An evaluation of models for runtime approximation in link discovery

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime of the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 500 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.