The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information...The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.展开更多
Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,an...Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.展开更多
Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one ...Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.展开更多
While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisua...While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.展开更多
Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Des...Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Despite this lack of application,agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization(FAO).This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems,by making available a self contained reference that provides:a comprehensive review of preexisting semantic resources and their construction methods,data interchange standards,as well as a survey of the current applications of semantic web technologies.展开更多
基金This work is financially supported by the Ministry of Earth Science(MoES),Government of India,(Grant.No.MoES/36/OOIS/Extra/45/2015),URL:https://www.moes.gov.in。
文摘The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
文摘Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.
基金This work was supported by China Scholarship Council and Lund University.
文摘Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.
基金financed by the French National Research Agency within the project ‘Heterogeneous data integration and spatial reasoning for locating victims in mountain areas–CHOUCAS’[ANR-16-CE23-0018].
文摘While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.
基金The research in this review was supported by FAPESP grants number 16/15524-3,15/14228-9,and CNPq grant 302645/2015-2.The authors would also like to thank the anonymous referees for their advice and criticisms.
文摘Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Despite this lack of application,agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization(FAO).This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems,by making available a self contained reference that provides:a comprehensive review of preexisting semantic resources and their construction methods,data interchange standards,as well as a survey of the current applications of semantic web technologies.
