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
The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly ...The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.展开更多
Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization.Typically,it is difficult to find the desired data from the large amount of datasets effic...Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization.Typically,it is difficult to find the desired data from the large amount of datasets efficiently and effectively.Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning,and they are either limited in data access rate or do not take the time cost into account.In this paper,we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies,which is referred to as Data Ontology and List-Based Publishing(DOLP).Specifically,we mainly improve the ocean data services in the following three aspects.First,we propose a unified semantic model called OEDO(Ocean Environmental Data Ontology)to represent heterogeneous ocean data by metadata and to be published as data services.Second,we propose an optimized quick service query list(QSQL)data structure for storing the pre-inferred semantically related services,and reducing the service querying time.Third,we propose two algorithms for optimizing QSQL hierarchically and horizontally,respectively,which aim to extend the semantics relationships of the data service and improve the data access rate.Experimental results prove that DOLP outperforms the benchmark methods.First,our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method,and are faster than the traditional semantic method based on direct reasoning.Second,DOLP can handle more complex semantic relationships than the existing methods.展开更多
在使用服务数据对象SDO(Service Data Objects)过程中,针对SDO连接异构数据源存在的效率和一致性问题,提出一种面向文法和自动机的推理方法,同时,引入本体进行语义描述数据交换,增强SDO语义识别,并通过数据中介服务(DMS)建立一种异构数...在使用服务数据对象SDO(Service Data Objects)过程中,针对SDO连接异构数据源存在的效率和一致性问题,提出一种面向文法和自动机的推理方法,同时,引入本体进行语义描述数据交换,增强SDO语义识别,并通过数据中介服务(DMS)建立一种异构数据源访问数据图的图结构,根据数据图的需求变更以及数据访问服务(DAS)实现行图裁剪成一棵棵数据对象最优树,建立相应的优化数学模型,并通过蚁群算法优化寻优该模型。最后通过在开源件SDOAPI中引入策略应用表示:提高了不同的数据源访问效率,增强了数据交换的一致性和语义性。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The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.
基金supported by the National Key Research and Development Program of China under Grant No.2018YFB0203801the National Natural Science Foundation of China under Grant Nos.61702529 and 61802424.
文摘Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization.Typically,it is difficult to find the desired data from the large amount of datasets efficiently and effectively.Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning,and they are either limited in data access rate or do not take the time cost into account.In this paper,we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies,which is referred to as Data Ontology and List-Based Publishing(DOLP).Specifically,we mainly improve the ocean data services in the following three aspects.First,we propose a unified semantic model called OEDO(Ocean Environmental Data Ontology)to represent heterogeneous ocean data by metadata and to be published as data services.Second,we propose an optimized quick service query list(QSQL)data structure for storing the pre-inferred semantically related services,and reducing the service querying time.Third,we propose two algorithms for optimizing QSQL hierarchically and horizontally,respectively,which aim to extend the semantics relationships of the data service and improve the data access rate.Experimental results prove that DOLP outperforms the benchmark methods.First,our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method,and are faster than the traditional semantic method based on direct reasoning.Second,DOLP can handle more complex semantic relationships than the existing methods.
文摘在使用服务数据对象SDO(Service Data Objects)过程中,针对SDO连接异构数据源存在的效率和一致性问题,提出一种面向文法和自动机的推理方法,同时,引入本体进行语义描述数据交换,增强SDO语义识别,并通过数据中介服务(DMS)建立一种异构数据源访问数据图的图结构,根据数据图的需求变更以及数据访问服务(DAS)实现行图裁剪成一棵棵数据对象最优树,建立相应的优化数学模型,并通过蚁群算法优化寻优该模型。最后通过在开源件SDOAPI中引入策略应用表示:提高了不同的数据源访问效率,增强了数据交换的一致性和语义性。
