Shared reference is an essential aspect of meaning. It is also indispensable for the semantic web, since it enables to weave the global graph, i.e., it allows different users to contribute to an identical referent. For example, an essential kind of referent is a geographic place, to which users may contribute observations. We argue for a human-centric, operational approach towards reference, based on respective human competences. These competences encompass perceptual, cognitive as well as technical ones, and together they allow humans to inter-subjectively refer to a phenomenon in their environment. The technology stack of the semantic web should be extended by such operations. This would allow establishing new kinds of observation-based reference systems that help constrain and integrate the semantic web bottom-up.

Geographic location is a key component for information retrieval on the Web, recommendation systems in mobile computing and social networks, and place-based integration on the Linked Data cloud. Previous work has addressed how to estimate locations by named entity recognition, from images, and via structured data. In this paper, we estimate geographic regions from unstructured, non geo-referenced text by computing a probability distribution over the Earth's surface. Our methodology combines natural language processing, geostatistics, and a data-driven bottom-up semantics. We illustrate its potential for mapping geographic regions from non geo-referenced text.