Perplexity of Index Models over Evolving Linked Data

In this paper we analyse the sensitivity of twelve prototypical Linked Data index models towards evolving data. Thus, we consider the reliability and accuracy of results obtained from an index in scenarios where the original data has changed after having been indexed. Our analysis is based on empirical observations over real world data covering a time span of more than one year. The quality of the index models is evaluated w.r.t. their ability to give reliable estimations of the distribution of the indexed data. To this end we use metrics such as perplexity, cross-entropy and Kullback-Leibler divergence. Our experiments show that all considered index models are affected by the evolution of data, but to different degrees and in different ways. We also make the interesting observation that index models based on schema information seem to be relatively stable for estimating densities even if the schema elements diverge a lot.