A Resource Space Model is a semantic model to organize, locate and operate Web resources in a multi-dimensional resource space. It's easy for users to understand the resource space and locate resources in it because a resource space is constructed based on the classification semantics. In general, resource spaces models are manually designed and constructed based on domain knowledge and resource analysis. Human factors, such as personal opinions, knowledge level and design skill, will influence the design result of a resource space. To reduce the difficulties of the manual design and ease the designing process, this work studies the issues of automated creation of a resource space and proposes a general method to automatically construct resource spaces from XML files.
{"title":"Automatic Construction of RSM Based on XML","authors":"Lei He","doi":"10.1109/SKG.2011.45","DOIUrl":"https://doi.org/10.1109/SKG.2011.45","url":null,"abstract":"A Resource Space Model is a semantic model to organize, locate and operate Web resources in a multi-dimensional resource space. It's easy for users to understand the resource space and locate resources in it because a resource space is constructed based on the classification semantics. In general, resource spaces models are manually designed and constructed based on domain knowledge and resource analysis. Human factors, such as personal opinions, knowledge level and design skill, will influence the design result of a resource space. To reduce the difficulties of the manual design and ease the designing process, this work studies the issues of automated creation of a resource space and proposes a general method to automatically construct resource spaces from XML files.","PeriodicalId":184788,"journal":{"name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134280958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With increment of personal data amount, how to allow users to efficiently re-find personal data items becomes an important research issue. According to general experience of persons, task is taken as a popular way to classify personal dataset, and is often taken as a factor to re-access expected items. In this paper, we propose a framework called TaskSpace to help users re-find expected data items based on user task, and present conceptual model of TaskSpace, framework for implementing a task-based system, methods to identify task relationships and interface for users to perform task-based query. TaskSpace framework provides users an alternative way to re-find personal information, and illustrates some interesting research issues.
{"title":"A Framework towards Task-Based Query in Personal DataSpace","authors":"Yukun Li","doi":"10.1109/SKG.2011.25","DOIUrl":"https://doi.org/10.1109/SKG.2011.25","url":null,"abstract":"With increment of personal data amount, how to allow users to efficiently re-find personal data items becomes an important research issue. According to general experience of persons, task is taken as a popular way to classify personal dataset, and is often taken as a factor to re-access expected items. In this paper, we propose a framework called TaskSpace to help users re-find expected data items based on user task, and present conceptual model of TaskSpace, framework for implementing a task-based system, methods to identify task relationships and interface for users to perform task-based query. TaskSpace framework provides users an alternative way to re-find personal information, and illustrates some interesting research issues.","PeriodicalId":184788,"journal":{"name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132653109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhangbing Zhou, M. Sellami, Walid Gaaloul, Bruno Defude
In service-oriented computing, a user usually needs to locate a desired service for (i) fulfilling her requirements, or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the internet, locating a service online may not be appropriate from the performance perspective, especially in large internet-based service repositories. Instead, services usually need to be clustered offline according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly.
{"title":"Clustering and Managing Data Providing Services Using Machine Learning Technique","authors":"Zhangbing Zhou, M. Sellami, Walid Gaaloul, Bruno Defude","doi":"10.1109/SKG.2011.9","DOIUrl":"https://doi.org/10.1109/SKG.2011.9","url":null,"abstract":"In service-oriented computing, a user usually needs to locate a desired service for (i) fulfilling her requirements, or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the internet, locating a service online may not be appropriate from the performance perspective, especially in large internet-based service repositories. Instead, services usually need to be clustered offline according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly.","PeriodicalId":184788,"journal":{"name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Wang, Zhisheng Huang, Yi Zeng, N. Zhong, F. V. Harmelen
When the physical space and the cyber space are linked by human, Cyber-Physical Society (CPS) has emerged and produced many challenges. Among which, the challenge of fast growing data and knowledge both from the physical space and the cyber space has become a crucial issue. Scalability becomes a big barrier in data processing (more specifically, search and reasoning). Traditional knowledge processing methods aim at providing users complete results in rational time, which is not applicable when it comes to very large-scale data. While in the context of Web and large-scale data, users' expectations are not always receiving complete results, instead, they may prefer to get some incomplete subset of the results compared to waiting for a long time. With this spirit, an approach named Interleaving Reasoning and Selection with Knowledge Summarization (IRSKS) is developed. This approach supports incomplete reasoning and heuristic search based on knowledge summarization. It can be divided into two phases: the off-line and the on-line processing. The off-line processing includes partitioning and summarization, and provides the basis for heuristic search. Partitioning makes one large-scale dataset become many small subsets (chunks). Summarization produces summaries that contain heuristic information (such as the location and major topics of each chunk) to build a bridge between the searching target and the partitioned large-scale dataset. Through the cues provided by summaries, a best search path can be found to locate the searching target. Along with the search path, the on-line processing includes interleaving reasoning and selection, which compose a dynamic searching process and support anytime behavior. In this way, the search space is greatly reduced and close to the searching target so that a good trade-off is achieved between the time and the quality of a query. Based on this approach, a prototype system named Knowledge Intensive Summarization System (KISS) has been developed and the evaluation with the KISS system on the PubMed dataset indicates that the proposed method is potentially effective for processing large-scale semantic data in the Cyber-Physical Society.
{"title":"Interleaving Reasoning and Selection with Knowledge Summarization","authors":"Yan Wang, Zhisheng Huang, Yi Zeng, N. Zhong, F. V. Harmelen","doi":"10.1109/SKG.2011.42","DOIUrl":"https://doi.org/10.1109/SKG.2011.42","url":null,"abstract":"When the physical space and the cyber space are linked by human, Cyber-Physical Society (CPS) has emerged and produced many challenges. Among which, the challenge of fast growing data and knowledge both from the physical space and the cyber space has become a crucial issue. Scalability becomes a big barrier in data processing (more specifically, search and reasoning). Traditional knowledge processing methods aim at providing users complete results in rational time, which is not applicable when it comes to very large-scale data. While in the context of Web and large-scale data, users' expectations are not always receiving complete results, instead, they may prefer to get some incomplete subset of the results compared to waiting for a long time. With this spirit, an approach named Interleaving Reasoning and Selection with Knowledge Summarization (IRSKS) is developed. This approach supports incomplete reasoning and heuristic search based on knowledge summarization. It can be divided into two phases: the off-line and the on-line processing. The off-line processing includes partitioning and summarization, and provides the basis for heuristic search. Partitioning makes one large-scale dataset become many small subsets (chunks). Summarization produces summaries that contain heuristic information (such as the location and major topics of each chunk) to build a bridge between the searching target and the partitioned large-scale dataset. Through the cues provided by summaries, a best search path can be found to locate the searching target. Along with the search path, the on-line processing includes interleaving reasoning and selection, which compose a dynamic searching process and support anytime behavior. In this way, the search space is greatly reduced and close to the searching target so that a good trade-off is achieved between the time and the quality of a query. Based on this approach, a prototype system named Knowledge Intensive Summarization System (KISS) has been developed and the evaluation with the KISS system on the PubMed dataset indicates that the proposed method is potentially effective for processing large-scale semantic data in the Cyber-Physical Society.","PeriodicalId":184788,"journal":{"name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A key challenge of Web Service (WS) composition is how to ensure reliable execution. Due to their inherent autonomy and heterogeneity, it is difficult to reason about the behavior of service compositions especially in case of failures. Therefore, there is a growing interest for verification techniques which help to prevent service composition execution failures. In this paper, we present a proof and refinement based approach for the formal representation, verification and validation of Web Services transactional compositions using the Event-B method.
{"title":"Verifying Composite Service Transactional Behavior with EVENT-B","authors":"Lazhar Hamel, Mohamed Graiet, Mourad Kmimech, Mohamed Tahar Bhiri, Walid Gaaloul","doi":"10.1109/SKG.2011.35","DOIUrl":"https://doi.org/10.1109/SKG.2011.35","url":null,"abstract":"A key challenge of Web Service (WS) composition is how to ensure reliable execution. Due to their inherent autonomy and heterogeneity, it is difficult to reason about the behavior of service compositions especially in case of failures. Therefore, there is a growing interest for verification techniques which help to prevent service composition execution failures. In this paper, we present a proof and refinement based approach for the formal representation, verification and validation of Web Services transactional compositions using the Event-B method.","PeriodicalId":184788,"journal":{"name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134494188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：