A lot of alignment systems providing mappings between the concepts of two ontologies rely on an additional source, called background knowledge, represented most of the time by a th...
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background...
There are several domains, such as health-care, in which the decision process usually has a background knowledge that must be considered. We need to maximize the accuracy of the m...
We present a new class of statistical deanonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. ...
Abstract. Ontology mapping has been recognised as an important approach to identifying similar information in heterogeneous ontologies. The Knowledge Organisation System Implicit M...
Traditional approaches for mining generalized association rules are based only on database contents, and focus on exact matches among items. However, in many applications, the use ...
Rafael Garcia Miani, Cristiane A. Yaguinuma, Maril...
The aim of this short paper is to present a general method of using background knowledge to impose constraints in conceptual clustering of object-attribute relational data. The pr...
Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary informatio...
In this paper we extend the state-of-the-art in utilizing background knowledge for supervised classification by exploiting the semantic relationships between terms explicated in O...
Meenakshi Nagarajan, Amit P. Sheth, Marcos Kawazoe...
Recent work has shown the necessity of considering an attacker's background knowledge when reasoning about privacy in data publishing. However, in practice, the data publishe...
David J. Martin, Daniel Kifer, Ashwin Machanavajjh...