Efficient and Effective SPARQL Autocompletion
on Very Large Knowledge Graphs

Materials for our CIKM'22 paper


Try our context-sensitive autocompletion on a variety of knowledge graphs

Evaluation web application

Click through our evaluation and explore our results in full detail

Evaluation web app

Code on Github

Note: all the improvements to QLever that are described in the paper have been merged into QLever's master branch over the last months. You can use it to build an index for the complete Wikidata and run the evaluation script to achieve results similar to those in the paper. To reproduce the exact results from the paper, see the next section.

Exact reproducibility materials

The following sections contain the exact version of QLever and the evaluation script that were used for the evaluation of the paper. Note that the binary index files for QLever are only compatible with this version and not with the current Github master, although they yield similar performance.

Evaluation script, queries, AC query templates, and result files

Evaluation Script

Extended version of QLever

We have provided a pre-compiled docker image of the exact version of QLever that was used for the evaluation in the paper and is compatible with the binary index files and the version of the evaluation script.

Docker image of our QLever extensions

Running QLever, Virtuoso and Blazegraph

Machine Requirements

We ran our experiments on a AMD Ryzen 7 3700X CPU (8 cores + SMT), 128 GB of DDR-4 RAM and 4 TB SSD storage (NVME, Raid 0). To roughly reproduce our results you need a similar machine. In particular you need (at least) 128GB of RAM and 3TB of SSD storage (Needed by QLever's Wikidata index). If you only want to run evaluations on the smaller two datasets (Freebase and Fbeasy), 2TB of SSD suffice. Running the Fbeasy evaluations only should also work on a machine with 500GB of SSD and 64GB of RAM. Your machine needs to run Linux and Docker must be installed. (Everything runs inside Docker so the exact Linux version and distribution should not be too important, we used Ubuntu 18.04)

Instructions for Running the Evaluation
How to run Virtuoso
How to run Blazegraph
How to run QLever
How to run the evaluation

Index Files

index files for virtuoso-wikidata
index files for virtuoso-freebase
index files for virtuoso-fbeasy
index files and executables for blazegraph(all KBs)
index files for qlever-wikidata
index files for qlever-freebase
index files for qlever-fbeasy