In contrast to the Relevance Theory of Communication

As the role of ontology in a multilingual setting becomes important to Semantic Web development, it becomes necessary to understand and model how an original conceptual meaning of a Source Language word is conveyed into a Target Language translation. Terminological ontology [1] is a tool used for knowledge sharing and domain-specific translation, and could potentially be suitable for simulating the cognitive models explaining real-world inter-cultural communication scenarios. In this paper, a framework referred to as the Relevance Theory of Communication [2] is contrasted to an empirical study applying Tversky´s contrast model [3] to datasets obtained from the terminological ontology. The results indicate that the alignment of two language-dependent terminological ontologies is a potential method for optimizing the relevance required in inter-cultural communication, in other words, for identifying corresponding concepts existing in two remote cultures

Influence of cultural prior-knowledge in cross-cultural communication

The role of ontology in a multilingual context is one of the emerging challenges in our modern information society. This work first explains different types of ontology applications in a multilingual context based on a number of dimensions defined in [Cimiano 2010]. These dimensions are useful for clarifying the role of ontologies depending on different types of cross-cultural communication scenarios. What is emphasized here is a new dimension in the ontology applications, namely the inherent asymmetric relation of communication between a communicator and an information receiver, which has been inspired by the pragmatic approach of the so-called Relevance Theory of Communication (RTC) [Sperber 1986]. Based on this ground theory, a new framework for simulating the cognitive processes involved in a cross-cultural communication is proposed

Computing Complexity of Cultures

Values are crucial for explaining the motivational basis of human attitudes and behavior, as well as social and personal organization. This project investigates methods to analyze values possessed by diverse individuals residing in several societies based in Japan and other foreign countries. The aim is to identify useful intercultural data analysis methods to examine the heterogeneity of societies within and across countries based on advanced AI technologies such as machine learning and ontology technologies. Our intercultural data analysis project is based on the publicly available data such as World Value Survey and European Social Survey. The project eventually aims at developing an intercultural data analysis tool for public and private service providers to identify potential target consumer segments of services/products and to indicate preferences of the potential customers in a foreign market

Categorization of Destinations and Formation of Mental Destination Representations: A Parallel Biclustering Analysis

Segmentation analysis in tourism research is challenged by an excessive number of variables used. The biclustering approach to segmentation is considered a promising approach to address the problem of an inappropriately large number of variables involved. This paper introduces, to tourism research, a disruptive biclustering approach advanced by recent developments of Bayesian relational modeling. This new approach, for the first time in tourism research, allows to design and conduct a segmentation analysis by simultaneously biclustering multiple datasets consisting of cases and variables in a parallel format. We demonstrate how the new analytical framework can be applied to analyze and compare patterns of associations which individuals have of multiple destinations. Subsequently, this paper elaborates potential contributions the Bayesian relational modeling framework makes to the tourism research discipline by outlining a conceptual idea of the segmentation analysis that enables the simultaneous biclustering of individuals and their associations for multiple destinations in a parallel format

A design for testability study on a high performance automatic gain control circuit.

A comprehensive testability study on a commercial automatic gain control circuit is presented which aims to identify design for testability (DfT) modifications to both reduce production test cost and improve test quality. A fault simulation strategy based on layout extracted faults has been used to support the study. The paper proposes a number of DfT modifications at the layout, schematic and system levels together with testability. Guidelines that may well have generic applicability. Proposals for using the modifications to achieve partial self test are made and estimates of achieved fault coverage and quality levels presente