An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.

Testing incremental politics : the case of North Sea oil

In the quest to secure the much vaunted benefits of North Sea oil, highly non-incremental technologies have been adopted. Nowhere is this more the case than with the early fields of the central and northern North Sea. By focusing on the inflexible nature of North Sea hardware, in such fields, this thesis examines the problems that this sort of technology might pose for policy making. More particularly, the following issues are raised. First, the implications of non-incremental technical change for the successful conduct of oil policy is raised. Here, the focus is on the micro-economic performance of the first generation of North Sea oil fields and the manner in which this relates to government policy. Secondly, the question is posed as to whether there were more flexible, perhaps more incremental policy alternatives open to the decision makers. Conclusions drawn relate to the degree to which non-incremental shifts in policy permit decision makers to achieve their objectives at relatively low cost. To discover cases where non-incremental policy making has led to success in this way, would be to falsify the thesis that decision makers are best served by employing incremental politics as an approach to complex problem solving.

Incremental nature of anterior eye grading scales determined by objective image analysis

Aim: To use previously validated image analysis techniques to determine the incremental nature of printed subjective anterior eye grading scales. Methods: A purpose designed computer program was written to detect edges using a 3 × 3 kernal and to extract colour planes in the selected area of an image. Annunziato and Efron pictorial, and CCLRU and Vistakon-Synoptik photographic grades of bulbar hyperaemia, palpebral hyperaemia roughness, and corneal staining were analysed. Results: The increments of the grading scales were best described by a quadratic rather than a linear function. Edge detection and colour extraction image analysis for bulbar hyperaemia (r2 = 0.35-0.99), palpebral hyperaemia (r2 = 0.71-0.99), palpebral roughness (r2 = 0.30-0.94), and corneal staining (r2 = 0.57-0.99) correlated well with scale grades, although the increments varied in magnitude and direction between different scales. Repeated image analysis measures had a 95% confidence interval of between 0.02 (colour extraction) and 0.10 (edge detection) scale units (on a 0-4 scale). Conclusion: The printed grading scales were more sensitive for grading features of low severity, but grades were not comparable between grading scales. Palpebral hyperaemia and staining grading is complicated by the variable presentations possible. Image analysis techniques are 6-35 times more repeatable than subjective grading, with a sensitivity of 1.2-2.8% of the scale.

Incremental tensor biased discriminant analysis:a new color-based visual tracking method

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.