Dave Kolas

Enabling the geospatial Semantic Web with Parliament and GeoSPARQL

As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. GeoSPARQL, a forthcoming OGC standard, attempts to unify data access for the geospatial Semantic Web. As authors of the Parliament triple store and contributors to the GeoSPARQL specification, we are particularly interested in the issues of geospatial data access and indexing. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on GeoSPARQL. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and finally our implementation of GeoSPARQL in the Parliament triple store.

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.

Five stars of Linked Data vocabulary use

In 2010 Tim Berners-Lee introduced a 5 star rating to his Linked Data design issues page to encourage data publishers along the road to good Linked Data. What makes the star rating so effective is its simplicity, clarity, and a pinch of psychology --is your data 5 star? While there is an abundance of 5 star Linked Data available today, finding, querying, and integrating/interlinking these data is, to say the least, difficult. While the literature has largely focused on describing datasets, e.g., by adding provenance information, or interlinking them, e.g., by co-reference resolution tools, we would like to take Berners-Lees original proposal to the next level by introducing a 5 star rating for Linked Data vocabulary use.

Rule-Based Discovery in Spatial Data Infrastructure

Answering questions based on spatial data is becoming increasingly important in a variety of domains. Often the required data are distributed and heterogeneous, and several data sources need to be combined in order to derive the information required by a user. Spatial data infrastructures (SDIs) are aimed at making the discovery and access to distributed geographic data more efficient. However, the catalogue services currently used in SDIs for discovering geographic data do not allow expressive queries and do not take into account that more than one data source might be required to answer a question. In this paper, we present a methodology that uses rules for both the discovery of data sources and, based on the discovered data, answering user queries in SDIs. We illustrate how this methodology allows inferences that use relationships between individuals and the combination of data from different sources, thus overcoming some of the limitations of other Semantic Web approaches that are based on Description Logics. The approach is illustrated by an example from the domain of disaster management.

Spatially-augmented knowledgebase

As an increasing number of applications on the web contain some elements of spatial data, there is a need to efficiently integrate Semantic Web technologies and spatial data processing. This paper describes a prototype system for storing spatial data and Semantic Web data together in a SPatially-AUgmented Knowledgebase (SPAUK) without sacrificing query efficiency. The goals are motivated through use several use cases. The prototypes design and architecture are described, and resulting performance improvements are discussed.

An ontology design pattern for surface water features

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Querying Geospatial Data over the Web: a GeoSPARQL Interface

This paper describes our work to create an interface to facilitate querying using GeoSPARQL. GeoSPARQL is a recent extension to the RDF query language SPARQL in which spatial operators are added. Both SPARQL and GeoSPARQL are W3C standards. GeoSPARQL will be especially useful to geospatial specialists to be able to query RDF data containing spatial information over the Web, instead of loading data into a Geographic Information System (GIS). However, GeoSPARQL queries are difficult to write. To solve this problem, we developed a Web-based GeoQuery tool with an intuitive interface geared toward geospatial professionals. GeoQuery includes drop down lists to select attributes and to select spatial operators. Using user input, a query in GeoSPARQL syntax is automatically generated. Further, output is displayed on a map. GeoQuery uses the Parliament implementation of GeoSPARQL for query processing.