Author(s): Till Blume, Matthias Schulte-Althoff, Thomas Gottron, Ansgar Scherp
Abstract: Schema-level indices address many challenges that come with the growing size of the Web of Data. We argue that there is not a one-size-fits-all index for the Web of Data. Rather, a parameterized, formal model is needed that allows to quickly design, tailor, and compare different schema-level indices. We provide the first formal model for schema-level indices called FLuID. Our model is abstracted from existing indices. In addition, our model provides novel features such as aggregation over owl:sameAs as well as RDFS inferencing. Indices defined with FLuID can be efficiently computed in O(n). We implemented the FLuID model following an existing stream-based schema computation approach for the Web of Data. We empirically show that indeed different index models are needed for different information needs, datasets, and space requirements.
Keywords: Linked data; Schema-level indices; Parameterized meta model