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Before computing the average minimum path length and the clustering coefficient, O-A SSOCIATION LINK NETWORK should be changed to an undirected graph because the average minimum path length and the clustering coefficients are usually used to undirected graph

Building Association Link Network for Semantic Link on Web Resources

IEEE T. Automation Science and Engineering, no. 3 (2011): 482-494

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摘要

Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World Wide Web to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery, and publishing, etc. Since existing methods for build...更多

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简介
  • A SSOCIATION LINK NETWORK (ALN) is a kind of semantic link network [30], [31], which is designed to establish associated relations among various resources (e.g., Web pages or documents in digital library) aiming at extending the loosely connected network of no semantics to an association-rich network.
  • Based on the discovered characteristics, connection-rich ALN is proposed to organize the associated resources more appropriate for supporting the Web intelligent activities.
  • This feature is in favor of the Web intelligent activities because the C-ALN has the appropriate browsing paths and the associated weight between resources.
重点内容
  • A SSOCIATION LINK NETWORK (ALN) is a kind of semantic link network [30], [31], which is designed to establish associated relations among various resources (e.g., Web pages or documents in digital library) aiming at extending the loosely connected network of no semantics to an association-rich network
  • Before computing the average minimum path length and the clustering coefficient, O-A SSOCIATION LINK NETWORK should be changed to an undirected graph because the average minimum path length and the clustering coefficients are usually used to undirected graph
  • Shows the results of the average minimum path length and the clustering coefficient of O-A SSOCIATION LINK NETWORK generated by the four data sets
  • From Table VIII, O-A SSOCIATION LINK NETWORK is approximate to a small world network because the average minimum path length is a little longer and the clustering coefficient is higher than the random network with same scale
  • 2) The weight of the links in the C-A SSOCIATION LINK NETWORK is appropriate for Web intelligent activities, which reflects the associated relation between resources
  • The number of nodes linking to its associated resources in Fig. 5 is 121, which means 82.3% of the sections link to their same papers’ section. Given these sections are more associated because their papers are from one journal; the accuracy up to 82.3% is high enough to answer the question: the association links of the resources are able to reflect the real associated resources
  • Since Element Fuzzy Cognitive Map have a good capability to represent Web resource with rich semantics and can be understood by machines we use Element Fuzzy Cognitive Map to represent the functions of web services and build A SSOCIATION LINK NETWORK to organize those Web services based on the similar and the associated relation between the resources of Web services
结果
  • 2) The weight of the links in the C-ALN is appropriate for Web intelligent activities, which reflects the associated relation between resources.
  • This feature is important for Web intelligent activities since the high weight links can reflect the associated relation between resources.
  • ALN is based on semantics of each resource instead of each user profile to support Web intelligent activities.
  • The number of the nodes whose highest weight link to the section of same paper in Fig. 6 is 127, which means that 87.0% of the resources with high weight association link is more associated content than those with low weight links.
  • This is not enough to evaluate the association link between resources since ALN is a weighted network.
  • The challenge of the incremental ALN is how to perform the association weight of the new coming Web resources efficiently and exactly.
  • Given an existing ALN and some new coming resources, the incremental building means to avoid computing association weight for all pairs of nodes in the existing
  • Given the huge number of resources in the WWW, it is unrealistic to compute the association weights between the new coming Web resources and each node in the existing ALN, respectively.
  • Since E-FCM have a good capability to represent Web resource with rich semantics and can be understood by machines the authors use E-FCM to represent the functions of web services and build ALN to organize those Web services based on the similar and the associated relation between the resources of Web services.
结论
  • 2) ALN which uses associated relation based on link is proposed to formalize into a loosely coupled semantic model for managing various Web resources, which extends the hyperlink network to an association-rich network for effective Web intelligent activities.
  • Based on the characteristics of the three states of ALN, the connection-rich ALN is proposed to organize the associated resources more appropriately for users’ Web intelligent activities.
  • ALN is feasible and has a brilliant perspective in the applications of Web intelligent activities, which will be an effective and efficient tool for the semantic links on the resources in digital library and Web environment.
表格
  • Table1: THE STORE MODEL OF E-FCM1 TO E-FCM7
  • Table2: THE ASSOCIATION WEIGHTS AMONG THE SEVEN RESOURCES
  • Table3: THE NUMBER OF THE LINKS IN THE O-ALNS
  • Table4: For example, the weight of element (17, 2) is equal to 0.6 meaning that the confidence of is 0.6. THE PRIOR KNOWLEDGE MATRIX OF THE SEVEN RESOURCES REPRESENTED BY
  • Table5: THE RESULTS OF AWT (I, J) BETWEEN THE SEVEN RESOURCES
  • Table6: THE AVERAGE WEIGHT OF THE O-ALNS
  • Table7: THE DETAILS OF THE DATA SETS TO GENERATE O-ALN
  • Table8: P THE RESULTS OF (k) AND P (k) OF K-ALN
  • Table9: THE AVERAGE MINIMUM PATH LENGTHS AND THE
  • Table10: SOME COMPLEX SYSTEM STATISTICAL PROPERTIES
  • Table11: THE SUM OF THE ALL POSSIBLE ASSOCIATED RELATIONS OF RESOURCE 1 REPRESENTED BY
  • Table12: P THE RESULTS OF (k); P k( ) OF O-ALN IN THE FOUR DATA SETS
  • Table13: THE AVERAGE MINIMUM PATH LENGTHS AND
  • Table14: THE THRESHOLD AND THE NUMBER OF ADDED LINKS
  • Table15: THE SPACE SIZE OF WEB PAGES IN THE FOUR DATA SETS
  • Table16: THE AVERAGE WEIGHT OF THE C-ALNS
Download tables as Excel
基金
  • This work was supported in part by the Shanghai Science and Technology Commission under Grant 09JC1406200, in part by the National Science Foundation of China under Grant 91024012, Grant 61071110, Grant 90818004, Grant 90612010, and in part by the Shanghai Leading Academic Discipline Project (J50103)
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