In general, knowledge-intensive data mining methods exploit background knowledge to improve the quality of their results. Then, in knowledge-rich domains often the interestingness of the mined patterns can be increased significantly. In this paper we categorize several classes of background knowledge for subgroup discovery, and present how the necessary knowledge elements can be modelled. Furth...

This paper describes the three formalism available for specifying background knowledge in the Tertius first order logic discovery tool. It also describes the way background knowledge effects the results of Tertius. The way Tertius handles integrity constraints is compared to an approach suggested in work done on the CLAUDIEN [1] learning system. Finally a number of examples of use of Tertius fo...

Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important direction of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and othe...

In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems’ performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for relation extraction. Our work is motivated by the fact ...

2 Introduction In most applications of formal models of categorization, category learning is portrayed as the building-up of a representation in memory for members of the category that have been observed. This assumption is perhaps the most basic that is made for models of categorization, that the representation of a category describes its observed members. Yet if category representations are t...

With the ever increasing size of scientific literature, finding relevant documents and answering questions has become even more of a challenge. Recently, ontologies — hierarchical, controlled vocabularies — have been introduced to annotate genomic data. They can also improve the question answering and the selection of relevant documents in the literature search. Search engines such as GoPubMed....

Biomedicine is an impressively fast developing, interdisciplinary field of research. To control the growing volumes of biomedical data, ontologies are increasingly used as common organization structures. Biomedical ontologies describe domain knowledge in a formal, computationally accessible way. They serve as controlled vocabularies and background knowledge in applications dealing with the inte...

One of the unresolved problems faced in the construction of intelligent tutoring systems is the acquisition of background knowledge, either for the specification of the teaching strategy, or for the construction of the student model, identifying the deviations of students’ behavior. In this paper, we argue that the use of sequential pattern mining and constraint relaxations can be used to autom...

Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain theories originate. In this paper we address the problem of constructing a domain theory from more general, abstract knowledge which may be available. The basis of our method is to rst assume a structur...