Information Management

The publications of the Information Management group can be accessed via the buttons on the left. Examples of our publications are:

Koenderink, N.J.J.P. Broekstra, J. Top, J.L., Computers in Agriculture 2010

Abstract

In agriculture, a major challenge is to automate knowledge-intensive tasks. Task-performing software is often opaque, which has a negative impact on a system's adaptability and on the end user's understanding and trust of the system's operation. A more transparent, declarative way of specifying the expert knowledge required in such software is needed. We argue that a white-box approach is in principle preferred over systems in which the applied expertise is hidden in the system code.  Internal transparency makes it easier to adapt the system to new conditions and to diagnose faulty behaviour. At the same time, explicitness comes at a price and is always bounded by practical considerations. Therefore we introduce the notion of bounded transparency, implying a balanced decision between transparency and opaqueness. The method proposed in this paper provides a set of pragmatic objectives and decision criteria to decide on each level of a task's decomposition whether more transparency is sensible or whether delegation to a black-box component is acceptable. We apply the proposed method in a real-world case study involving a computer vision application for seedling inspection in horticulture and show how bounded transparency is obtained. We conclude that the proposed method offers structure to the application designer in making substantiated implementation decisions.

Semantic support for quantitative research processes

Rijgersberg, H., Top, J. L., Meinders, M.,  IEEE Intelligent Systems 2009
 

Abstract
Scientific research aims to describe and understand real-world phenomena and their underlying mechanisms in a transparent, reproducible way. Quantitative research expresses scientific knowledge in quantities, units of measurement, measurement scales, mathematical relations and operations, tables, graphs, and so on. Generally speaking, quantitative information (such as in experimental data, mathematical equations, programming code, data files, and graphs) is difficult to find and interpret. Scientists often can't execute a model because it isn’t in a suitable input format for the preferred mathematical software and so requires manual adaptation. Although neither completeness in all contextual details nor full automation is feasible, opportunities for improvement in this situation are abundant. To take a step in this direction,we discuss some key elements of the quantitative research process and build a model of quantitative research according to established tenets of the philosophy of science. On the basis of this model, we design an ontology for quantitative research and demonstrate its adequacy for expressing scientific research in food science. The proposed Ontology of Quantitative Research lets scientists express the meaning of data and models and supports automated invocation of computational methods from a conceptual level. Finally, we report on the ontology’s application in Quest, a prototype quantitative e-Science tool.

N. Koenderink, J. Top, L. van Vliet, International Journal of Human-Computer Studies 2006

Abstract

One of the major challenges in computer vision is to create automated systems that perform tasks with at least the same competences as human experts. In particular for automated inspection of natural objects this is not easy to achieve. The task is hampered by large in-class variations and complex 3D-morphology of the objects and subtle argumentations of experts. For example, in our horticultural case we deal with quality assessment of young tomato plants, which requires experienced specialists. We submit that automation of such a task employing an explicit model of the objects and their assessment is preferred over a black-box model obtained from modelling input-output relations only. We propose to employ ontologies for representing the geometrical shapes, object parts and quality classes associated with the explicit models. Our main contribution is the description of a method to develop a white-box computer vision application in which the needed expert knowledge is defined by (i) decomposing the task of the inspection system into subtasks and (ii) identifying the algorithms that execute the subtasks. This method describes the interaction between the task decomposition and the needed task-specific knowledge and studies the delicate balance between general domain knowledge and task-specific details. As a proof of principle of this methodology, we work through a horticultural case study and argue that the method leads to robust, well-performing, and extendable computer vision system.