Abstract. Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

Executive summaryThe role that ontologies play or can play in designing and employing semantic technologies has been widely acknowledged by the Semantic Web and Linked Data communities. But the level of collaboration between these communities and the Applied Ontology community has been much less than expected. Also, ontologies and ontological techniques appear to be of marginalized use in Big Data and its applications.To understand this situation and foster greater collaboration, Ontology Summit 2014 brought together representatives from the Semantic Web, Linked Data, Big Data and Applied Ontology communities, to address three basic problems involving applied ontology and these communities:(1) The role of ontologies [in these communities], (2) Current uses of ontologies in these communities, and (3) Engineering of ontologies and semantic integration.The intent was to identify and understand: (a) causes and challenges (e.g. scalability) that hinder reuse of ontologies in Semantic Web and Linked Data, (b) solutions that can reduce the differences between ontologies on and off line, and (c) solutions to overcome engineering bottlenecks in current Semantic Web and Big Data applications.Over the past four months, presentations from, and discussions with, representatives of the Semantic Web, Linked Data, and Applied Ontology communities have taken place across four tracks. Each Track focused on different aspects of this year's Summit topic: (Track A) Investigation of sharable and reusable ontologies; (Track B) Tools, services and techniques for a comprehensive and effective use of ontologies; (Track C) Investigation of the engineering bottlenecks and the ways to prevent and overcome them; (Track D) Enquiry on the variety problem in Big Data.In addition to the four Tracks' activities there was a Hackathon. Six different Hackathon projects took place, all available at their individual project public repositories. An online Community Library and an online Ontology Repository have been created as freely accessible Community resources.This Ontology Summit 2014 Communique presents a summary of the results, original in its attempt both to merge different communities' discourses and to achieve consensus across the Summit participants with respect to open problems and recommendations to address them.

Examined the relationship between certain handwriting characteristics and Eysenck's Extraversion-Introversion and Kagan's Impulsivity-Reflectivity personality dimensions. Using 46 female subjects, scores on the EPQ inventory and Matching Familiar Figures test were compared by factor analysis to the handwriting characteristics of: middle zone height; middle zone breadth; upper zone height; lower zone height; space between words; right margin breadth; left margin breadth; a general size measure; handwriting time; paragraph indention; slant. The factor analysis revealed three distinct writing styles: one related to Extraversion; another related to Introversion; and a final one related to Reflectivity. These findings were discussed in relation to statements made by graphologists. Also, the significance of trait-sign and factor analysis in comparison with other methods of graphoanalysis was discussed.

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