Deep web data integration needs to do schema matching on web query interfaces and obtain the mapping table.By introducing semantic conflicts into web query interface integration and discussing the origins and categori...Deep web data integration needs to do schema matching on web query interfaces and obtain the mapping table.By introducing semantic conflicts into web query interface integration and discussing the origins and categories of the semantic conflicts,an ontology-based schema matching method is proposed.The process of the method is explained in detail using the example of web query interface integration in house domain.Conflicts can be detected automatically by checking semantic relevance degree,then the categories of the conflicts are identified and messages are sent to the conflict solver,which eliminates the conflicts and obtains the mapping table using conflict solving rules.The proposed method is simple,easy to implement and can be flexibly reused by extending the ontology to different domains.展开更多
Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due t...Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization.Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics,especially metadata semantics.The metadata-based and the instance-based matching methods,in the case of being employed individually,are not efficient to determine the concept relationships,which are crucial for finding high-quality matches between schema attributes.In this paper,we propose an improved schema matching method,based on the combination of instance and metadata(CIM)matcher.The main method of our approach is to utilize schema structure,element labels,and the corresponding instance data information.The matching process is divided into two phases.In the first phase,the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema,and the generated matching results are raw mappings,which will be reused in the next instance matching phase.In the second phase,the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated.Finally,the raw mappings and the fine mappings are combined,and the final mappings are obtained.Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver.The results indicate that,the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.展开更多
Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables...Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables qualitative improvement of the stored data.This study aimed to enhance the applicability of XML Schema matching,which improves the speed and quality of information stored in bridge SCDs.First,the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs.The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations.Second,the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree.The decision tree model was built using the content elements stored in the SCD,design companies,bridge types,and weight factors as input variables,and the matching accuracy as the target variable.The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.展开更多
Human-centric service is an important domain in smart city and includes rich applications that help residents with shopping, dining, transportation, entertainment, and other daily activities. These applications have g...Human-centric service is an important domain in smart city and includes rich applications that help residents with shopping, dining, transportation, entertainment, and other daily activities. These applications have generated a massive amount of hierarchical data with different schemas. In order to manage and analyze the city-wide and cross-application data in a unified way, data schema integration is necessary. However, data from human-centric services has some distinct characteristics, such as lack of support for semantic, matching, large number of schemas, and incompleteness of schema element labels. These make the schema integra- tion difficult using existing approaches. We propose a novel framework for the data schema integration of the human-centric services in smart city. The framework uses both schema metadata and instance data to do schema matching, and introduces human intervention based on a similarity entropy criteria to balance precision and efficiency. Moreover, the framework works in an incremental manner to reduce computation workload. We conduct an experiment with real-world dataset collected from multiple estate sale application systems. The results show that our approach can produce high-quality mediated schema with relatively less human in- terventions compared to the baseline method.展开更多
How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learnin...How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learning of labeled samples and unlabeled database records in order to reduce the dependence on tediously hand-labeled training data. The pro- posed model was used to solve the problem of schema matching between data source schema and database schema. Experimental results using a large number of Web pages from diverse domains show the novel approach＇s effectiveness.展开更多
Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitor...Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.展开更多
Matching query interfaces is a crucial step in data integration across multiple Web databases. Different types of information about query interface schemas have been used to match attributes between schemas. Relying o...Matching query interfaces is a crucial step in data integration across multiple Web databases. Different types of information about query interface schemas have been used to match attributes between schemas. Relying on a single aspect of information is not sufficient and the matching results of individual matchers are often inaccurate and uncertain. The evidence theory is the state-of-the-art approach for combining multiple sources of uncertain information. However, traditional evidence theory has the limitations of treating individual matchers in different matching tasks equally for query interface matching, which reduces matching performance. This paper proposes a novel query interface matching approach based on extended evidence theory for Deep Web. Our approach firstly introduces the dynamic prediction procedure of different matchers＇ credibilities. Then, it extends traditional evidence theory with the credibilities and uses exponentially weighted evidence theory to combine the results of multiple matchers. Finally, it performs matching decision in terms of some heuristics to obtain the final matches. Our approach overcomes the shortage of traditional method and can adapt to different matching tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.展开更多
基金The National Natural Science Foundation of China(No.60673130)the Natural Science Foundation of Shandong Province(No.Y2006G29,Y2007G24,Y2007G38)the Encouragement Fund for Young Scholars of Shandong Province(No.2005BS01002)
文摘Deep web data integration needs to do schema matching on web query interfaces and obtain the mapping table.By introducing semantic conflicts into web query interface integration and discussing the origins and categories of the semantic conflicts,an ontology-based schema matching method is proposed.The process of the method is explained in detail using the example of web query interface integration in house domain.Conflicts can be detected automatically by checking semantic relevance degree,then the categories of the conflicts are identified and messages are sent to the conflict solver,which eliminates the conflicts and obtains the mapping table using conflict solving rules.The proposed method is simple,easy to implement and can be flexibly reused by extending the ontology to different domains.
基金This work was supported by the National Natural Science Foundation of China[grant number 41201393]the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing of Wuhan University[grant number 14I03].
文摘Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization.Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics,especially metadata semantics.The metadata-based and the instance-based matching methods,in the case of being employed individually,are not efficient to determine the concept relationships,which are crucial for finding high-quality matches between schema attributes.In this paper,we propose an improved schema matching method,based on the combination of instance and metadata(CIM)matcher.The main method of our approach is to utilize schema structure,element labels,and the corresponding instance data information.The matching process is divided into two phases.In the first phase,the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema,and the generated matching results are raw mappings,which will be reused in the next instance matching phase.In the second phase,the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated.Finally,the raw mappings and the fine mappings are combined,and the final mappings are obtained.Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver.The results indicate that,the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2016R1A6A3A11934917).
文摘Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables qualitative improvement of the stored data.This study aimed to enhance the applicability of XML Schema matching,which improves the speed and quality of information stored in bridge SCDs.First,the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs.The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations.Second,the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree.The decision tree model was built using the content elements stored in the SCD,design companies,bridge types,and weight factors as input variables,and the matching accuracy as the target variable.The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.
基金funded by the National High Technology Research and Development Program of China(863)under Grant No.2013AA01A605
文摘Human-centric service is an important domain in smart city and includes rich applications that help residents with shopping, dining, transportation, entertainment, and other daily activities. These applications have generated a massive amount of hierarchical data with different schemas. In order to manage and analyze the city-wide and cross-application data in a unified way, data schema integration is necessary. However, data from human-centric services has some distinct characteristics, such as lack of support for semantic, matching, large number of schemas, and incompleteness of schema element labels. These make the schema integra- tion difficult using existing approaches. We propose a novel framework for the data schema integration of the human-centric services in smart city. The framework uses both schema metadata and instance data to do schema matching, and introduces human intervention based on a similarity entropy criteria to balance precision and efficiency. Moreover, the framework works in an incremental manner to reduce computation workload. We conduct an experiment with real-world dataset collected from multiple estate sale application systems. The results show that our approach can produce high-quality mediated schema with relatively less human in- terventions compared to the baseline method.
基金Supported by the National Defense Pre-ResearchFoundation of China(4110105018)
文摘How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learning of labeled samples and unlabeled database records in order to reduce the dependence on tediously hand-labeled training data. The pro- posed model was used to solve the problem of schema matching between data source schema and database schema. Experimental results using a large number of Web pages from diverse domains show the novel approach＇s effectiveness.
基金Projects(2007AA01Z126, 2007AA01Z474) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0928) supported by the Program for New Century Excellent Talents in University
文摘Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.
基金Supported by the National Natural Science Foundation of China under Grant No. 90818001the Natural Science Foundation of Shandong Province of China under Grant No. Y2007G24
文摘Matching query interfaces is a crucial step in data integration across multiple Web databases. Different types of information about query interface schemas have been used to match attributes between schemas. Relying on a single aspect of information is not sufficient and the matching results of individual matchers are often inaccurate and uncertain. The evidence theory is the state-of-the-art approach for combining multiple sources of uncertain information. However, traditional evidence theory has the limitations of treating individual matchers in different matching tasks equally for query interface matching, which reduces matching performance. This paper proposes a novel query interface matching approach based on extended evidence theory for Deep Web. Our approach firstly introduces the dynamic prediction procedure of different matchers＇ credibilities. Then, it extends traditional evidence theory with the credibilities and uses exponentially weighted evidence theory to combine the results of multiple matchers. Finally, it performs matching decision in terms of some heuristics to obtain the final matches. Our approach overcomes the shortage of traditional method and can adapt to different matching tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.
