On-Demand and Lightweight Knowledge Graph Generation – a Demonstration with DBpedia

07/02/2021
by   Malte Brockmeier, et al.
0

Modern large-scale knowledge graphs, such as DBpedia, are datasets which require large computational resources to serve and process. Moreover, they often have longer release cycles, which leads to outdated information in those graphs. In this paper, we present DBpedia on Demand – a system which serves DBpedia resources on demand without the need to materialize and store the entire graph, and which even provides limited querying functionality.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/13/2020

A Dual-Store Structure for Knowledge Graphs

To effectively manage increasing knowledge graphs in various domains, a ...
research
09/16/2020

RDF2Vec Light – A Lightweight Approach for Knowledge Graph Embeddings

Knowledge graph embedding approaches represent nodes and edges of graphs...
research
01/04/2020

Quantum Machine Learning Algorithm for Knowledge Graphs

Semantic knowledge graphs are large-scale triple-oriented databases for ...
research
03/28/2022

AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs

Large-scale knowledge graphs are increasingly common in many domains. Th...
research
08/18/2023

Semantic relatedness in DBpedia: A comparative and experimental assessment

Evaluating semantic relatedness of Web resources is still an open challe...
research
09/07/2023

PyGraft: Configurable Generation of Schemas and Knowledge Graphs at Your Fingertips

Knowledge graphs (KGs) have emerged as a prominent data representation a...
research
11/28/2015

Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is ess...

Please sign up or login with your details

Forgot password? Click here to reset