning. Here we show how to speed up the process of propositionalization of relational data by orders of magnitude, by exploiting such ontologies through a novel refinement operator used in the construction of conjunctive relational featu...

This paper presents a case study in which an Inductive Logic Programming (ILP) technique is applied to natural language processing. Aleph, an ILP system, is used to induce differences among documents. A Case-Based Reasoning (CBR) system is proposed for the purpose of compiling the background knowledge inputted into Aleph. In the CBR system, lexical and syntactic information concerning words in ...

This paper introduces a new approach to provide users with solutions to explore a domain via an information space. A key point in our approach is that information searching and exploring takes place in a domaindependent semantic context. A given context is described through its vocabulary organised along hierarchies that structure the information space. These hierarchies are simplified views on...

Document enrichment is the task of retrieving additional knowledge from external resource over what is available through source document. This task is essential because of the phenomenon that text is generally replete with gaps and ellipses since authors assume a certain amount of background knowledge. The recovery of these gaps is intuitively useful for better understanding of document. Conven...

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge give...

Teachers establish prerequisites that students must meet before they are permitted to enter their courses. It is expected that having these prerequisites will provide students with the knowledge and skills they will need to successfully learn the course content. Also, the material that the students are expected to have previously learned need not be included in a course. We wanted to determine ...

We present work in progress that uses Latent Semantic Indexing (LSI) in conjunction with background knowledge and unlabeled examples to improve text classification accuracy. The singular value decomposition (SVD) that is performed by LSI is done on an expanded term by document matrix that includes the labeled training examples as well as the unlabeled examples. We report classification accuracy...

Background knowledge has been actively investigated as a possible means to improve performance of machine learning algorithms. Research has shown that background knowledge plays an especially critical role in three atypical text categorization tasks: short-text classification, limited labeled data, and non-topical classification. This chapter explores the use of machine learning for non-hierarc...

Attribute exploration is a formal concept analytical tool for knowledge discovery by interactive determination of the implications holding between a given set of attributes. The corresponding algorithm queries the user in an efficient way about the implications between the attributes. The result of the exploration process is a representative set of examples for the entire theory and a set of im...