The publications of the Information Management group can be accessed via the buttons on the left. Examples of our publications are:
Converting and Annotating Quantitative Data Tables
Mark van Assem, Hajo Rijgersberg, Mari Wigham and Jan Top, ISWC 2010
Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in .g. spreadsheet les and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format difficult. For example, he table header cell "f (Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.
Bounded Transparency for Automated Inspection in Agriculture
Koenderink, N.J.J.P. Broekstra, J. Top, J.L., Computers in Agriculture 2010
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., and Meinders, M., IEEE Intelligent Systems 2009
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.