Name
Abstract | ||
|---|---|---|
A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-68204-4_15 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data science,Distributed Computing Environment,Computer science,Exploit,Semantic analytics,Knowledge base,Analytics,RDF,Software framework,Database,Scalability | Conference | 10588 |
ISSN | Citations | PageRank |
0302-9743 | 11 | 0.86 |
References | Authors | |
20 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jens Lehmann | 1 | 5375 | 355.08 |
| Gezim Sejdiu | 2 | 16 | 3.33 |
| Lorenz Bühmann | 3 | 603 | 31.20 |
| Patrick Westphal | 4 | 132 | 7.98 |
| Claus Stadler | 5 | 363 | 26.65 |
| Ivan Ermilov | 6 | 98 | 11.27 |
| Simon Bin | 7 | 14 | 2.31 |
| Nilesh Chakraborty | 8 | 22 | 8.33 |
| Muhammad Saleem | 9 | 194 | 21.78 |
| Axel-Cyrille Ngonga Ngomo | 10 | 1775 | 139.40 |
| Hajira Jabeen | 11 | 67 | 10.58 |