The schema theory for semantic link network

Zhuge, Hai; Sun, Yunchuan

doi:10.1016/j.future.2009.08.012

Cited by 48 publications

(3 citation statements)

References 10 publications

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“…For an ordered pair of resources 〈R, R′〉, types of semantic relations depend on their resource types rt and rt′, where each resource type is the representation of a set of resources with one or more properties. Between two resource types rt and rt′, there might be a list of semantic relations (Zhuge & Sun, 2010).…”

Section: Computing Theory For Semantic Relationsmentioning

confidence: 99%

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Zhang

Sun

Bie

2016

International Journal of Data Warehousing and Mining

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Measuring graph similarity is a primary issue for graph-related applications. Many works have been proposed on simple topology-based structural similarity measuring for networks. It is not enough for semantic-rich networks like semantic networks, semantic link networks, and event-linked networks where semantic-based structural similarity measuring is more important than topology-based structure similarity measuring. In this paper, the authors introduce a semantic-based structural similarity for the first time and then propose an approach to measure the semantic-based structural similarity between networks with the computing theory for semantic relations as the foundation. A case study in semantic link network of the scientific research is also presented to show the feasibility of the proposed approach.

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Section: Computing Theory For Semantic Relationsmentioning

confidence: 99%

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Zhang

Sun

Bie

2016

International Journal of Data Warehousing and Mining

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“…The main characteristic for these technologies is to represent all elements by adding some marked label to annotate corresponding meaning [18]. Semantic link network is proposed as a semantic data model for managing web resources, in which nodes can be any type of resources and edges can be any semantic relation [19–22]. The schema theory provides the basis for normalized management of semantic link network [19].…”

Section: Introductionmentioning

confidence: 99%

Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Sun

Yan

Zhang

et al. 2014

International Journal of Distributed Sensor Networks

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Massive sensing data are generated continuously in the Internet of Things. How to organize and how to query the big sensing data are big challenges for intelligent applications. This paper studies the organization of big sensing data with event-linked network (ELN) model, where events are regarded as primary units for organizing data and links are used to represent the semantic associations among events. Several different types of queries on the event-linked network are also explored, which are different from queries on traditional relational database. We use an instance of smart home to show the effectiveness and efficiency of organization and query approaches based on the event-linked network.

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“…Its edges can be any semantic relations. A semantic link network instance is a directed graph, denoted as S (ResourceSet, LinkSet), where S is the name of the semantic link network, ResourceSet is a set of resources, and LinkSet is a set of semantic links in the form of ' R R    , where R ,  ' R ResourceSet, and  is a semantic factor representing a semantic relation between R and ' R [1]. S-SLN is a special instance of SLN where nodes are Web services and arcs are service relationship links to describe inherent semantic association among Web services.…”

Section: Introductionmentioning

confidence: 99%

Constructing Service Semantic Link Network Based on the Probabilistic Graphical Model

Zhao¹,

Ma²

2012

IJCIS

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Automatic services collaboration calls for the development of semantically structured service network to maximize the utility of Web services. Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by the dependency relationship between services. We provided an effective method for constructing S-SLN based on the graphical structure representation of the dependencies embedded in a probabilistic model. A Markov network is an undirected graph whose links represents probability dependencies. We first learned Markov network structure from Web services data, and then transformed the undirected Markov network structure into a directed graph structure of S-SLN based on the same joint probability distribution. Finally, experimental results show the effectiveness of the method.

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The schema theory for semantic link network

Cited by 48 publications

References 10 publications

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Constructing Service Semantic Link Network Based on the Probabilistic Graphical Model

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