Know, Know Where, KnowWhereGraph: A densely connected, cross‐domain knowledge graph and geo‐enrichment service stack for applications in environmental intelligence

Abstract

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines, intelligent personal assistants, business intelligence, and so on. Interestingly, despite large-scale data availability, they have yet to be as successful in the realm of environmental data and environmental intelligence. In this paper, we will explain why spatial data require special treatment, and how and when to semantically lift environmental data to a KG. We will present our KnowWhereGraph that contains a wide range of integrated datasets at the human-environment interface, introduce our application areas, and discuss geospatial enrichment services on top of our graph. Jointly, the graph and services will provide answers to questions such as "what is here," "what happened here before," and "how does this region compare to . . . " for any region on earth within seconds.

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

“…Specifically, we develop the EVKG based on external data sources in terms of four aspects including EV basic specifications, EV registration information, EV charging infrastructure, and electricity transmission networks. Moreover, we also link EVKG with other open-sourced KGs such as KnowWhereGraph (Janowicz et al, 2022) and GNIS-LD (Regalia et al, 2018), which are also connected with DBpedia (Auer et al, 2007), Wikidata (Vrandečić, 2012), and GeoNames (Ahlers, 2013). Three criteria are used to select EV-related data sources: (1) finer spatial resolution: EV data should be recorded in small geographic units (e.g., ZIP code level); (2) finer temporal resolution: EV data should be updated frequently (e.g., annually); and (3) reliable data resources: EV data should be collected from reliable organizations and institutions (e.g., governments and large NGOs).…”

“…Other than these general-purpose graphs, we also have various large-scale geospatial KGs such as GeoNames (https://www.geona mes.org/; Ahlers, 2013) and KnowWhereGraph (https://knowl edgew hereg raph. org/; Janowicz, 2021;Janowicz et al, 2022) which have shown unique superiority in uplifting environmental intelligence as well.…”

“…To address the challenges in the EV industry, knowledge graphs (KGs) have emerged as a promising solution (Noy et al, 2019). As a novel data paradigm, KGs are a combination of technologies, terminologies, and data cultures for densely interconnecting (Web‐scale) data across domains in a human and machine‐readable format (Bizer et al, 2009; Janowicz et al, 2022). With an ontology, or so‐called KG schema, to encode the terminology semantically, KGs also foster interoperability across different domains (Hitzler, 2021; Zhu, Janowicz, Mai, et al, 2022).…”

Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning

“…One research direction in Symbolic GeoAI are Geospatial KGs (GeoKGs) (Janowicz et al, 2022;Kuhn et al, 2014). GeoKGs, as symbolic representations of geospatial knowledge, are at the core of GeoAI and facilitate many intelligent applications such as geographical question answering (Chen et al, 2013;Kuhn et al, 2021;Nyamsuren et al, 2021;Scheider et al, 2021), geospatial knowledge summarization (Yan et al, 2019), geospatial data integration (Bernard et al, 2022;Sun et al, 2021;Trisedya et al, 2019), or geographic knowledge discovery (Hu et al, 2015;Jiang et al, 2018;Li et al, 2012;Mai, Janowicz, Prasad, et al, 2020;Park & Lee, 2022).…”

GeoGraphViz: Geographically constrained 3D force‐directed graph for knowledge graph visualization

“…One of its aims is to develop cutting‐edge knowledge graph technologies for linking cross‐domain data for building an open knowledge network that fosters convergence research. A key research advancement in spatial sciences is the creation of KnowWhereGraph (Janowicz et al, 2022), a knowledge graph that connects environmental datasets related to natural disasters, agriculture, and soil properties to understand the environment impacts on society. Semantic enrichment services are provided to support semantic reasoning and question answering on top of its data store of over 12 billion facts (Liu, Gu, et al, 2022).…”