Ontology mapping is the task of finding semantic relationships between entities (i.e. concept, attribute and relation) of two ontologies. In the existing literatures, many (semi-)automatic approaches have found considerable interest by combining several mapping strategies (namely multi-strategy mapping). However, experiments show that multi-strategy based mapping does not always outperform its ...

We establish a generalized Hyers–Ulam–Rassias stability theorem in the fuzzy sense. In particular, we introduce the notion of fuzzy approximate Jensen mapping and prove that if a fuzzy approximate Jensen mapping is continuous at a point, then we can approximate it by an everywhere continuous Jensen mapping. As a fuzzy version of a theorem of Schwaiger, we also show that if every fuzzy approxima...

In many scientific settings data can be naturally partitioned into variable groupings called views. Common examples include environmental (1st view) and genetic information (2nd view) in ecological applications, chemical (1st view) and biological (2nd view) data in drug discovery. Multi-view data also occur in text analysis and proteomics applications where one view consists of a graph with obs...

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping (ICIM) is able to identify epistatic quantitative trait loci (QTLs) no matter whether the two interacting QTLs have any additive effects. In this articl...

Creating coordinated multiagent policies in an environment with uncertainties is a challenging issue in the research of multiagent learning. In this paper, a coordinated learning approach is proposed to enable agents to learn both individual policies and coordinated behaviors by exploiting independent relationships inherent in many multiagent systems. We illustrate how this approach is employed...

Multi-stage additive tests are commonly used in applications. Appropriate definition of its test decisions, however, turns out to be challenging. There are a number of existing methods for this purpose, mainly by combining p-values of individual tests using a conditional error probability function. While these methods are flexible enough to use in most applications, their results depend on the ...