Name
Abstract | ||
|---|---|---|
Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals. To this aim, we use specific suitable fragments from the Datalog(^pm ) family of languages, and we introduce the vadalog system, which puts these swift logics into action. This system exploits the theoretical underpinning of relevant Datalog(^pm ) languages and combines it with existing and novel techniques from database and AI practice. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-73117-9_1 | IJCAI |
Field | DocType | Citations |
Data science,Discrete mathematics,Computer science,Theoretical computer science,Exploit,Analytics,Datalog,Rotation formalisms in three dimensions,Management system,Big data,Scalability,Computational complexity theory | Conference | 5 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luigi Bellomarini | 1 | 46 | 13.11 |
| Georg Gottlob | 2 | 9594 | 1103.48 |
| Andreas Pieris | 3 | 1177 | 64.25 |
| Emanuel Sallinger | 4 | 71 | 20.76 |