The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

While significant progress has been made to implement the Digital Earth vision,current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes.To achieve effectiveness of decisionmaking using Digital Earth for understanding the Earth and its systems,new infrastructures that provide capabilities of computational simulation are needed.This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems(SDSSs)to expand capabilities of the currently implemented infrastructure of Digital Earth.Main technologies applied in the framework such as heterogeneous ontology integration,ontology-based catalog service,and web service composition were introduced.We proposed a partitionrefinement algorithm for ontology matching and integration,and an algorithm for web service discovery and composition.The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet.Based on the proposed framework,a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.

Towards geospatial semantic search:exploiting latent semantic relations in geospatial data

This paper reports our efforts to address the grand challenge of the Digital Earth vision in terms of intelligent data discovery from vast quantities of geo-referenced data.We propose an algorithm combining LSA and a Two-Tier Ranking(LSATTR)algorithm based on revised cosine similarity to build a more efficient search engine-Semantic Indexing and Ranking(SIR)-for a semantic-enabled,more effective data discovery.In addition to its ability to handle subject-based search,we propose a mechanism to combine geospatial taxonomy and Yahoo!GeoPlanet for automatic identification of location information from a spatial query and automatic filtering of datasets that are not spatially related.The metadata set,in the format of ISO19115,from NASA's SEDAC(Socio-Economic Data Application Center)is used as the corpus of SIR.Results show that our semantic search engine SIR built on LSATTR methods outperforms existing keyword-matching techniques,such as Lucene,in terms of both recall and precision.Moreover,the semantic associations among all existing words in the corpus are discovered.These associations provide substantial support for automating the population of spatial ontologies.We expect this work to support the operationalization of the Digital Earth vision by advancing the semantic-based geospatial data discovery.

Geospatial data ontology:the semantic foundation of geospatial data integration and sharing

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science.However,the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing.Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines.Thus,many efforts have been made to explore ontology-based geospatial data integration and sharing.However,there is a lack of a specialized ontology that would provide a unified description for geospatial data.In this paper,with a focus on the characteristics of geospatial data,we propose a unified framework for geospatial data ontology,denoted GeoDataOnt,to establish a semantic foundation for geospatial data integration and sharing.First,we provide a characteristics hierarchy of geospatial data.Next,we analyze the semantic problems for each characteristic of geospatial data.Subsequently,we propose the general framework of GeoDataOnt,targeting these problems according to the characteristics of geospatial data.GeoDataOnt is then divided into multiple modules,and we show a detailed design and implementation for each module.Key limitations and challenges of GeoDataOnt are identified,and broad applications of GeoDataOnt are discussed.

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics.Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization.There is a lack of understanding on the impact of spatial scale,semantic granularity,and geographic context on POI-based land use modeling,particularly large-scale land use modeling.In this study,we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions.We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources.Then,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs.We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

A parallel approach for improving Geo-SPARQL query performance

Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.