In most intent recognition studies, annotations of query intent are created post hoc by external assessors who are not the searchers themselves. It is important for the field to get a better understanding of the quality of this process as an approximation for determining the searcher's actual intent. Some studies have investigated the reliability of the query intent annotation process by measuring the interassessor agreement. However, these studies did not measure the validity of the judgments, that is, to what extent the annotations match the searcher's actual intent. In this study, we asked both the searchers themselves and external assessors to classify queries using the same intent classification scheme. We show that of the seven dimensions in our intent classification scheme, four can reliably be used for query annotation. Of these four, only the annotations on the topic and spatial sensitivity dimension are valid when compared with the searcher's annotations. The difference between the interassessor agreement and the assessor‐searcher agreement was significant on all dimensions, showing that the agreement between external assessors is not a good estimator of the validity of the intent classifications. Therefore, we encourage the research community to consider using query intent classifications by the searchers themselves as test data.

Applying artificial intelligence techniques to management of chronic diseases - smart monitoring - has great potential to improve chronic disease care. Probabilistic models offer powerful methods for automatic data interpretation, and thus play a potentially large role in mobile, personalised care. In particular in the context of disease monitoring one needs clinical time-series data that include data of multiple patient parameters, to allow building such models. However, in practice clinical time-series data of patients with chronic disease are only limited available, and when they are available usually only of a few patients. In this paper, we explore different ways to build predictive models for the detection of COPD exacerbations and related hospitalisation, focusing on the temporal aspect of monitoring data while taking into account data sparsity. Preliminary results indicate that even with the limited data available some predictions can be made about hospitalisation.

Recently a number of studies have demonstrated that search engine logfiles are an important resource to determine the relevance relation between URLs and query terms. We hypothesized that the queries associated with a URL could also be presented as useful URL metadata in a search engine result list, e.g. for helping to determine the semantic category of a URL. We evaluated this hypothesis by a classification experiment based on the DMOZ dataset. Our method can also annotate URLs that have no associated queries.