Name
Title | ||
|---|---|---|
Building the Multi-layer Theory of Association Semantic based on the Power-law Distribution of Linking Keywords. |
Abstract | ||
|---|---|---|
Web information contain plentiful, significant knowledge which is eager to be explored by users. Effective semantic layered technology not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of the related information system. This paper builds the multi-layer theory of association semantic based on the power-law distribution of linking keywords. First, some experiments of four types of keywords with different linking role are done to discover the possible distribution law. Experiment results show that four types of keywords are all reveal power-law distribution. Then, based on the discovered power-law distribution, the multi-layer theory of association semantic is built. The multi-layer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle size on Association Link Network (ALN). KeywordsAssociation Link Network, power-law distribution, multi-layer theory of association semantic, knowledge discovery in Web resources. |
Year | Venue | Field |
|---|---|---|
2016 | SEKE | Information system,Web resource,Data mining,Multi layer,Pareto distribution,Information retrieval,Computer science,Knowledge extraction,Distribution law,Web information |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangli Zhu | 1 | 0 | 0.68 |
| Xiaojun Tang | 2 | 26 | 5.80 |
| Shunxiang Zhang | 3 | 125 | 18.93 |
| Zheng Xu | 4 | 31 | 7.18 |