Paper Info
Title
Multi-relational PageRank for Tree Structure Sense Ranking.
Abstract
In this paper, we study the problem of structural sense ranking for tree data using a multi-relational PageRank approach. By considering multiple types of structural relations, the original tree structural context is better leveraged and hence used to improve the ranking of the senses associated to the tree elements. Upon this intuition, we advance research on the application of PageRank-style methods to semantic graphs inferred from semistructured/plain text data by developing the first PageRank-based formulations that exploit heterogeneity of links to address the problem of structural sense ranking in tree data. Experiments on a large real-world benchmark have confirmed the performance improvement hypothesis of our proposed multi-relational approach.
Year
DOI
Venue
2013
10.1007/s11280-014-0302-4
World Wide Web
Keywords
Field
DocType
Tree-structured data,structural sense ranking,heterogeneous information networks
Data mining,Ranking SVM,Computer science,Theoretical computer science,Tree structure,Artificial intelligence,PageRank,Ranking,Exploit,Plain text,Machine learning,Performance improvement,Incremental decision tree
Conference
Volume
Issue
ISSN
18
5
1386-145X
Citations 
PageRank 
References 
0
0.34
29
Authors
2
Name
Order
Citations
PageRank
Roberto Interdonato17012.42
Andrea Tagarelli247552.29