Simon Scheider

A Geo-ontology Design Pattern for Semantic Trajectories

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.

Geospatial semantics and linked spatiotemporal data --Past, present, and future

The Geosciences and Geography are not just yet another application area for semantic technologies. The vast heterogeneity of the involved disciplines ranging from the natural sciences to the social sciences introduces new challenges in terms of interoperability. Moreover, the inherent spatial and temporal information components also require distinct semantic approaches. For these reasons, geospatial semantics, geo-ontologies, and semantic interoperability have been active research areas over the last 20 years. The geospatial semantics community has been among the early adopters of the Semantic Web, contributing methods, ontologies, use cases, and datasets. Today, geographic information is a crucial part of many central hubs on the Linked Data Web. In this editorial, we outline the research field of geospatial semantics, highlight major research directions and trends, and glance at future challenges. We hope that this text will be valuable for geoscientists interested in semantics research as well as knowledge engineers interested in spatiotemporal data.

Grounding geographic categories in the meaningful environment

Ontologies are a common approach to improve semantic interoperability by explicitly specifying the vocabulary used by a particular information community. Complex expressions are defined in terms of primitive ones. This shifts the problem of semantic interoperability to the problem of how to ground primitive symbols. One approach are semantic datums, which determine reproducible mappings (measurement scales) from observable structures to symbols. Measurement theory offers a formal basis for such mappings. From an ontological point of view, this leaves two important questions unanswered. Which qualities provide semantic datums? How are these qualities related to the primitive entities in our ontology? Based on a scenario from hydrology, we first argue that human or technical sensors implement semantic datums, and secondly that primitive symbols are definable from the meaningful environment, a formalized quality space established through such sensors.

Affordance-based categorization of road network data using a grounded theory of channel networks

We propose a grounded ontological theory of channel networks to categorize features, such as junctions, in road network databases. The theory is grounded, because its primitives can be given an unambiguous interpretation into directly observable qualities of physical road networks, such as supported movements and their medium, connectedness of such media, and turnoff restrictions. The theory provides a very general approach to automatically annotate and integrate road network data from heterogeneous sources, because it rests on application-independent observation principles. We suggest that road network categories such as junctions and roads are based on locomotion affordances. Road network databases can be mapped into our channel network theory, so that instances of roads and junctions can be automatically categorized or checked for consistency by what they afford. In this paper, we introduce affordance-based definitions of a road network and a junction, and show that the definition of latter is satisfied by some of the most common junction types.

An Ontology Design Pattern for Cartographic Map Scaling

The concepts of scale is at the core of cartographic abstraction and mapping. It defines which geographic phenomena should be displayed, which type of geometry and map symbol to use, which measures can be taken, as well as the degree to which features need to be exaggerated or spatially displaced. In this work, we present an ontology design pattern for map scaling using the Web Ontology Language (OWL) within a particular extension of the OWL RL profile. We explain how it can be used to describe scaling applications, to reason over scale levels, and geometric representations. We propose an axiomatization that allows us to impose meaningful constraints on the pattern, and, thus, to go beyond simple surface semantics. Interestingly, this includes several functional constraints currently not expressible in any of the OWL profiles. We show that for this specific scenario, the addition of such constraints does not increase the reasoning complexity which remains tractable.

Meaningful spatial prediction and aggregation

The appropriateness of spatial prediction methods such as Kriging, or aggregation methods such as summing observation values over an area, is currently judged by domain experts using their knowledge and expertise. In order to provide support from information systems for automatically discouraging or proposing prediction or aggregation methods for a dataset, expert knowledge needs to be formalized. This involves, in particular, knowledge about phenomena represented by data and models, as well as about underlying procedures. In this paper, we introduce a novel notion of meaningfulness of prediction and aggregation. To this end, we present a formal theory about spatio-temporal variable types, observation procedures, as well as interpolation and aggregation procedures relevant in Spatial Statistics. Meaningfulness is defined as correspondence between functions and data sets, the former representing data generation procedures such as observation and prediction. Comparison is based on semantic reference systems, which are types of potential outputs of a procedure. The theory is implemented in higher-order logic (HOL), and theorems about meaningfulness are proved in the semi-automated prover Isabelle. The type system of our theory is available as a Web Ontology Language (OWL) pattern for use in the Semantic Web. In addition, we show how to implement a data-model recommender system in the statistics tool environment R. We consider our theory groundwork to automate semantic interoperability of data and models. We introduce a new notion of meaningfulness of spatial prediction and aggregation.Observation, prediction, and aggregation procedures are formalized as functions.We show how datasets can be described as results of executing such procedures.We propose formal checks of meaningfulness based on functional correspondence.Meaningfulness checks on the type level are implemented in the R software and OWL.

Pedestrian flow prediction in extensive road networks using biased observational data

In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.

Towards sustainable mobility behavior: research challenges for location-aware information and communication technology

Private transport accounts for a large amount of total CO2 emissions, thus significantly contributing to global warming. Tools that actively support people in engaging in a more sustainable life-style without restricting their mobility are urgently needed. How can location-aware information and communication technology (ICT) enable novel interactive and participatory approaches that help people in becoming more sustainable? In this survey paper, we discuss the different aspects of this challenge from a technological and cognitive engineering perspective, based on an overview of the main information processes that may influence mobility behavior. We review the state-of-the-art of research with respect to various ways of influencing mobility behavior (e.g., through providing real-time, user-specific, and location-based feedback) and suggest a corresponding research agenda. We conclude that future research has to focus on reflecting individual goals in providing personal feedback and recommendations that take into account different motivational stages. In addition, a long-term and large-scale empirical evaluation of such tools is necessary.

Exploratory querying of SPARQL endpoints in space and time

The linked data Web provides a simple and flexible way of accessing information resources in a self-descriptive format. This offers a realistic chance of perforating existing data silos. However, in order to do so, space, time and other semantic concepts need to function as dimensions for effectively exploring, querying and filtering contents. While triple stores, SPARQL endpoints, and RDF were designed for machine access, large burdens are still placed on a user to simultaneously explore and query the contents of a given endpoint according to these dimensions. First, one has to know the semantic concepts and the type of knowledge contained in an endpoint a-priori in order to query content effectively. Second, one has to be able to write and understand SPARQL and RDF. And third, one has to understand complex data type literals for space and time. In this article, we propose a way to deal with these challenges by interactive visual query construction, i.e., by letting query results feedback into both (space-time) exploration and filtering, and thus enabling exploratory querying. We propose design principles for SPEX (Spatio-temporal content explorer), a tool which helps people unfamiliar with the content of SPARQL endpoints or their syntax to explore the latter in space and time. In a preliminary user study on a repository of historical maps, we found that our feedback principles were effective, however, that successful question answering still requires improvements regarding space-time filtering, vocabulary explanation and the linking of space-time windows with other displays.

Place reference systems: A constructive activity model of reference to places

Reference to places is a central but largely underexposed problem of information science. Place has been a major object of research in many domains including Geography, Cognitive Science and Geographic Information Science. However, Geographic Information Systems (GIS) have been built solely on space reference systems creating a gap between human conceptualization and machine representation. While reference to space only partially captures reference to place, most existing definitions of place either reduce the latter to the former or lack a formal characterization of how places are constructed. In a spatial coordinate system, locations are referenced by angles and distances to other referents. In this paper, we suggest that place reference systems can be built based on localizing things (locatums) involved in simulated activities relative to other involved referents (locators). We propose a formal theory about relevant types of activities and their involved participants, and show how place referents can be identified and localized by choosing locators and locatum among the participants. We formally derive an ontology of places, publish a corresponding OWL version, and demonstrate how to compute a market place and a vantage place in a GIS.