Name
Abstract | ||
|---|---|---|
Many ranking methods have been proposed for RDF data. These methods often use the structure behind the data to measure its importance. Recently, some of these methods have started to explore information from other sources such as the Wikipedia page graph for better ranking RDF data. In this work, we propose DBtrends, a ranking function based on query logs. We extensively evaluate the application of different ranking functions for entities, classes, and properties across two different countries as well as their combination. Thereafter, we propose MIXED-RANK, a ranking function that combines DBtrends with the best-evaluated entity ranking function. We show that: (i) MIXED-RANK outperforms state-of-the-art entity ranking functions, and; (ii) query logs can be used to improve RDF ranking functions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2993318.2993322 | SEMANTICS |
Field | DocType | Citations |
Data mining,Graph,Semantic annotation,Information retrieval,Ranking SVM,Ranking,Computer science,Ranking (information retrieval),RDF | Conference | 2 |
PageRank | References | Authors |
0.39 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edgard Marx | 1 | 139 | 14.31 |
| Amrapali Zaveri | 2 | 368 | 24.37 |
| Diego Moussallem | 3 | 45 | 13.94 |
| Sandro Rautenberg | 4 | 6 | 2.91 |