scholarly journals TOWARD SEMANTIC WEB INFRASTRUCTURE FOR SPATIAL FEATURES' INFORMATION

2015 ◽  
Vol XL-1-W5 ◽  
pp. 57-62
Author(s):  
R. Arabsheibani ◽  
S. Ariannamazi ◽  
F. Hakimpour
Keyword(s):  
Semantic Web ◽  
Information Integration ◽  
Spatial Data ◽  
Graph Databases ◽  
Huge Amount ◽  
Relational Models ◽  
Data Infrastructure ◽  
Spatial Features ◽  
Web Infrastructure ◽  
The Web

The Web and its capabilities can be employed as a tool for data and information integration if comprehensive datasets and appropriate technologies and standards enable the web with interpretation and easy alignment of data and information. Semantic Web along with the spatial functionalities enable the web to deal with the huge amount of data and information. The present study investigate the advantages and limitations of the Spatial Semantic Web and compare its capabilities with relational models in order to build a spatial data infrastructure. An architecture is proposed and a set of criteria is defined for the efficiency evaluation. The result demonstrate that when using the data with special characteristics such as schema dynamicity, sparse data or available relations between the features, the spatial semantic web and graph databases with spatial operations are preferable.

scholarly journals WPS ENABLED SDI: AN OPEN SOURCE APPROACH TO PROVIDE GEOPROCESSING IN WEB ENVIRONMENT

2019 ◽  
Vol IV-5/W2 ◽  
pp. 119-126
Author(s):  
A. K. Tripathi ◽  
S. Agrawal ◽  
R. D. Gupta
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Geospatial Data ◽  
Distributed Data ◽  
Data Infrastructure ◽  
Web Environment ◽  
Local Resources ◽  
Processing Service ◽  
Server Architecture ◽  
The Web

Abstract. Sharing and management of geospatial data among different communities and users is a challenge which is suitably addressed by Spatial Data Infrastructure (SDI). SDI helps people in the discovery, editing, processing and visualization of spatial data. The user can download the data from SDI and process it using the local resources. However, large volume and heterogeneity of data make this processing difficult at the client end. This problem can be resolved by orchestrating the Web Processing Service (WPS) with SDI. WPS is a service interface through which geoprocessing can be done over the internet. In this paper, a WPS enabled SDI framework with OGC compliant services is conceptualized and developed. It is based on the three tier client server architecture. OGC services are provided through GeoServer. WPS extension of GeoServer is used to perform geospatial data processing and analysis. The developed framework is utilized to create a public health SDI prototype using Open Source Software (OSS). The integration of WPS with SDI demonstrates how the various data analysis operations of WPS can be performed over the web on distributed data sources provided by SDI.

Semantic Web Based Agricultural Information Integration

2017 ◽  
Vol 8 (3) ◽  
pp. 39-51 ◽  
Author(s):  
Kaladevi Ramar ◽  
Geetha Gurunathan
Keyword(s):  
Information System ◽  
Semantic Web ◽  
Information Integration ◽  
Relevant Information ◽  
Integrated Systems ◽  
Web Based ◽  
Prevention Methods ◽  
Available Information ◽  
Semantic Linkage ◽  
The Web

Huge volume of information is available in the WWW. However, the demand is on relevant information rather than available information, which are often heterogeneous and distributed. Agriculture is one such domain, which includes information like soil, crops, weather, etc., under one roof. This information is in different representations and structures e.g. weather. This scenario leads to a challenge that how to integrate the available and heterogeneous agricultural information to deliver better production. The information on the web is syntactically structured but, the need is to provide semantic linkage. The semantic web supports the existing web to easily process and interpret information. In this paper, a semantic based Agricultural Information System (AIS) is proposed which addresses heterogeneity issues among weather systems and integrates information like soil, weather, crop and fertilizers. AIS helps the farmers regarding the type of crop/soil, crop/climate, fertilizer applications, diseases and prevention methods using effective retrieval of information from integrated systems.

XML Mining for Semantic Web

2011 ◽  
pp. 317-342
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

XML Mining for Semantic Web

Data Mining ◽  
2013 ◽  
pp. 625-649
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

scholarly journals Open Community-Based Crowdsourcing Geoportal for Earth Observation Products: A Model Design and Prototype Implementation

2021 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
Mohammad H. Vahidnia ◽  
Hossein Vahidi
Keyword(s):  
Spatial Data ◽  
Earth Observation ◽  
Easy Access ◽  
Community Based ◽  
Prototype Implementation ◽  
Data Infrastructure ◽  
Proposed Model ◽  
And Performance ◽  
Giving Feedback ◽  
The Web

Over the past few decades, geoportals have been considered as the key technological solutions for easy access to Earth observation (EO) products, and the implementation of spatial data infrastructure (SDI). However, less attention has been paid to developing an efficient model for crowdsourcing EO products through geoportals. To this end, a new model called the “Open Community-Based Crowdsourcing Geoportal for Earth Observation Products” (OCCGEOP) was proposed in this study. The model was developed based on the concepts of volunteered geographic information (VGI) and community-based geoportals using the latest open technological solutions. The key contribution lies in the conceptualization of the frameworks for automated publishing of standard map services such as the Web Map Service (WMS) and the Web Coverage Service (WCS) from heterogeneous EO products prepared by volunteers as well as the communication portion to request voluntary publication of the map services and giving feedback for quality assessment and assurance. To evaluate the feasibility and performance of the proposed model, a prototype implementation was carried out by conducting a pilot study in Iran. The results showed that the OCCGEOP is compatible with the priorities of the new generations of geoportals, having some unique features and promising performance.

scholarly journals MATCHING ALTERNATIVE ADDRESSES: A SEMANTIC WEB APPROACH

2015 ◽  
Vol XL-1-W5 ◽  
pp. 63-66
Author(s):  
S. Ariannamazi ◽  
F. Karimipour ◽  
F. Hakimpour
Keyword(s):  
Semantic Web ◽  
Information Integration ◽  
Spatial Data ◽  
Rapid Development ◽  
Crowd Sourcing ◽  
Knowledge Modeling ◽  
Data Types ◽  
Geospatial Information ◽  
Multiple Data ◽  
Web Standards

Rapid development of crowd-sourcing or volunteered geographic information (VGI) provides opportunities for authoritatives that deal with geospatial information. Heterogeneity of multiple data sources and inconsistency of data types is a key characteristics of VGI datasets. The expansion of cities resulted in the growing number of POIs in the OpenStreetMap, a well-known VGI source, which causes the datasets to outdate in short periods of time. These changes made to spatial and aspatial attributes of features such as names and addresses might cause confusion or ambiguity in the processes that require feature’s literal information like addressing and geocoding. VGI sources neither will conform specific vocabularies nor will remain in a specific schema for a long period of time. As a result, the integration of VGI sources is crucial and inevitable in order to avoid duplication and the waste of resources. Information integration can be used to match features and qualify different annotation alternatives for disambiguation. This study enhances the search capabilities of geospatial tools with applications able to understand user terminology to pursuit an efficient way for finding desired results. Semantic web is a capable tool for developing technologies that deal with lexical and numerical calculations and estimations. There are a vast amount of literal-spatial data representing the capability of linguistic information in knowledge modeling, but these resources need to be harmonized based on Semantic Web standards. The process of making addresses homogenous generates a helpful tool based on spatial data integration and lexical annotation matching and disambiguating.

scholarly journals SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES

2018 ◽  
Vol XLII-4 ◽  
pp. 639-645 ◽  
Author(s):  
D. Ulutaş Karakol ◽  
G. Kara ◽  
C. Yılmaz ◽  
Ç. Cömert
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Linked Data ◽  
Relational Databases ◽  
Data Sources ◽  
Data Conversion ◽  
Web Data ◽  
Link Discovery ◽  
Rdf Data ◽  
The Web

<p><strong>Abstract.</strong> Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc.</p>

Semantic Web-Based Agricultural Information Integration

2018 ◽  
pp. 86-104
Author(s):  
Kaladevi Ramar ◽  
Geetha Gurunathan
Keyword(s):  
Semantic Web ◽  
Information Integration ◽  
Relevant Information ◽  
Integrated Systems ◽  
Web Based ◽  
Worldwide Web ◽  
Prevention Methods ◽  
Available Information ◽  
Semantic Linkage ◽  
The Web

A huge volume of information is available in the worldwide web. However, the demand is on relevant information rather than available information, which are often heterogeneous and distributed. Agriculture is one such domain, which includes information like soil, crops, weather, etc. under one roof. This information is in different representations and structures (e.g., weather). This scenario leads to a challenge of how to integrate the available and heterogeneous agricultural information to deliver better production. As the information on the web is syntactically structured, the need is to provide semantic linkage. The semantic web supports the existing web to easily process and interpret information. In this chapter, a semantic-based agricultural information system (AIS) is proposed that addresses heterogeneity issues among weather systems and integrates information like soil, weather, crop, and fertilizers. AIS helps the farmers regarding the type of crop/soil, crop/climate, fertilizer applications, diseases, and prevention methods using effective retrieval of information from integrated systems.

Semantic Web Based Agricultural Information Integration

2018 ◽  
pp. 1686-1700
Author(s):  
Kaladevi Ramar ◽  
Geetha Gurunathan
Keyword(s):  
Information System ◽  
Semantic Web ◽  
Information Integration ◽  
Relevant Information ◽  
Integrated Systems ◽  
Web Based ◽  
Prevention Methods ◽  
Available Information ◽  
Semantic Linkage ◽  
The Web

Huge volume of information is available in the WWW. However, the demand is on relevant information rather than available information, which are often heterogeneous and distributed. Agriculture is one such domain, which includes information like soil, crops, weather, etc., under one roof. This information is in different representations and structures e.g. weather. This scenario leads to a challenge that how to integrate the available and heterogeneous agricultural information to deliver better production. The information on the web is syntactically structured but, the need is to provide semantic linkage. The semantic web supports the existing web to easily process and interpret information. In this paper, a semantic based Agricultural Information System (AIS) is proposed which addresses heterogeneity issues among weather systems and integrates information like soil, weather, crop and fertilizers. AIS helps the farmers regarding the type of crop/soil, crop/climate, fertilizer applications, diseases and prevention methods using effective retrieval of information from integrated systems.

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