Name
Abstract | ||
|---|---|---|
Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11749-2_25 | WEB INFORMATION SYSTEMS ENGINEERING - WISE 2014, PT I |
Keywords | Field | DocType |
Linked Data, data interlinking, recommender systems, Bayesian classifier, social networks | Recommender system,Data mining,Social network,Naive Bayes classifier,Computer science,Social network analysis,Linked data,Artificial intelligence,Machine learning,Exploratory search,Bayesian probability | Conference |
Volume | ISSN | Citations |
8786 | 0302-9743 | 5 |
PageRank | References | Authors |
0.51 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giseli Rabello Lopes | 1 | 107 | 16.44 |
| Luiz André P. Paes Leme | 2 | 90 | 13.81 |
| Bernardo Pereira Nunes | 3 | 185 | 30.96 |
| Marco Antonio Casanova | 4 | 178 | 24.18 |
| Stefan Dietze | 5 | 597 | 68.07 |