=Paper=
{{Paper
|id=Vol-3011/pattern1
|storemode=property
|title=A Pattern for Modeling Causal Relations Between Events
|pdfUrl=https://ceur-ws.org/Vol-3011/pattern1.pdf
|volume=Vol-3011
|authors=Cogan Shimizu,Rui Zhu,Gengchen Mai,Mark Schildhauer,Krzysztof Janowicz,Pascal Hitzler
}}
==A Pattern for Modeling Causal Relations Between Events==
https://ceur-ws.org/Vol-3011/pattern1.pdf
A Pattern for Modeling Causal Relations
Between Events?
Cogan Shimizu1 , Rui Zhu3 , Gengchen Mai3 , Mark Schildhauer2 , Krzysztof
Janowicz3 , and Pascal Hitzler1
1
Data Semantics Laboratory, Kansas State University, USA
2
Center for Spatial Studies, University of California, Santa Barbara, USA
3
National Center for Ecological Analysis & Synthesis, Santa Barbara, USA
Abstract. Space and time are useful nexuses for integrating data. For
instance, events affect the places in which they occur and the people that
participate in them. By capturing the effects that they may have on a
place, coupled with authoritative sources on possible causality between
types of events, we can model causal relations between events. In this
paper we present an ontology design pattern for modeling the causal
relations between events, discuss the primary conceptual components,
how they may be instantiated, and present overarching examples related
to the domain of disaster risk management.
1 Introduction
Space and time are frequently useful nexuses for integrating data. For example,
using common spatial or topological calculi (e.g., such as RCC5, RCC8, or DE-
9IM) one can describe how spatial entities (e.g., events or records of events)
interrelate. However, there are fewer resources for modeling how events may (or
did) interact causally. That is, via time and space, in such a way that they
affect or cause each other to occur. We note that this is distinct from different
conceptualizations of an event, such as the ontology design pattern for a recurring
event series [1]. We emphasize the importance of causation. Certain notions, such
as seasonality, is not the same as causation. To this end, we have developed an
ontology design pattern that provides a framework for modeling spatiotemporal
data, in particular events, and capturing the nature of relationships between
them, emphasizing causality, as declared by some a priori notion of causation,
such as the IRDR’s (Integrated Research on Disaster Risk) taxonomy [7]. More
specifically, this pattern addresses a scenario that is concerned with three key
questions.
– How are events connected to each other?
– Who asserts that they are connected?
– How do these events affect the places at which they occur?
?
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 Shimizu, C. et al
Answering these questions is valuable for understanding both the nature of
events, but also for understanding places. More concretely, it may allow us to
examine the causes (and subsequently consequences) of sociodemographic or geo-
graphic triggers in an area. This is valuable in its own right, but also in particular
valuable to the domain of disaster risk management, whereby understanding the
causal relations between events can result in saved lives. We briefly consider, at
a top level, two scenarios within the domain of disaster risk management.
– Identifying possible locations of future subsidence
– Predicting resurgence of endemic disease
We briefly expand on these use-case scenarios in the next section. In Section 3 we
discuss the details of this ontology design pattern and in Section 4 we conclude.
2 Use-case Scenarios
The following use-case scenarios are motivated by the KnowWhereGraph project4
and its partners within the domain of disaster risk management. KnowWhere-
Graph aims at providing a densely interlinked knowledge graph for environmen-
tal intelligence applications for enriching the data of decision-makers and data
scientists with pre-integrated data custom-tailored to their spatial area of inter-
est, thereby reducing the time needed to address an emerging crisis or to gain
situational awareness.
We have identified two such use-cases that demonstrate the usefulness of our
pattern. In the following, we have included a selection of competency questions
that were used to guide the development of this pattern.
2.1 Wildfire Scenario
In this scenario, we are interested in the consequences of a wildfire that lead
to the pre-conditions of other natural hazards. In particular, we may start with
the trigger event of the wildfire. According to the IRDR Programme’s “Peril
Classification and Hazard Taxonomy” this may be a lightning strike or a human
action [7]. The resultant wildfire can drastically and problematically induce soil
erosion (e.g., by removing the flora and root systems that hold soil together)
[8]. Subsequently, a storm with heavy rainfall can cause a landslide, which in
turn can degrade soil, damage infrastructure, and so on. Modeling this chain
of events in such a way that goes beyond temporal ordering can help decision
makers detect locations where possible landslides or other forms of subsidence
are likely to occur based on which events have occurred in certain places.
2.2 Hurricane Scenario
Hurricanes are seasonally recurring events that often lead to disasters with strong
primary and secondary humanitarian relief implications—from emergency med-
ical considerations due to injuries and exposure from the storm event itself, to
4
See https://knowwheregraph.org/.
A Pattern for Modeling Causal Relations Between Events 3
secondary and tertiary implications arising from disruption of food and water
supply systems, and elevation in the incidence of specific diseases, such as cholera
and dysentery. Regional variation in these factors also exist due to differences
in robustness of their infrastructures, endemism of certain diseases, and so on.
Representing associations among these factors based on past events can be used
to forecast region-specific disaster relief needs, as well as better understand the
efficacy of certain disaster relief actions.
2.3 Selected Competency Questions
We have included competency questions (CQ) that pertain to the use case de-
scribed above and CQs that may be used with other event and more general use
cases.
CQ 1. Given a fire x, which regions will be effected by smoke exposure, given
current wind direction projections?
CQ 2. How were the 2019 Southern California fires affecting the tourism
industry?
CQ 3. Was the Cholera outbreak in Mozambique contributing to the food
shortage in year x ?
CQ 4. What are the causalities of the wildfire?
CQ 5. What factors can you find in a specific region that would help explain
e.g. the stroke belt. Which contaminants of farms may be related from
the health literature to strokes?
CQ 6. What farmlands or vegetation covers have been mostly affected in
the fire?
CQ 7. What were the reasons for the landslide east of Santa Barbara in
April 2017?
CQ 8. What were wildfires affecting the Ventura area in the 2010s?
CQ 9. Where are areas of increased heat exceedence and pollution, where
migration is not driven by urbanization?
CQ 10. Where are the places where heat is rising and (human) migration is
occurring where there are no indicators of urbanization?
CQ 11. Which farm has experienced disease?
CQ 12. Which region affected by a transmissible disease is affected by a hur-
ricane?
CQ 13. Which region affected by the current hurricane just suffers from an-
other natural disaster?
CQ 14. Which regions affected by wildfires are expected to experience heavy
rain?
3 The Causal Event Pattern
Overview
The Causal Event pattern has four main components: Event(Abstract), Event
(Concrete), Provenance, and Place. That is not to say that the others are unim-
portant, but that these are the key conceptual components. We note that a
Shimizu, C. et al
Fig. 1. The schema diagram for the CausalEvent Pattern. Yellow boxes are classes; blue dashed boxes are also classes, but acknowledge
external dependency (i.e., they are left unmodeled in the pattern); and edges with filled arrows are object properties. The large Gray
box indicates a module, which indicates a conceptual grouping of nodes in the schema diagram.
4
A Pattern for Modeling Causal Relations Between Events 5
Fig. 2. This diagram shows naively constructed instance data that populates a portion
of the pattern, emphasizing how it might connect to multiple instances of itself.
6 Shimizu, C. et al
notion of time is also an incredibly important but that we do not commit to any
specific conceptualization thereof. Furthermore, the distinction between the two
different types of events (read: algorithm vs. execution thereof) is central. This
allows us to make top-level statements about the type of an event, such as the
environmental characteristics necessary for it to occur and the consequences that
follow. Provenance is also important as it drives the trust of the overall knowl-
edge graph – whose reputation is at stake for making these claims? Finally, the
notion of Place is important for grounding these events in space (and time) in a
human meaningful way.
The rest of the concepts play a supporting role: StateOfAffairs, Observa-
tion, and ObservationTypes allow us to record the empirical data that indicates
the presence of an event, or model the conditions in a location according as
they set-up, or have been impacted by, events. The PossiblyCausesRelation and
ResultsInRelations are reifications of simpler properties that allow us to more
appropriately, and directly, capture provenance.
A schema diagram for this pattern is shown in Figure 1 and an example of it
in a naive population is shown in Figure 2. For each concept in the pattern, we
provide the formalization as well as further discussion regarding its role in the
pattern and how it may be used and instantiated.5 Formalization was conducted
according to the “Systematic Axiomatization” Step in the Modular Ontology
Modeling paradigm, utilizing the axiom patterns (and labels) as found in [3].
Each axiom appears only once in this section and appears in the section cor-
responding to the “source” of the arrow representing the relation. Throughout,
we use initialisms for formatting purposes (e.g., STE in place of Spatiotempo-
ralExtent, RIR in place of ResultsInRelation, and PCR in place of PossiblyCauses-
Relation).
The OWL file can be found online 6 and is annotated with extended OPLa
[6,5]. This pattern has already been integrated into the internal MODL for Co-
ModIDE [11]. Scoped Domain and Scoped Range axioms restrict the domains
and ranges based on fillers; this is a strict axiom that intends to limit the overall
impact of the axiom on the rest of the ontology.
Finally, we note that the names for the classes and properties can be con-
tentious. However, this pattern is meant to be used as a template (i.e., turned
into a module through template-based instantiation [4]); in template-based in-
stantiation, the structure of the pattern is re-used, where the names provide
guidance for the initial conceptualization in an ontology engineering workflow.
Thus, the names generally change in this process and are no longer a concern.
SpatiotemporalExtent
STE v ∀overlapsWith.STE (Scoped Range) (1)
5
By this we mean template-based instantiation which is the method by which a pattern
is adapted to a use-case [4], particularly in the MOMo setting [12].
6
See https://github.com/KnowWhereGraph/causal-events-pattern.
A Pattern for Modeling Causal Relations Between Events 7
∃overlapsWith.STE v STE (Scoped Domain) (2)
SpatiotemporalExtent is left unmodeled in this pattern and is instead left as a
“hook” for potentially more complex modeling depending on specific needs. In
the past we have instantiated this concept as a pair of data properties connecting
latitude and longitude (and ignoring the temporal component). Alternatively, we
have utilized concepts from the commonly accepted standards from the Open
Geospatial Consortium and W3C GeoSPARQL [9] and owl:Time [14], respec-
tively.
Place
Place v ∀hasSTE.STE (Scoped Range) (3)
∃hasSTE.Place v STE (Scoped Domain) (4)
Place refers to a conceptual location that goes beyond mere coordinates. These
might be very well defined, such as the boundaries of a voting district, or vague
regions, such as “Southern California.” In the case of the latter, the ontology
engineer may opt to remove the hasSTE property and perhaps utilize a locatedIn
property that points back at Place. One way of instantiating this node would be
through a gazetteer.
Event(Concrete)
Here, we use Event(C) in place of Event(Concrete).
Event(C) v ∀hasSTE.STE (Scoped Range) (5)
∃hasSTE.Event(C) v STE (Scoped Domain) (6)
Event(C) v ∃hasSTE.Event(C) (Existential) (7)
Event(C) v ≤1hasSTE.Event(C) (Functionality) (8)
Event(C) v ∀affects.Place (Scoped Range) (9)
∃affects.Event(C) v Place (Scoped Domain) (10)
Event(C) v ∀ofType.Event(Abstract) (Scoped Range) (11)
∃ofType.Event(C) v Event(Abstract) (Scoped Domain) (12)
Event(C) v ∀hasRIR.RIR (Scoped Range) (13)
∃hasRIR.Event(C) v RIR (Scoped Domain) (14)
Event(Concrete) is an event that occurs in space and time. This concept is
complementary, and disjoint with, the Event(Abstract) class. Essentially, the dif-
ference is that the Event(Abstract) is the prototypical or archetypal notion of a
type of event. For example, a Hurricane (the scientific topic) can cause flooding
after landfall. This is not about any specific hurricane, but hurricanes in gen-
eral. Hurricane Katrina, for example, did cause flooding and we can leverage this
8 Shimizu, C. et al
connection between abstract and concrete for a high fidelity model. Nearly any
ontology for events can be used here.
We note, in Axioms 7 and 8, that an event must occur in space and time;
that is, it has a spatiotemporal extent.
Event(Abstract)
Event(Abstract) v ∀hasPCR.PCR (Scoped Range) (15)
∃hasPCR.Event(Abstract) v PCR (Scoped Domain)
(16)
PCR v ∀resultsIn.Event(Abstract) (Scoped Range) (17)
∃resultsIn.Event(Abstract) v RIR (Scoped Domain)
(18)
Event(Abstract) is the abstract notion of an event. For instance, an expert may
study hurricanes or wildfires.
StateOfAffairs
Here, we use Obs in place of Observation.
StateOfAffairs v ∀pertainsTo.STE (Scoped Range) (19)
∃pertainsTo.StateOfAffairs v STE (Scoped Domain) (20)
StateOfAffairs v ∀indicates.Event(C) (Scoped Range) (21)
∃indicates.StateOfAffairs v Event(C) (Scoped Domain) (22)
StateOfAffairs v ∀hasConstituent.Obs (Scoped Range) (23)
∃hasConstituent.StateOfAffairs v Obs (Scoped Domain) (24)
StateOfAffairs is a collection of conceptually linked observations. A straightfor-
ward choice for instantiating this node may be the ObservationCollection from the
extended SOSA/SSN ontology [2]. In SOSA/SSN members of such collections
all share at least one attribute, such as the time they occur, or their feature of
interest. In this case, the constituent observations would share a temporal entity
that is strictly after an Event.
ResultsInRelation
Here, we use accordingTW in place of accordingToWhom.
RIR v ∃hasRIR− .RIR (Existential) (25)
RIR v ∀resultsIn.StateOfAffairs (Scoped Range) (26)
∃resultsIn.RIR v StateOfAffairs (Scoped Domain) (27)
RIR v ∃resultsIn.RIR (Existential) (28)
A Pattern for Modeling Causal Relations Between Events 9
RIR v ∀accordingTW.Provenance (Scoped Range) (29)
∃accordingTW.RIR v Provenance (Scoped Domain) (30)
ResultsInRelation is a reification of the resultsIn property. This is used to attach
provenance. We note in Axiom 26 that the inverse filler of hasResultsInRelation
must exist and must be a ResultsInRelation.
Provenance
Provenance is left unmodeled and is instead left as a “hook” for potentially more
complex modeling depending on specific needs. Generally, we suggest to utilize
the EntityWithProvenance pattern included in MODL [13], which itself is based
on the PROV Ontology [10].
Observation
Here, we use accordingTW in place of accordingToWhom, Obs in place of Obser-
vation, and hasOT in place of hasObservationType.
Obs v ∀accordingTW.Provenance (Scoped Range) (31)
∃accordingTW.Obs v Provenance (Scoped Domain) (32)
Obs v ∀hasSTE.STE (Scoped Range) (33)
∃hasSTE.Obs v STE (Scoped Domain) (34)
Obs v ∀hasOT.ObservationType (Scoped Range) (35)
∃hasOT.Obs v ObservationType (Scoped Domain) (36)
Observation is some records of fact about a place in space and time. In the
same manner as StateOfAffairs, the straightforward instantiation is also from
SOSA/SSN with the eponymous sosa:Observation.
ObservationType
ObsType v ∀pertainsTo.Event(Abstract) (Scoped Range) (37)
∃pertainsTo.ObsType v Event(Abstract) (Scoped Domain) (38)
ObservationType determines the aspect of reality that the Observation is record-
ing. This is an explicit typing mechanism, but can also be instantiated instead
as an ObservableProperty from SOSA/SSN.
PossiblyCausesRelation
PCR v ∀resultsIn.Event(Abstract) (Scoped Range) (39)
∃resultsIn.PCR v Event(Abstract) (Scoped Domain) (40)
10 Shimizu, C. et al
PCR v ∃resultsIn.PCR (Existential) (41)
PCR v ∀accordingTW.Provenance (Scoped Range) (42)
∃accordingTW.PCR v Provenance (Scoped Domain) (43)
−
PCR v ∃hasPCR .PCR (Existential) (44)
PossiblyCausesRelation is a reification of the possiblyCauses property, so that
provenance may be attached.
4 Conclusion
Modeling the causal relationships between events is an important step in un-
derstanding places. By understanding what has happened in a location and, to
some extent, why those events occurred, one can gain deep insight into the na-
ture of a particular place, and possibly, what events can be expected to occur.
As such, we have developed this pattern as a first step in understanding the
nature of causation between complex events. To do this, we distinguish between
the abstract and concrete notions of events. For example, consider the difference
between a popular pizza recipe and the actual pizza that is produced. A recipe,
when reasonably followed, produces some (hopefully) tight variation of the ex-
pected output. We consider the notion of a “hurricane” to be similarly useful.
Thus by understanding the generalities of the abstract hurricane, we may rea-
son more correctly about instances of a hurricane, such as “Hurricane Katrina.”
The pattern currently assumes that the ontology engineer has a priori knowl-
edge of causal relations, such as using taxonomies from IRDR Programme or
the United Nations. However, one could consider that the PossiblyCausesRelation
to be generated by some KG mining algorithm detecting spatiotemporal over-
lap and indicating possible causation. Additionally, we have demonstrated two
use-case scenarios, particularly within the domain of disaster risk management,
where modeling such notions will have a high impact. Additionally, we provided
a basic graphical example that easily maps to triple output.
Future work entails expanding the Event(Abstract) module for more sophisti-
cated modeling, as well as including shortcuts to simplify the population of the
pattern.
Acknowledgements. The authors acknowledge support by the National Sci-
ence Foundation under Grant 2033521 A1: KnowWhereGraph: Enriching and
Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technolo-
gies. 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.
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