Conceptual Neighborhood Graphs: Event Detectors, Data Relevancy, and Language Translation

The past thirty years of research have provided a host of advancements in the development of spatial relation sets and organizational structures by which we can consider a partial ordering of those sets. Multi-modal geofoundation models require knowledge of these types of sets and structures to provide cognitively plausible descriptive outputs when prompted by artificially intelligent tools. Conceptual neighborhood graphs are organizational structures that provide crucial insights into several relevant aspects of spatial data mining and spatial machine learning. In this chapter, we discuss mechanisms in which conceptual neighborhood graphs can provide insights that have not been leveraged in contemporary spatial information systems that provide opportunities for enhancing generative artificial intelligence both in sequential activities and in the translation of images and languages. We specifically discuss conceptual neighbors as event detection mechanisms within sequential image batches, pre-filtering mechanisms for relevant spatial data based on prepositional keywords, and spatial language translation between spoken and written languages.