Contributions in Merlin

null

null

null

The casual user is typically overwhelmed by the formal logic of the Semantic Web. The gap between the end user and the logic-based scaffolding has to be bridged if the Semantic Web's capabilities are to be utilized by the general public. This paper proposes that controlled natural languages offer one way to bridge the gap. We introduce GINO, a guided input natural language ontology editor that allows users to edit and query ontologies in a language akin to English. It uses a small static grammar, which it dynamically extends with elements from the loaded ontologies. The usability evaluation shows that GINO is well-suited for novice users when editing ontologies. We believe that the use of guided entry overcomes the habitability problem, which adversely affects most natural language systems. Additionally, the approach's dynamic grammar generation allows for easy adaptation to new ontologies.

For a growing number of applications good similarity measures are crucial to ensure that the applications works as desired. Similarity measures can be used to find the most similar object to another one, or can be used to perform a categorisation task, whereby the calculated similarity value will be used to determine the category. But manually defining a good similarity measure, especially if complex and domain specific objects have to be compared, can be a difficult task. A lot of domain knowledge combined with knowledge in computer science (namely how these similarity measures work internally) is needed, and there exists no approved methodology to do this. Therefore the global goal in this diploma thesis is, instead of manually defining similarity measures, to learn them and to evaluate the achieved results.To be able to learn similarity measures, an universal framework is used, the Local/Global Framework. The idea is to use the Local/Global principle to compare complex objects, whereby the local similarity measures and the amalgamation function can be learned. Another precondition for this is to have an evaluation method to estimate a particular similarity measure's soundness. Typically this is done by comparing the similarity measure's results with a so-called gold standard.To learn, the evolutionary principles observed in nature will be exploited in an artificial evolution. This artificial evolution can be implemented as a genetic algorithm or a genetic programming approach can be used. In the first case parameters of similarity measures will be learned, in the second case, using the genetic programming approach, the algorithms themselves are learned. In both cases the goal is to find similarity measures, which will show only a small deviation to the gold standard. In the case of using a similarity measure to do a categorisation, the goal will be to properly identify the category an object or a pair of objects (the two compared ones) belongs to.

null

We discuss regression models for ordered responses, such as ratings of bonds, schooling attainment, or measures of subjective well-being. Commonly used models in this context are the ordered logit and ordered probit regression models. They are based on an underlying latent model with single index function and constant thresholds. We argue that these approaches are overly restrictive and preclude a flexible estimation of the effect of regressors on the discrete outcome probabilities. For example, the signs of the marginal probability effects can only change once when moving from the smallest category to the largest one. We then discuss several alternative models that overcome these limitations. An application illustrates the benefit of these alternatives.

The adoption of the Internet Protocol (IP) by a number of non-IP network operators, such as telecom or cable TV operators, opens the path toward new business models. IP will allow operators to provide a unified wired as well as wireless access to a wide range of services to their users. Additionally, using the same communication protocols and standard interfaces, enables different providers to coordinate any type of resources in virtual organizations (VO) and supports the composition of services aggregated across multiple domains. On one hand, such an open environment requires new business models to be adopted by the involved parties. On the other hand, grid middleware infrastructure supporting integrated accounting, charging, pricing, and billing across multiple domains has to be in place to facilitate service provisioning in multiple VOs. Based on the relevant set of requirements derived, a new and extended A4C Architecture (Authentication, Authorization, Accounting, Auditing, Charging) has been developed, implemented, and evaluated for mobile grids providing pervasive access to knowledge.

We introduce non-homothetic preferences into an innovation-based growth model and study how income and wealth inequality affect economic growth. We identify a (positive) price effect -- where increasing inequality allows innovators to charge higher prices and (negative) market-size effects -- with higher inequality implying smaller markets for new goods and/or a slower transition of new goods into mass markets. It turns out that price effects dominate market-size effects. We also show that a redistribution from the poor to the rich may be Pareto improving for low levels of inequality.