=Paper=
{{Paper
|id=Vol-3637/paper46
|storemode=property
|title=The KnowWhereGraph Ontology: A Showcase
|pdfUrl=https://ceur-ws.org/Vol-3637/paper46.pdf
|volume=Vol-3637
|authors=Cogan Shimizu,Shirly Stephen,Rui Zhu,Kitty Currier,Mark Schildhauer,Dean Rehberger,Pascal Hitzler,Krzysztof Janowicz,Colby K. Fisher,Mohammad Saeid Mahdavinejad,Antrea Christou,Adrita Barua,Abhilekha Dalal,Sanaz Saki Norouzi,Zilong Liu,Meilin Shi,Ling Cai,Gengchen Mai,Zhangyu Wang,Yuanyuan Tian
|dblpUrl=https://dblp.org/rec/conf/jowo/ShimizuS0CSRHJF23
}}
==The KnowWhereGraph Ontology: A Showcase==
https://ceur-ws.org/Vol-3637/paper46.pdf
The KnowWhereGraph Ontology: A Showcase
Cogan Shimizu1,∗ , Shirly Stephen2 , Rui Zhu3 , Kitty Currier2 , Mark Schildhauer4 ,
Dean Rehberger5 , Pascal Hitzler6 , Krzysztof Janowicz2,7 , Colby K. Fisher8 ,
Mohammad Saeid Mahdavinejad6 , Antrea Christou1 , Adrita Barua6 , Abhilekha Dalal6 ,
Sanaz Saki Norouzi6 , Zilong Liu2,7 , Meilin Shi2,7 , Ling Cai9 , Gengchen Mai10 ,
Zhangyu Wang2 and Yuanyuan Tian11
1
Wright State University, OH, USA
2
University of California, Santa Barbara, CA, USA
3
University of Bristol, United Kingdom
4
National Center for Ecological Analysis & Synthesis, CA, USA
5
Michigan State University, MI, USA
6
Kansas State University, KS, USA
7
University of Vienna, Austria
8
Hydronos Labs, Princeton, NJ, USA
9
IBM Research, CA, USA
10
University of Georgia, GA, USA
11
Arizona State University, AZ, USA
Abstract
KnowWhereGraph is one of the largest fully publicly available spatially enabled knowledge graphs. It
includes data on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature,
precipitation), soil properties, crop and land-cover types, demographics, and human health, among other
themes. These have been leveraged through the graph by a variety of applications to address challenges
in food security and agricultural supply chains; sustainability related to soil conservation practices and
farm labor; and delivery of emergency humanitarian aid following a disaster. This paper showcases the
KnowWhereGraph ontology, which acts as the schema for the KnowWhereGraph. We discuss how it
enables the powerful spatial and semantic integration across these datasets, our validation paradigm, and
the applications it supports.
Keywords
Geospatial Knowledge Graphs, Ontology Engineering, Modular Ontology Modeling, Geo-Enrichment
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1. Introduction
KnowWhereGraph1 (KWG) is one of the largest, publicly available geospatial knowledge graphs
in the world. The KWG supports applications in the food, agriculture, humanitarian relief, and
energy sectors and their attendant supply chains, generally; environmental policy issues relative
to interactions among agricultural sustainability, soil conservation practice, and farm labor; and
delivery of emergency humanitarian aid, within the US and internationally. It brings together
over 30 datasets related to observations of natural hazards (e.g., hurricanes, wildfires, and smoke
plumes), spatial characteristics related to climate (e.g., temperature, precipitation, and air quality),
soil properties, crop and land-cover types, demographics, and human health, among others,
resulting in a knowledge graph with over 16 billion triples.
We present the KnowWhereGraph ontology, resulting from significant knowledge and ontology
engineering and reuse, which integrates these datasets, to address meaningful use cases, and
provides answers to questions such as “what is here”, “what happened here before”, “how does
this region compare to…” at a high spatial resolution across the entire globe [1].
2. Related Work
There are a few ontologies that deal with geospatial information, but not to the extent or breadth
that the KWG ontology provides. In some cases, we re-use related ontologies (e.g., SOSA/SSN [2])
and describe them in Section 3.4.
Some standardized and structured vocabularies for describing environmental concepts exist but
have limitations that impacted their usability and effectiveness within the context of the KWG.
Moreover, these vocabularies are either large and complex, with many concepts and relationships,
or too simple, which makes them challenging to use effectively.
The Environmental Ontology (ENVO) ENVO covers a wide range of environmental concepts
[3], but unfortunately has limited coverage of human-related environmental concepts, such as
environmental pollution. Some definitions in ENVO are ambiguous or imprecise, which led to
confusion and misinterpretation. For example, “flood” as a continuant versus “flooding” as an
occurrent made it ambiguous for us to realistically categorize a flood, as reported by NOAA.
Semantic Web for Earth and Environmental Terminology (SWEET) SWEET has expansive
coverage of geospatial and environmental terms and properties [4]. Unfortunately, as a whole,
SWEET tends to be imprecise, having overlapping definitions and inconsistent use of relations.
For example, both “phenomenon” and “observable property” refer to measurable or observable
Ontology Showcase and Demonstrations Track, 9th Joint Ontology Workshops (JOWO 2023), co-located with FOIS 2023,
19-20 July, 2023, Sherbrooke, Québec, Canada.
∗
Corresponding author.
Envelope-Open cogan.shimizu@wright.edu (C. Shimizu); shirlystephen@ucsb.edu (S. Stephen); rui.zhu@bristol.ac.uk (R. Zhu);
kcurrier@ucsb.edu (K. Currier); schild@nceas.ucsb.edu (M. Schildhauer); rehberg@msu.edu (D. Rehberger);
hitzler@ksu.edu (P. Hitzler); krzysztof.janowicz@univie.ac.at (K. Janowicz); colby@hydronoslabs.com (C. K. Fisher);
saeid@ksu.edu (M. S. Mahdavinejad); christou.2@wright.edu (A. Christou); adrita@ksu.edu (A. Barua);
adalal@ksu.edu (A. Dalal); sanazsn@ksu.edu (S. Saki Norouzi); zilong.liu@univie.ac.at (Z. Liu);
meilin.shi@univie.ac.at (M. Shi); lingcai@ucsb.edu (L. Cai); gengchen.mai25@uga.edu (G. Mai);
zhangyuwang@ucsb.edu (Z. Wang); yuanyuantian@asu.edu (Y. Tian)
GLOBE https://coganshimizu.com/ (C. Shimizu); https://people.cs.ksu.edu/~hitzler/ (P. Hitzler)
Orcid 0000-0003-4283-8701 (C. Shimizu)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://knowwheregraph.org/
characteristics of the natural world. Another example is where ‘part of‘ and ‘has part‘ are used
interchangeably to describe hierarchical relationships between concepts.
Google Data Commons & Schema.org The Google Data Commons,2 powered by annotations
from Schema.org3 provides a mechanism via its “Map Explorer” tool to visualize data that has a
geospatial component. However, by its nature, Schema.org is a semantically shallow representa-
tion, which generally indicates the type of data something is. This was insufficient for the heavy
semantic harmonization needs of the KWG ontology.
3. The KnowWhereGraph Ontology
The KWG ontology satisfies several requirements: enabling geospatial integration, facilitating
data integration, providing rich inferencing, and expediting maintainability. We describe these
below.
Enable Geospatial Integration The primary purpose of KWG is to provide a convenient
method for integrating data along a geospatial dimension. This is integral to the mission of the
project and, subsequently, a core requirement for the graph and its schema.
Facilitate Data Integration KnowWhereGraph must be capable of providing an overarching
framework for the semantic harmonization of key terms and concepts.
Provide Rich Inferencing Beyond a 1:1 representation of the (integrated) datasets, KWG’s
schema must be expressive enough to infer latent relationships between datasets, such as causality
of events or the inheritance of spatial characteristics.
Highly Maintainable To be maximally useful, KWG must be easily maintained by the com-
munity. This includes both the degree of facilitation of data integration, but how amenable the
schema – and thus the graph – are to modification: either through the incorporation of new or
evolving use cases, rectifying conceptual errors in the graph, or adapting to changes.
To facilitate satisfying these requirements we utilize the Modular Ontology Methodology (MOMo;
[5]), which leverages ontology design patterns [6] as first-class citizens to enable quick, iterative,
plug-and-play schema development. Through a process called template-based instantiation [7]
a single pattern can be used to represent similar datasets with minimal effort; this process is
documented in additional detail [8].
3.1. Represented Domains
KWG represents a myriad of domains – and is capable of integrating more. Any domain that
is capable of being represented along a geospatial axis can be incorporated. As of now, the
graph generally supports the hazard (and related) domains. That is, it supports given physical
phenomena that can negatively impact places, people, or the economy, including the specifics of
who is impacted, what is impacted, and how the impacts can be mitigated. In addition to the four
general requirements above, the KWG ontology supports several pilot use cases across different
domains.
Humanitarian Relief Disasters are complex and dynamic situations requiring humanitarian
organizations to evaluate and respond rapidly to many different issues simultaneously. Often what
2
https://www.datacommons.org/
3
https://schema.org
Thematic Dataset Source Agency Example Attributes
Soil Properties USDA soil type, farmland class
Wildfires USGS, USDA, USFS, NIFC wildfire type, num acres burned
Earthquakes USGS magnitude
Climate Hazards NOAA casulaties, property damage
Experts (Covid-19 Mobility) Direct Relief name, affiliation, expertise
Expert (General) KWG, UC System, Direct Relief, name, affiliation, expertise
Semantic Scholar
Cropland Types USDA crop types (raster data)
Air Quality EPA air quality index
Smoke Plumes Forecasts NOAA daily smoke plume forecast
Climate NOAA temperature, precipitation
Disaster Declarations FEMA area, amount approved
Smoke Plume Extents NOAA smoke plume extent
BlueSky Forecasts BlueSky PM10, PM5
Highway Networks DoT road type, road length, signage
Public Health Observations CDC, USCB, University of poverty, diabetes, obesity
Wisconsin
Public Health Infrastructure HIFLD pharmacies, hospitals
Social Vulnerability CDC, ATSDR social vulnerability index
Hurricane Tracks NOAA max wind speed, min pressure
Table 1
This table shows the thematic datasets (i.e., those which describe physical phenomena and their relation
to time and places) that are integrated via the KWG ontology.
is most needed to improve effective response is quick access to the right experts at the right time.
To assist in identifying people with expertise in humanitarian aid and relief, with a particular focus
on health and the health care impacts of disasters, we are working with Direct Relief to showcase
how our knowledge graph can give them rapid access to area briefings, including previous events
and physical properties of, for example, climate and transportation infrastructure in the affected
regions.
Food Supply Chain Resilience Moreover, understanding and improving the robustness and
adaptability of the food supply chain is of critical importance to make it more resilient to distur-
bances in food supply and demand networks. Network fracturing and delayed recovery during
extreme weather events is always an inherent risk when it comes to wildfires, floods, and other
natural hazards. In the face of uncertain natural hazards, which are increasing in frequency and
severity, it is vital that the implications of these disruptions are evaluated for the source nodes of
our supply chains, such that resiliency in the whole supply chain can be promoted. To solve this
challenge, we are partnering with the Food Industry Association (FMI), which has identified food
safety and food quality issues rising from environmental disasters or disturbances as high-priority
industry concerns.
3.2. Integrated Datasets
Tables 1 and 2 show condensed views of the datasets that the KWG Ontology integrates. These
datasets are widely sourced, originating from non-governmental organizations (NGOs), US gov-
ernmental agencies, open source data, and the commercial sector (with attribution).
Place-Centric Dataset Defining Authority Spatial Coverage
S2 Cells Google Lvl 9 (Global), Lvl 13 (US)
Global Administrative Regions GADM.org Global
US Federal Judicial Districts DoJ, ESRI US
National Weather Zones NOAA US
FIPS Codes USCB US
Designated Market Areas Nielsen US
ZIP Codes USPS US
Climate Divisions NOAA US
Census Metropolitan Area USCB US
Drought Zone NDMC US
GNIS USGS US
Table 2
This table shows the place-centric datasets (i.e., those which describe human-meaningful regions) which
are integrated via the KWG ontology.
3.3. Using the KWG Ontology
KWG, and thus the KWG Ontology, are used in the pilots described above, as well as in several tools
(e.g., Knowledge Explorer [9], which supports “follow-your-nose” exploration) for information
retrieval and visualization and geo-enrichment services. These tools are online and can be found,
with additional documentation and tutorials at https://knowwheregraph.org/tools/.
3.4. Ontologies Reused in the KWG Ontology
We have developed several standalone ontologies and resources, and reuse a number of well-
known, standardized, or W3C-recommended vocabularies, taxonomies, and ontologies. This helps
to support greater interoperability with other knowledge graphs, to maintain consistency in our
data model, and to leverage existing tools that support these vocabularies.
Ontology Design Patterns During the development of the KWG ontology, we both create new
and adapt ontology design patterns (ODP; [6]). So far, four new patterns have been developed:
the hierarchical features ODP [10], the causal relations ODP [11], the taxonomy alignment ODP
[12], and the computational observation ODP [13]. We adapted existing patterns from MODL
[14]: EntityWithProvenance ODP and the AgentRole ODP.
GeoSPARQL We used GeoSPARQL [15, 16], an Open Geospatial Consortium (OGC) standard,
to represent our geospatial data in RDF. It, in turn, reuses the OGC Simple Features (SF) standard,
which defines a set of geometric primitives (e.g., points or polygons) and their spatial relationships.
Within the KWG ontology, we represent any discrete geographic feature type (Hazard, Region,
and their subclasses) that has a spatial extent as a subclass of GeoSPARQL’s geo:SpatialObject
class. We also use the spatial relationships from GeoSPARQL (based on DE-9IM spatial relations)
to establish pre-computed spatial relationships between any two spatial features. While several
graph databases support GeoSPARQL, we found a number of features of GraphDB including its
support of GeoSPARQL [17] made it the best candidate for KWG.
SOSA/SSN The Sensors, Observations, Sampling, and Actuator Ontology [18], coupled with the
Semantic Sensor Network ontology (SOSA/SSN; [19]) are used to model observations made by
sensors that detect, measure, or observe properties of features [2]. They can be made to work
together by refining the interpretation of two concepts: sosa:FeatureOfInterest, and sosa:Obser-
vation. In the KWG ontology, a sosa:FeatureOfInterest represents both the thing whose property
can be observed and anything that can have a spatial representation and an associated geometry.
Observations (and their collections) in SOSA are defined as the act of measuring, estimating, or
calculating the value of a property using a sensor (device, agent, or software), while a feature of
interest is an element whose property is being observed to arrive at a result.
OWL-Time The Time ontology is (re)used for all representations of time within the KWG
ontology. The super-property for most time-related conceptualizations is kwg-ont:hasTempo-
ralScope, which effectively has a range of any temporal entity from OWL-Time. For serializations,
we reuse the XML schema datatypes.
Metadata and Provenance To easily maintain metadata and provenance, we reuse several
vocabularies to describe the KWG ontology. For example, we use annotation properties from
Dublin Core Metadata Initiative (DCMI) Metadata Terms [20] to describe the title, description,
rights, license, date created, and creator. We use Friend of a Friend (FOAF [21]) to describe the
development team and their roles. The Simple Knowledge Organization System (SKOS) is used to
annotate definitions, examples, and the taxonomic structure between domain concepts. Finally,
we use PROV-O [22] to describe the provenance of resources, e.g., to track the provenance and
lineage of a dataset.
QUDT The QUDT (Quantities, Units, Dimensions and Data Types) ontology [23] is used for rep-
resenting climate measurement data (such as temperature, Palmer drought severity index, cooling
degree days) and their corresponding units of measure. Specific climate quantity types (such as
mean or value) are denoted using the kwg-ont:Quantity class, a subclass of kwg-ont:Quantity-
Value. Measured values and corresponding data properties are then captured using data properties
qudt-unit:unit and qudt-unit:numericValue.
The Expertise Ontology KWG contains information on agents who are experts on topics
related to specific disaster types, disaster management activities, named disasters, and public
health. The Expertise Ontology4 (EO) was developed to represent all varied expertise-related
information consistently. At a high level, EO consists of a core set of classes and properties to
1) model experts (individuals or groups), topics, and their relations, 2) represent hierarchical
relations between topics of different levels of granularity, and 3) connect topics with relevant
content in a knowledge graph. EO facilitates representing not only research- and theory-based
expertise, but also experience-based expertise by modeling the activities that an expert may have
engaged in or their role and affiliation within an organization, and scopes these spatially and
temporally.
The Disaster Management Domain Ontology KWG contains at least 11 hazard datasets and
at least one hazard (Fire) from four different sources (see Table 1). To model their semantics and
enable integration using any existing ontologies, we are developing the Disaster Management
Domain Ontology (DMDO), which will provide a framework to align diverse hazard types, for-
mats of data, and domain vocabularies consistently within KWG, but also for better situational
awareness through clarification of the spatiotemporal interactions of similar events. The ontology
disambiguates hazards from disasters and their impacts, but also distinguishes spatiotemporal
events from their observations. DMDO also formalizes the UNDRR hazard classification [24]
using the taxonomy alignment ODP [12].
3.5. Evaluation
The KnowWhereGraph Ontology has been evaluated through its ability to meet use-case require-
ments, as outlined in Section 3. We do this through interviewing domain experts and analyzing
4
https://github.com/KnowWhereGraph/expertise-ontology
competency questions and their corresponding SPARQL queries with results.
In formulating aspects of the ontology, and especially in understanding specific thematic datasets,
it is necessary to draw in the expertise of knowledgeable practitioners and subject matter experts.
By iterating through multiple versions of the ontology with multiple different experts, we are
able to converge on a common conceptualization. To evaluate the materialization, as well as the
effectiveness of querying against the graph, we develop suites of competency questions. These
allow for the connection between natural language, expected usage of the graph, and the KWG
ontology (via the formulation of a SPARQL query). The analysis of the actual results, as opposed
to expected results, allows us to evaluate if our conceptualization mirrors domain expertise and
meets our use-case needs.
In a secondary manner, the KWG ontology is evaluated through its usability. That is, how well
it meets the needs of developers creating applications against the entire knowledge graph. To
this end, we realized that the materialization (aka shortcuts) would be necessary to link more
effectively places and their identifiers (and subsequently simplify queries). For example, regions
from different place-centric datasets could previously only be obtained through a complex query
that drilled down to a cell-based representation, and then abstracted back upwards to the region in
question. An entirely new version of the ontology was rolled out to accommodate this identified
need. Finally, we provide a set of shapes (defined in the SHApes Constraint Language; SHACL;
[25]) to validate the materialization of the ontology, which can be found online5 and in [26].
4. Conclusion
The KnowWhereGraph is a complex project with multiple evolving use cases, a large team, and
an ambitious goal. We have presented the KnowWhereGraph Ontology, which integrates over 30
datasets to power several motivating use cases. It was developed using a pattern-based method
(i.e., modular ontology modeling [5]) that reused a significant number of existing environmental
and geospatial ontologies, vocabularies, and resources.
Availability We provide multiple types of documentation for the KnowWhereGraph Ontology:
living documentation (generated using [27]) coupled with schema diagrams (generated manually)
can be found at [28], alongside a static, technical report (generated using [29]). Finally, the ontology
itself can be found in [30] and is released under the CC BY 4.0 license. The KnowWhereGraph is
maintained by the KnowWhereGraph team; more details can be found in [31].
Acknowledgments
This work was funded by the National Science Foundation under Grant 2033521 A1: KnowWhere-
Graph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI
Technologies. Any opinions, findings, and conclusions or recommendations expressed in this
material are those of the authors and do not necessarily reflect the views of the National Science
Foundation.
5
https://github.com/KnowWhereGraph/KWG-SHACL
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