The semantic web has been a method to facilitate search and application of data based on meaning across different websites and applications using common standards. Similar techniques have been created for GIS, where finding and applying geospatial data may require protocols and standards that allow the large variety of data to be more easily searched. With the plethora of GIS data, one can conceptualize GIS as a consolidated information infrastructure. Scale, interoperability, and complexity of data, particularly as the great diversity of data are often created and structured differently, require semantic ontologies to facilitate data transfer and use for increased needs for spatial services offered.
Semantic Models in GIS
Semantic ontologies have been designed to give a clearer understanding of spatial bounds and context of geospatial data. This includes defining space outside of only physical bounds but potentially in a more abstract format. Different data models are increasingly employed, requiring multi-dimensional and multiple ways in which data are viewed and understood. For semantic models, GIS tools need to manage these variations. Semantic similarity measurements can be used for finding relevant geographies such as in applying SIM-DL that compares similarity between concepts stored based on geographic feature types. One current example includes the OSM Semantic Network in finding similar or comparable places in OpenStreetMap.
While defined and ill-defined geographies can pose a challenge, other challenges include spatial-temporal data in GIS data that are particularly challenging for traditional databases such as relational databases. Use of so-called “continuum” models are one way in which parent-child relationships can be assigned for spatial-temporal data such that maps can be dynamically updated based a scalable search using potentially different data models.
Semantic Kriging Techniques for Poor Quality GIS Data
However, one of the bigger challenges current research is focusing on is geospatial data that have inherent errors or are missing information. Search may yield a useful result, but the data returned may have structural or quality problems. One way to resolve data quality is the use of semantic kriging techniques that apply semantic association with ordinary kriging techniques to interpolate what missing values might be for a semantically identified dataset. Such technologies are likely to become more common as the scale of data rapidly grows.
 For more developed approach on semantic GIS, see: Cai, G. (2007). Contextualization of geospatial database semantics for human-GIS interaction. Geoinformatica, 11, 217–237.
 For more on utilizing SIM-DL, see: Janowicz, K., Schade, S., Bröring, A., Keßler, C., Maué, P., & Stasch, C. (2010). Semantic Enablement for Spatial Data Infrastructures. Transactions in GIS, 14(2), 111–129.
 For a relatively recent approach, see: Harbelot, B., Arenas, H., & Cruz, C. (2013). Continuum: a spatiotemporal data model to represent and qualify filiation relationships (pp. 76–85). ACM Press.
 For more on semantic kriging, see: Bhattacharjee, S., Mitra, P., & Ghosh, S. K. (2014). Spatial Interpolation to Predict Missing Attributes in GIS Using Semantic Kriging. IEEE Transactions on Geoscience and Remote Sensing, 52(8), 4771–4780.