Bob Wielinga’s research while affiliated with Vrije Universiteit Amsterdam and other places

It is possible to automatically annotate a natural science spreadsheet using lexical matching, given that the tables in these spreadsheets meet a number of requirements regarding the content. Results of a survey show that most of the existing natural science spreadsheets deviate from the ideal situation. We propose to complement lexical matching with both heuristics and knowledge from external vocabularies to overcome these deviations.

In this paper we present an experiment which has been performed to validate a pragmatic-based, expert-based and basic-level ontology. These ontologies were created for use in an application which generates questions for ordinary people with the purpose to determine a crisis situation. All three ontologies have specific characteristics related to their method of creation. This experiment shows that using the basic-level ontology results in the fastest and least ambiguous determination of a crisis situation.

Using an ontology to automatically generate questions for ordinary people requires a structure and concepts compliant with human thought. Here we present methods to develop a pragmatic, expert-based and a basic-level ontology and a framework to evaluate these ontologies. Comparing these ontologies shows that expert-based ontologies are most easy to construct but lack required cognitive semantic characteristics. Basic-level ontologies have structure and concepts which are better in terms of cognitive semantics but are most expensive to construct.

Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of "bare" data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous — let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice it is often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain.

This paper presents research in the context of two multidisciplinary projects aimed at facilitating the history domain with an automatic approach for event extraction and modelling. To realise this, the Semantics of History project is providing a historical ontology and a lexicon to support the detection of historical events in textual data whilst the Agora project focusses on exploring the modelling aspects of historical events and employing the combined results in an event-driven browse and search approach. Furthermore, the historical events are used as a flexible model to identify semantically relevant relationships between objects in highly diverse museum collections, creating meaningful 'cause' and 'effect' links along the key event dimensions 'who', 'what', 'where' and 'when'. This should finally support the (re)interpretation process of history research, by allowing end-users to create their own personal narratives, leading to theoretical reflection on the meaning of digitally mediated public history in contemporary society. In this paper, we give a high-level overview of the research challenges in the realisation of a desired search and browse scenario. Finally, we outline the open issues and future research.