Entities in large RDF data graphs are often associated with spatial information (i.e., locations and geometries). This has motivated the extension of RDF query languages (such as SPARQL) to support queries with spatial predicates. Still, there are limited efforts on the evaluation of spatial queries over RDF data. We are interested in extending RDF storage and search models in this direction. In  we proposed an effective encoding scheme for spatial entities, the introduction of on-the-fly spatial filters and spatial join algorithms, and several optimizations that minimize the overhead of geometry and dictionary accesses. These techniques are general enough to be applied on most RDF engines; we implemented them as an extension to the open-source RDF-3X engine and evaluated their effectiveness. Still, structured query languages, such as SPARQL, have limited practical applicability because they require users to understand the language as well as the RDF schema. Keyword search on RDF data aims at relieving the user from these requirements; the user only inputs a set of keywords and the goal is to find small RDF subgraphs which contain all keywords. In , we studied an intuitive location-based keyword query on RDF data, which searches for RDF subgraphs that contain the query keywords and are rooted at spatial entities close to the query location. The novelty of kSP queries is that they are location-aware and that they do not rely on the use of structured query languages. We designed search and data pre-processing techniques that facilitate spatial RDF keyword search.
 J. Liagouris, N. Mamoulis, P. Bouros, and M. Terrovitis, «An Effective Encoding Scheme for Spatial RDF Data,» Proceedings of the VLDB Endowment (PVLDB), 7(12):1271-1282, 2014.
 J. Shi, D. Wu, and N. Mamoulis «Top-k Relevant Semantic Place Retrieval on Spatial RDF Data,» Proceedings of the ACM Conference on Management of Data (SIGMOD), San Francisco, CA, June 2016.