Abstract. An approach to improve an RCC-derived geospatial approximation is presented which makes use of concept inclusion axioms in OWL. The algorithm used to control the approximation combines hypothesis testing with consistency checking provided by a knowledge representation system based on description logics. Propositions about the consistency of the refined ABox w.r.t. the associated TBox when compared to baseline ABox and TBox are made. Formal proves of the divergent consistency results when checking either of both are provided. The application of the approach to a geospatial setting results in a roughly tenfold improved approximation when using the refined ABox and TBox. Ways to further improve the approximation and to automate the detection of falsely calculated relations are discussed.

Over the last few years, the processing of dynamic data has gained increasing attention in the Semantic Web community. This led to the development of several stream reasoning systems that enable on-the-fly processing of semantically annotated data that changes over time. Due to their streaming nature, analyzing such systems is extremely difficult. Currently, their evaluation is conducted under heterogeneous scenarios, which makes it hard to clearly compare them, understanding the benefits and limitations of each of them. In this paper, we strive for a better understanding the key challenges that these systems must face and define a generic methodology to evaluate their performance. Specifically, we identify three Key Performance Indicators (KPIs) and seven commandments that specify how to design the stress tests for system evaluation.Abstract. Over the last few years, the processing of dynamic data has gained increasing attention in the Semantic Web community. This led to the development of several stream reasoning systems that enable on-thefly processing of semantically annotated data that changes over time. Due to their streaming nature, analyzing such systems is extremely difficult. Currently, their evaluation is conducted under heterogeneous scenarios, which makes it hard to clearly compare them, understanding the benefits and limitations of each of them. In this paper, we strive for a better understanding the key challenges that these systems must face and define a generic methodology to evaluate their performance. Specifically, we identify three Key Performance Indicators (KPIs) and seven commandments that specify how to design the stress tests for system evaluation.

In order to support the processing of qualitative spatial queries, spatial knowledge must be represented in a way that machines can make use of it. Ontologies typically represent thematic knowledge. Enhancing them with spatial knowledge is still a challenge. In this article, an implementation of the Region Connection Calculus (RCC) in the Web Ontology Language (OWL), augmented by DL-safe SWRL rules, is used to represent spatio-thematic knowledge. This involves partially ordered partitions, which are implemented by nominals and functional roles. Accordingly, a spatial division into administrative regions, rather than, for instance, a metric system, is used as a frame of reference for evaluating closeness. Hence, closeness is evaluated purely according to qualitative criteria. Colloquial descriptions typically involve qualitative concepts. The approach presented here is thus expected to align better with the way human beings deal with closeness than does a quantitative approach. To illustrate the approach, it is applied to the retrieval of documents from the database of the Datacenter Nature and Landscape (DNL).

Processing streams rather than static files of Linked Data has gained increasing importance in the web of data. When processing data streams system builders are faced with the conundrum of guaranteeing a constant maximum response time with limited resources and, possibly, no prior information on the data arrival frequency. One approach to address this issue is to delete data from a cache during processing -a process we call eviction. The goal of this paper is to show that data-driven eviction outperforms today's dominant data-agnostic approaches such as first-in-first-out or random deletion. Specifically, we first introduce a method called Clock that evicts data from a join cache based on the likelihood estimate of contributing to a join in the future. Second, using the well-established SR-Bench benchmark as well as a data set from the IPTV domain, we show that Clock outperforms data-agnostic approaches indicating its usefulness for resource-limited linked data stream processing. Abstract. Processing streams rather than static files of Linked Data has gained increasing importance in the web of data. When processing datastreams system builders are faced with the conundrum of guaranteeing a constant maximum response time with limited resources and, possibly, no prior information on the data arrival frequency. One approach to address this issue is to delete data from a cache during processing -a process we call eviction. The goal of this paper is to show that datadriven eviction outperforms today's dominant data-agnostic approaches such as first-in-first-out or random deletion. Specifically, we first introduce a method called Clock that evicts data from a join cache based on the likelihood estimate of contributing to a join in the future. Second, using the well-established SR-Bench benchmark as well as a data set from the IPTV domain, we show that Clock outperforms data-agnostic approaches indicating its usefulness for resourcelimited linked data stream processing.

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