Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.     

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.     

Deeply integrating Linked Data with Geographic Information Systems

The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.     

An Ontology‐Driven Framework and Web Portal for Spatial Decision Support1

Numerous systems and tools have been developed for spatial decision support (SDS), but they generally suffer from a lack of re‐usability, inconsistent terminology, and weak conceptualization. We introduce a collaborative effort by the SDS Consortium to build a SDS knowledge portal. We present the formal representation of knowledge about SDS, the various ontologies captured and made accessible by the portal, and the processes used to create them. We describe the portal in action, and the ways in which users can search, browse, and make use of its content. Finally, we discuss the lessons learned from this effort, and future development directions. Our work demonstrates how ontologies and semantic technologies can support the documentation and retrieval of dynamic knowledge in GIScience by offering flexible schemata instead of fixed data structures.     

A Task Ontology Driven Approach for Live Geoprocessing in a Service‐Oriented Environment

In a service‐oriented environment, Web geoprocessing services can provide geoprocessing functions for a variety of applications including Sensor Web. Connecting Sensor Web and geoprocessing services together shows great potentail to support live geoprocessing using real‐time data inputs. This article proposes a task ontology driven approach to live geoprocessing. The task in the ontology contains five aspects: task type, task priority, task constraints, task model, and task process. The use of the task ontology in driving live geoprocessing includes the following steps: (1) Task model generation, which generates a concrete process model to fulfill user demands; (2) Process model instantiation, which transforms the process model into an executable workflow; (3) Workflow execution: the workflow engine executes the workflow to generate value‐added data products using Sensor Web data as inputs. The approach not only helps create semantically correct connections between Sensor Web and Web geoprocessing services, but also provides sharable problem solving knowledge using process models. A prototype system, which leverages Web 2.0, Sensor Web, Semantic Web, and geoprocessing services, is developed to demonstrate the applicability of the approach.     

Loose programming of GIS workflows with geo‐analytical concepts

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.     

Characterizing Spatial Databases via their Derivation: A Complement to Content Ontologies

Developing an ontology that succinctly describes the contents of a spatial database is a very difficult undertaking. Yet most current efforts to develop spatial ontologies remain focused on describing content. Ontologies describing other aspects of spatial databases may prove to be much easier to develop and nearly as useful as content ontologies, and yet these alternative ontologies have received little attention from the research community. This paper explores one such alternative, specifically, an ontology that describes how a spatial database may have been derived. Derivation ontologies are shown to be highly complementary to content ontologies, and in some cases can perform nearly identical tasks. It is also shown that derivation ontologies are much more straightforward to develop than are content ontologies. Finally, we present a genetic programming (GP)‐based approach to automatically developing derivation ontologies for existing databases. It is concluded that while derivation ontologies cannot replace content ontologies, they are a useful and practical complement that offer their own unique set of strengths to the problem of semantically characterizing spatial data.     

Collaborative Ontology Development for the Geosciences

Ontology‐based information publishing, retrieval, reuse, and integration have become popular research topics to address the challenges involved in exchanging data between heterogeneous sources. However, in most cases ontologies are still developed in a centralized top‐down manner by a few knowledge engineers. Consequently, the role that developers play in conceptualizing a domain such as the geosciences is disproportional compared with the role of domain experts and especially potential end‐users. These and other drawbacks have stimulated the creation of new methodologies focusing around collaboration. Based on a review of existing approaches, this article presents a two‐step methodology and implementation to foster collaborative ontology engineering in the geosciences. Our approach consists of the development of a minimalistic core ontology acting as a catalyst and the creation of a virtual collaborative development cycle. Both methodology and prototypical implementation have been tested in the context of the EU‐funded ForeStClim project which addresses environmental protection with respect to forests and climate change.     

A Framework for Intensional and Extensional Integration of Geographic Ontologies

This paper presents ongoing research in the field of extensional mappings between ontologies. Hitherto, the task of generating mappings between ontologies has focused on the intensional level of ontologies. The term intensional level herein, refers to the set of concepts that are included in an ontology. However, an ontology that has been created for a specific task or application needs to be populated with instances. These comprise the extensional level of an ontology. This particular level is generally neglected during the ontologies’ integration procedure. Thus, although methodologies of geographic ontologies integration, ranging from alignment to true integration, have, in the course of years, presented a solid ground for information exchange, little has been done in exploring the relationships between the data. In this context, this research strives to set a framework for extensional mappings between ontologies using Information Flow Theory by presenting a case study of interoperability between the thematic content of maps.     

An ontological system based on MODIS images to assess ecosystem functioning of Natura 2000 habitats: A case study for Quercus pyrenaica forests

The implementation of the Natura 2000 network requires methods to assess the conservation status of habitats. This paper shows a methodological approach that combines the use of (satellite) Earth observation with ontologies to monitor Natura 2000 habitats and assess their functioning. We have created an ontological system called Savia that can describe both the ecosystem functioning and the behaviour of abiotic factors in a Natura 2000 habitat. This system is able to automatically download images from MODIS products, create indicators and compute temporal trends for them. We have developed an ontology that takes into account the different concepts and relations about indicators and temporal trends, and the spatio-temporal components of the datasets. All the information generated from datasets and MODIS images, is stored into a knowledge base according to the ontology. Users can formulate complex questions using a SPARQL end-point. This system has been tested and validated in a case study that uses Quercus pyrenaica Willd. forests as a target habitat in Sierra Nevada (Spain), a Natura 2000 site. We assess ecosystem functioning using NDVI. The selected abiotic factor is snow cover. Savia provides useful data regarding these two variables and reflects relationships between them.     

Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.     

A Spatial Data Infrastructure Approach for the Characterization of New Zealand's Groundwater Systems

The future information needs of stakeholders for hydrogeological and hydro‐climate data management and assessment in New Zealand may be met with an Open Geospatial Consortium (OGC) standards‐compliant publicly accessible web services framework which aims to provide integrated use of groundwater information and environmental observation data in general. The stages of the framework development described in this article are search and discovery as well as data collection and access with (meta)data services, which are developed in a community process. The concept and prototype implementation of OGC‐compliant web services for groundwater and hydro‐climate data include demonstration data services that present multiple distributed datasets of environmental observations. The results also iterate over the stakeholder community process and the refined profile of OGC services for environmental observation data sharing within the New Zealand Spatial Data Infrastructure (SDI) landscape, including datasets from the National Groundwater Monitoring Program and the New Zealand Climate Database along with datasets from affiliated regional councils at regional‐ and sub‐regional scales. With the definition of the New Zealand observation data profile we show that current state‐of‐the‐art standards do not necessarily need to be improved, but that the community has to agree upon how to use these standards in an iterative process.     

The Basic Formal Ontology as a Reference Framework for Modeling the Evolution of Administrative Units

In information systems, ontologies promise advantages such as enhanced interoperability, knowledge sharing, and integration of data sources. In this article, we show that the upper ontology Basic Formal Ontology can facilitate the modeling of an evolution of administrative units. This is demonstrated by creating a spatiotemporal ontology for the administrative units of Switzerland. The ontology tackles the problem that the geometric data is typically captured by taking snapshots at regular intervals while the thematic data is continually updated. The ontology presented merges time‐stamped geometries with a formally described history of administrative units, allowing for complex spatiotemporal queries neither standard approach would support. The resulting populated knowledge base was evaluated against a set of spatiotemporal test queries. The evaluation showed that this knowledge base supports a wide range of queries on the evolution of the administrative units of Switzerland between 1960 and 2010.     

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.     

Thinking about Space‐Time Connections: Spatiotemporal Scheduling of Individual Activities

This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space‐time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space‐time and affecting spatiotemporal accessibility for individuals.     

A Task-Based Ontology Approach to Automate Geospatial Data Retrieval

This paper presents a task‐based and Semantic Web approach to find geospatial data. The purpose of the project is to improve data discovery and facilitate automatic retrieval of data sources. The work presented here helps create the beginnings of a Geospatial Semantic Web. The intent is to create a system that provides appropriate results to application users who search for data when facing tasks such as emergency response or planning activities. In our task‐based system, we formalize the relationships between types of tasks, including emergency response, and types of data sources needed for those tasks. Domain knowledge, including criteria describing data sources, is recorded in an ontology language. With the ontology, reasoning can be done to infer various types of information including which data sources meet specific criteria for use in particular tasks. The vision presented here is that in an emergency, for example, a user accesses a Web‐based application and selects the type of emergency and the geographic area. The application then returns the types and locations (URLs) of the specific geospatial data needed. We explore the abilities and limitations of the OWL Web Ontology Language along with other Semantic Web technologies for this purpose.     

Geo‐Social Model: A Conceptual Framework for Real‐time Geocollaboration

Synchronous geocollaboration helps geographically dispersed people to work together in a shared geospatial environment. Its real‐time nature, multiple users' interaction and diversity of work context impose some special social, organizational and technological requirements, making the development of such real‐time geocollaboration systems a challenging task. A conceptual framework is therefore needed to specify and describe what synchronous geocollaboration is, considering its social, spatial and technical aspects. The geo‐social model presented in this article describes a conceptual framework for synchronous geocollaboration systems addressing the above aspects, identifies the core elements of the system and describes how these elements collaborate with each other. This model is presented using application‐level ontology and is then applied to a multi‐agent system based prototype in which multiple users can interact and negotiate in a shared 3D geospatial environment.     

Asynchronous Geospatial Processing: An Event‐Driven Push‐Based Architecture for the OGC Web Processing Service

Geospatial processing tasks like solar potential analyses or floodplain investigations within flood scenarios are often complex and deal with large amounts of data. If such analysis operations are performed in distributed web‐based systems, technical capabilities are mostly not sufficient. Major shortcomings comprise the potentially long execution times and the vast amount of messaging overhead that arise from common poll‐based approaches. To overcome these issues, an approach for an event‐driven architecture for web‐based geospatial processing is proposed within this article. First, this article presents a thorough qualitative discussion of different available technologies for push‐based notifications. The aim of this discussion is to find the most suitable push‐based messaging technologies for application with OGC Web Processing Services (WPS). Based on this, an event‐driven architecture for asynchronous geospatial processing with the WPS is presented, building on the Web Socket Protocol as the transport protocol and the OGC Event Service as the message‐oriented middleware. The proposed architecture allows pushing notifications to clients once a task has completed. This paradigm enables the efficient execution of web‐based geospatial processing tasks as well as the integration of geographical analyses into event‐driven real‐time workflows.