LODE: Linking Open Descriptions of Events
Ryan Shaw1, Raphae¨l Troncy2,3 and Lynda Hardman3
1School of Information, University of California, Berkeley
2EURECOM, Sophia Antipolis, France
3CWI, Amsterdam, The Netherlands
UC Berkeley School of Information Report 2009-036
August 2009
Abstract
People conventionally refer to an action or occurrence taking place at a certain time at a specific
location as an event. This notion is potentially useful for connecting individual facts recorded in the
rapidly growing collection of linked data sets and for discovering more complex relationships between
data. In this paper, we provide an overview and comparison of existing RDFS+OWL event models,
looking at the different choices they make of how to represent events. We describe a recommended
model for publishing records of events as Linked Data. We present tools for populating this model and
a prototype of an “event directory” web service, which can be used to locate stable URIs for events that
have occurred and to provide RDFS+OWL descriptions of them and links to related resources.
Contents
1 Introduction 2
2 Comparison of Existing Event Models 2
2.1 Event Models Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Fundamental Types of Events: Aspect and Agentivity . . . . . . . . . . . . . . . . . . . . . . 3
2.3 Events and Temporal Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Events, Spaces and Places . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.5 Participation in Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.6 Events, Influence, Purpose and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.7 Events, Parts and Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Towards a Linked Data Event Model 8
4 Applications 10
4.1 Extracting Events from Wikipedia Timelines . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Interoperability with Legacy Event Collections . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5 Conclusion and Future Work 12

UC Berkeley School of Information Report 2009-036 LODE: Linking Open Descriptions of Events
1 Introduction
Though their specific methods differ significantly, both historians and journalists work to produce narrative
chains of events to explain phenomena in the past. The resulting historical records of events constitute
valuable cultural heritage of interest to academics as well as the general public. The Linked Data1 effort
seeks to publish and connect RDF data sets on the Web using dereferenceable URIs for identifying web
documents, real-world objects, links between them and/or other pieces of information. Yet, while standard
and widely used vocabularies have emerged for representing people, places, and other types of entities as
Linked Data, none has yet emerged specifically for events.
The term “event” has several meanings. It is used to mean both phenomena that have happened (e.g.
things reported in news articles or explained by historians) and phenomena that are scheduled to happen (e.g.
things put in calendars and datebooks). Various standards and formats have been proposed for representing
the latter as structured data, usually for personal information management purposes2. Furthermore, there
are several web services available for aggregating, classifying and syndicating scheduled events3. In this
paper, we focus on the former category: phenomena that have happened in the past. Hence, we assume
events to be unique entities that, while they may have been part of a series of similar events, occurred only
once.
This paper makes two contributions. First, we compare existing models of historical events looking at the
different choices they make of how to represent events (Section 2). These models serve different communities
and have different strengths. Our goal is not to propose yet another ontology per se, but rather to build an
interlingua model that solves an interoperability problem by providing a set of axioms expressing mappings
between existing event ontologies (Section 3). Second, we present tools for populating this model with data
coming from existing sources, such as Wikipedia timelines. We describe a prototype of an “event directory”4
web service which can be used to locate stable URIs for past events and to provide RDFS+OWL descriptions
of those events and links to related resources (Section 4). Finally, we give our conclusions and outline future
work in Section 5.
2 Comparison of Existing Event Models
A number of different RDFS+OWL ontologies providing classes and properties for modeling events and their
relationships have been proposed (see Table 2). In this section, we present an analysis based on their main
Event model Ontology URL
CIDOC CRM http://cidoc.ics.forth.gr/OWL/cidoc_v4.2.owl
ABC Ontology http://metadata.net/harmony/ABC/ABC.owl
Event Ontology http://purl.org/NET/c4dm/event.owl#
EventsML-G2 http://www.iptc.org/EventsML/
DOLCE+DnS Ultralite http://www.loa-cnr.it/ontologies/DUL.owl
F http://events.semantic-multimedia.org/ontology/2008/12/15/model.owl
OpenCYC Ontology http://www.opencyc.org/
Table 1: Ontologies for representing events
constituent properties: type (Section 2.2), time (Section 2.3), space (Section 2.4), participation (Section 2.5),
causality (Section 2.6) and composition (Section 2.7). This builds upon previous work in which we examined
a number of different non-RDFS+OWL models for representing information about events [11].
1http://linkeddata.org/
2E.g. the iCalendar specification, http://tools.ietf.org/html/rfc2445
3E.g. the Upcoming social event calendar portal, http://upcoming.yahoo.com/
4We provide an interface for searching and browsing linked description of events at http://www.linkedevents.org
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2.1 Event Models Overview
Though all of the ontologies presented in Table 2 provide classes and properties suitable for representing
events, they were created to serve different purposes. The CIDOC CRM [3] and ABC [7] ontologies aim at
enabling better interoperability among metadata standards for describing complex multimedia objects held
by museums and libraries. The events they intend to describe include both historical events in the broad
sense (e.g. wars, or births) as well as events in the histories of the objects being described (e.g. changes of
ownership, or restoration).
The Event Ontology (EO) [8] was developed by the Centre for Digital Music to be used in conjunction
with music-related ontologies. Although intended to describe events such as performances, compositions,
recordings, or sound generation, there is nothing specific to the music domain in this event ontology. EO
is the most used event ontology in the Linked Data community. EventsML-G2 has been developed by
the International Press Telecommunications Council (IPTC) for exchanging structured information about
events among news providers and their partners. It is intended to represent both planned events and past
or breaking events as reported in the news, thus adopting a journalistic point of view.
DOLCE+DnS Ultralite (DUL) is a lightweight “upper” ontology for grounding domain-specific ontologies
in a set of well-analyzed basic concepts. It is a combination and simplification of the DOLCE foundational
ontology and the Constructive Descriptions and Situations pattern for representing aspects of social real-
ity [4]. The F Event Model is a formal model of events built on top of DUL. It provides additional properties
and classes for modeling participation in events, as well as parthood relations, causal relations, and corre-
lations between events. F also provides the ability to assert that multiple models represent views upon or
interpretations of the same event [9]. OpenCYC is also an “upper” ontology, but at the other end of the
spectrum from DUL: rather than being a lightweight set of core concepts it provides hundreds of thousands
of concepts intended to model “all of human consensus reality”.
2.2 Fundamental Types of Events: Aspect and Agentivity
Given their different intended applications, these ontologies define events in varying ways. Table 2.2 provides
a comparison of the prose descriptions for the top-level event classes. Furthermore, all of these ontologies,
cidoc:E2.Temporal Entity “[E2.Temporal Entity] comprises all phenomena, such as the in-
stances of E4.Periods, E5.Events and states, which happen over
a limited extent in time.”
abc:Event “An Event marks a transition between Situations.”
eo:Event “An arbitrary classification of a space/time region, by a cognitive
agent.”
eventsml:Event “...something that happens and is subject to news coverage.”
dul:Event “Any physical, social, or mental process, event, or state.”
f:Event “...perduring entities (or perdurants or occurants) that unfold over
time, i.e., they take up time..”
cyc:Situation “...a state or event consisting of one or more objects having certain
properties or bearing certain relations to each other.”
Table 2: Definitions of top-level event-related classes
with the exception of EO, make an attempt to distinguish among some fundamental types of events. The
basis upon which these distinctions are made vary.
One way to distinguish types of events is their aspect, i.e. whether the event involved is an ongoing
activity or process or, the completion of some activity or transition between states. For example, OpenCYC
defines a concept called Situation and uses aspect to distinguish between two main specializations of this
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concept: StaticSituation and Event. The former denotes a situation in which some state of affairs has
persisted throughout the situation’s interval of time, while the latter denotes a situation in which some
change has occurred during the situation’s interval of time.
CIDOC makes a similar but conceptually less clear distinction among two types of E2.Temporal Entity
which it calls E3.Condition State and E5.Event. It is less clear because CIDOC also introduces the concept
E4.Period, a type of temporal entity that is not static, yet that does not necessarily involve a change of
state. E3.Condition State is also defined narrowly to only denote descriptions of “the prevailing physical
condition of any material object or feature” which would seem to exclude descriptions of, for example, the
relative state of two things. E3.Condition State is similar to the ABC ontology’s Situation concept,
instances of which describe the states of tangible things at particular times. The ABC ontology then uses
this Situation concept to narrowly define an Event concept as a transition between two different Situation
instances. This makes it difficult to describe an event that is characterized by a change in the relationship
between two things rather than a change in the state of a single object.
Another way to classify events is to make distinctions based on whether there is some person, thing,
force, etc. that is identified as having produced the event, i.e. whether there is an agent identified. It is on
this basis that both OpenCyc and DUL distinguish an Action as a particular type of Event, and CIDOC
distinguishes an E7.Activity as a particular type of E5.Event. The ABC ontology also distinguishes an
Action concept as something performed by an agent, but rather than being a specialization of the Event
concept, it is defined as disjoint with the Event concept, which can “contain” actions via a hasAction
property. Thus the ABC ontology suggests that events are fully described as sets of actions taken by specific
agents, which may be an issue for modeling events such as earthquakes.
One potential problem with building these types of classifications into an ontology for modeling things that
happened is that they force a knowledge engineer to adopt a particular perspective on what happened. This
is desirable for precise modeling in specific domains that share a descriptive paradigm, but it is undesirable
if the goal is to enhance access to documents which may present different interpretations of the same events.
Distinctions based on aspect or agentivity are not necessarily inherent to what happened, but instead are
rooted in particular interpretations. Whether a historical event or a event reported in the news involves an
identifiable change or not, or whether agency can be assigned, is often a matter of debate, and its resolution
should not be a prerequisite for representing what happened using a concept from an ontology.
This desire to separate events from their interpretations is what drives the approach taken by DUL,
which provides a Situation concept, instances of which may describe different views or interpretations of
the same Event instance. Using the DUL ontology, the types of classifications discussed above would be
applied to instances of Situation rather than to instances of Event5.
2.3 Events and Temporal Intervals
Temporality is the major distinguishing feature of events as entities. Thus, relating events to spans of
time is arguably the most important aspect of modeling an event. Modeling the temporality of an event
should not be confused with modeling spans of time themselves. Allen’s work on temporal intervals [1] has
provided the foundation for time ontologies such as OWL-Time [6]. Modeling event temporality, on the other
hand, requires tools for linking events to spans of time. The relationship between events and chronological
spans of time is analogous to the relationship between places and spatial coordinate systems. In each case,
instances of the former have persistent, socially attributed meanings, while the latter are arbitrary systems
for subdividing an abstract space.
In general there are two possibilities for linking events to ranges of time. The first approach uses datatype
properties, directly relating event instances with RDF literals representing calendar dates (and thus typed
5DUL does specialize its Event concept on the basis of agentivity, providing the Action concept for events that have at least
one participating agent and the Process concept for events that are not recognized having participating agents.
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using one of the date-related XML Schema datatypes such as xsd:date or xsd:dateTime). The second
approach introduces a class for representing temporal intervals, and uses object properties to link event
instances with instances of this class. Temporal interval instances can then be linked to calendar values
using datatype properties.
ABC, CIDOC, and EO all take the second approach, with ABC and CIDOC introducing their own
classes for temporal intervals, and EO using the TemporalEntity class from OWL-Time. DUL allows both
approach: dates for an event can be directly asserted using the hasEventDate datatype property, or the
temporal interval involved can be made explicit by instantiating the TimeInterval class and linking an event
instance to it using the isObservableAt object property.
The advantage of associating dates directly with events is simplicity: there are fewer abstractions to deal
with, and it is simple to filter or sort events using standard date parsing and comparison routines. This also
makes it simple to export lists of events for visualization on a timeline. But the tradeoff for this simplicity is
an inability to express more complex relationships to time, such as temporal intervals that do not coincide
with date units, or uncertainty about when precisely an event took place within some bounded temporal
interval. This is a problem for representing historical events.
By introducing classes for representing temporal intervals, one can use Allen’s temporal calculus for
reasoning about these more complex relationships. For example, if the precise date of a historical event is
not known but some boundaries can be established within which it must have occurred, the time between
these boundaries can be represented as a temporal interval, and a containment relationship can be asserted
between that interval and the (unknown) interval during which the event occurred. The drawback to such an
approach is that it can be off-puttingly complex as it introduces a number of abstract entities. The problem
also arises of how to either mint URIs to identify these entities or deal with the problems introduced by
using blank nodes.
2.4 Events, Spaces and Places
Events can be linked to abstract temporal regions (Section 2.3) and to abstract spatial regions or to seman-
tically significant places. ABC, CIDOC and EO only support linking to spatial regions. CIDOC provides a
class (E53.Place) for “extent in space” to which events can be related via the P7.took place at property.
Instances of E53.Place may have names (E44.Place Appellation), but there is no way to link an event to
a place name except through a specific spatial extent. ABC’s Place class also emphasizes spatial location
rather than meaningful place. EO’s place property has a range of wgs84:SpatialThing, which is also defined
in terms of spatial extent.
Only DUL makes an explicit place/space distinction between Place and SpaceRegion. An event instance
can be related to a Place via the hasLocation property, or related to a SpaceRegion via the hasRegion
property. This is the most flexible approach, as it allows one to make assertions about events that occurred
in places not easily resolvable to geospatial coordinate systems. For example, scholars of ancient history may
work with documents that do not distinguish between real and mythical events. These scholars may wish
to indicate that some event is recorded as having occurred at a mythical place. Similar problems are posed
by contemporary events which may occur at virtual places such as those found within massive multi-player
online environments. In both cases it is convenient to be able to associate events to such places without
having to specify geospatial coordinates for them.
Furthermore, making a clear distinction between places and spatial regions enables one to deal properly
with the phenomenon of places changing their absolute spatial location over time. W. G. Sebald tells the
story of the town Dunwich in England, which, due to erosion, steadily relocated westward [10]. An event
that occurred in the thriving seaport of 12th century Dunwich took place in a spatial location that is now
several meters beneath the sea, well to the east of an event that occurred in the contemporary village of
Dunwich. Yet, one might like to capture the fact that the locations of these two events are linked by the
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continuously existing place called Dunwich. Unless we can distinguish between the place called Dunwich
and the geospatial locations it has occupied at different times, such nuance is not possible.
2.5 Participation in Events
While the time and place dimensions are crucial for describing events, we are usually interested in events
because of the people or organizations or things involved in them. Thus the various event ontologies provide
properties for linking agents and things to events.
2.5.1 Object Involvement in Events
ABC defines two types of properties for relating an Event to a tangible thing (an Actuality in ABC
parlance). The involves property does not imply anything beyond simple involvement. The hasResult
property relates an Event to a tangible thing or attribute of a thing which exists as a result of that Event.
ABC also defines various sub-properties of these two properties that further specialize these meanings. For
example destroys is a specialization of involves implying that the involved Actuality ceased to exist as
a result of its involvement in the Event.
CIDOC defines a property P12.occurred in the presence of, which like ABC’s involves relates an
E5.Event to a E77.Persistent Item (endurant) without committing to any implied role for that item
beyond simple involvement. P12.occurred in the presence of is the root of a hierarchy of properties ex-
pressing more specialized forms of involvement such as P25.moved and P31.has modified. Unlike ABC’s
Actuality, CIDOC’s E77.Persistent Item encompasses not only tangible entities but also intangible
concepts or ideas, making CIDOC’s P12.occurred in the presence of a broader concept than ABC’s
involves. DUL defines a hasParticipant for relating an Event to an Object. Like E77.Persistent Item,
DUL’s Object includes social and mental objects as well as physical ones. EO’s factor property, having no
range defined, is similarly broad. EO also defines a product property that, like ABC’s hasResult, links an
Event to some thing that exists as a result of that Event.
2.5.2 Agent Participation in Events
ABC defines a hasPresence property for weakly asserting that an agent was present at an event without im-
plying that the agent took an active role. It is specialized by the hasParticipant property, which does imply
an active or causal role for the agent. CIDOC’s equivalent of ABC’s hasPresence is P11.had participant,
and its equivalent of ABC’s hasParticipant is P14.carried out by. DUL’s involvesAgent property is a
specialization of hasParticipant for relating an Event to an Agent. EO provides the agent property for
the same purpose.
F stands apart from the other ontologies in the tools it offers for modeling participation in events. Using
DUL, one can assert that a given object or agent participated in an event. F uses the descriptions and
situations (DnS) pattern[4] to enable a further classification of this participation as an instance of some
role-based class. For example, using DUL one might state that the agents Brian Boru and Ma´el Mo´rda mac
Murchada participated in the Battle of Clontarf. Using F, one can further state that the Battle of Contarf
is classified as a battle, that battles have commanders, and that Brian and Ma´el Mo´rda are classified as
commanders.
CIDOC’s P14.1 in the role of property provides some limited support for classifying an agent’s par-
ticipation in an event as an instantiation of a particular role. However, since it is defined as a property of
the P14.carried out by property, it requires the use of OWL Full. Furthermore, there does not seem to be
a way to associate roles with generic event schemas in the manner described above.
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2.6 Events, Influence, Purpose and Causality
Event models vary widely in their approaches to modeling relations of causality, purpose, or influence.
Both EO and CIDOC provide properties for making very broad assertions linking events to any relevant
thing (tangible or not). CIDOC defines P15.was influenced by, while EO defines factor. Note that EO
does not distinguish between a thing’s participation in an event and a thing’s influence upon an event,
using the same property for both relations. Likewise, it seems that the only difference between CIDOC’s
P12.occurred in the presence of and P15.was influenced by is whether the relevant thing was physi-
cally present (and, by implication, a E77.Persistent Item). The only support that ABC offers for making
assertions about causality is the hasResult property, discussed above.
In historical discourse, there is often a lack of consensus about relations of causality, purpose, or in-
fluence. Thus simple properties like these are unlikely to to be adequate for modeling assertions about
such relations. Here the F model’s DnS pattern provides a far more powerful and flexible modeling tool.
Unlike the other models, F takes the position that only other events can stand in causal relation to an
event. Rather than directly linking events via a property expressing causality, events are included in an
EventCausalitySituation. The EventCausalitySituation includes not only the events being classified
as the cause and the effect, but also the theory under which causality is being asserted. Using the F model’s
interpretation pattern, one can assert that a given EventCausalitySituation is part of a specific interpre-
tation of an event. Thus multiple, potentially conflicting causality relations can be asserted for the same set
of events by specifying the interpretive context in which the relations are made.
2.7 Events, Parts and Composition
Often, it is desirable to model an event A as being part of some other event B. While an event A’s being
part of event B implies that event B ’s timespan contains event A’s timespan, event parthood is more than
temporal containment. One may get married during the Olympics, but that does not make one’s marriage
part of the Olympics. Thus, event ontologies must distinguish between mere temporal containment and
mereological relationships between sub-events and some greater event. Ontologies that make a distinction
between temporal spans and events can clearly distinguish between the two types of relationships, as the
former apply to time spans while the latter apply to events.
CIDOC distinguishes between time-spans and periods/events, and provides the P86.falls within prop-
erty to express containment relations among the former and the P9.consists of property to express part-of
relationships among the latter. EO defines a sub event property, and ABC defines an isSubEventOf prop-
erty for expressing mereological relationships among events. Since ABC conceptualizes events as consisting
of sets of actions taken by specific agents, it also provides the hasAction property for linking events to the
actions they contain.
DUL defines two properties for linking events to sub-events: hasPart and hasConstituent. hasPart
can be used both for temporal containment relationships such as “the 20th century contains year 1923” and
for semantic relationships such as “World War II included Pearl Harbour”. dul:hasConstituent attempts
to capture the notion that we sometimes model aspects of the world as consisting of layers at different levels
of abstraction, which are not strictly parts of one another. Thus society is constituted of individual people,
even though you might not want to say that people are “parts” of society because people and societies exist
at different levels of abstraction. This distinction is useful for events as well, as it allows us to describe a
large and complex event like the French Revolution as being constituted of many smaller events, even though
these smaller events may not be “parts” of the larger event in the same sense that a set is part of a tennis
match.
In keeping with its use of the DnS pattern, F enables one to define a high-level description of how an
event can be composed of smaller events. Specific situations (i.e. specific groups of events) can then satisfy
this description. This allows one to simply describe the conditions under which an event is considered to
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be part of another event, and infer parthood based on this description, rather than requiring parthood
to be explicitly asserted every time. For large events that may contain large numbers of sub-events, this
could be quite useful. And, of course, F’s interpretation pattern allows for multiple, potentially conflicting
decompositions of the same event.
3 Towards a Linked Data Event Model
Having thoroughly reviewed the various event ontologies, we now propose a model for encapsulating the
overlap among them and enabling interoperability between them. The proposed model is intentionally
minimal, representing the shared properties of the best models reviewed above rather than attempting to
include all the features each individual model provides. It contains mainly a list of formal axioms asserting
the mappings between the event models reviewed.
Our goal is to enable interoperable modeling of the “factual” aspects of events. Factual aspects can be
characterized in terms of the four Ws : What happened, Where did it happen, When did it happen, and Who
was involved. These links among events, people, places, and times are those relations about which a stable
consensus has been reached. Their defining characteristic is that most people agree that they occurred.
Whether such relations are considered to be empirically given or rhetorically produced will depend on one’s
epistemological stance. That should not prevent them from being used as a way to link people, things and
activities to particular times and places, allowing users to navigate from resources that depict or describe
any of these elements to resources that depict or describe any other of these elements.
Factual relations within and among events are intended to represent intersubjective “consensus reality”
and thus are not necessarily associated with a particular perspective or interpretation (though they must
be amenable to change over time as consensus changes). Thus we have purposefully excluded properties for
categorizing events or for relating them to other events through parthood or causal relations. We believe
that these aspects belong to an interpretive dimension best handled through the DnS approach of the F
event model.
The Table 3 shows the main properties of our model, aligned with approximately equivalent proper-
ties from the models discussed above. Note that this table is only approximate, as it conflates equivalent
properties and sub-properties. For the actual equivalence relations, see the ontology itself available at
http://linkedevents.org/model/.
ABC CIDOC DUL EO LODE
atTime P4.has time-span isObservableAt time atTime
P7.took place at place inSpace
inPlace hasLocation atPlace
involves P12.occurred in the presence of hasParticipant factor involved
hasPresence P11.had participant involvesAgent agent involvedAgent
Table 3: Excerpt of approximate mappings between properties from various event models
3.0.1 Agentivity.
In keeping with our goal of modeling only intersubjectively agreed-upon “facts” about events, our model is
agnostic with regard to judgements of aspect or agentivity (see Section 2.2). Users are free to model historical
or reported events without taking a position on what has changed or where agency lies. This agnosticism has
consequences for mapping our Event class to those defined by other models. We consider our Event class
to be directly equivalent to those defined by EO and DUL, as both of these are also agnostic with respect
to aspect and agentivity. But our event class is not equivalent to the E5.Event class since CIDOC defines
E5.Event to exclude ongoing states, activities, or processes. Because we wish to support the modeling of such
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static entities as events, we define our Event class to be a subclass of CIDOC’s E2.TemporalEntity, which
is the superclass of E5.Event (via E4.Period) and E3.Condition State. Our Event class is a subclass of
E2.TemporalEntity because the latter is defined as “anything that happens over a limited extent in time”,
which is more general than the definition we wish to give. Specifically, we want to restrict our definition to
only include those things happening over a limited extent in time that have been reported as events by some
agent, e.g. a historian or journalist.
3.0.2 Time.
Like most of the other event models, we link events to ranges of time via instances of a temporal interval
class. Like EO, we use TemporalEntity from OWL TIME as our temporal interval class, so our atTime
property is directly equivalent to EO’s time property. atTime is a subclass of DUL’s isObservableAt
property, as it restricts the domain of the latter to include only events. Likewise, atTime is a sub-property
of CIDOC’s P4.has time-span because it restricts the domain of the latter to include only events (as we
define them here) rather than any temporal entity (recall that our event class is a subclass of CIDOC’s
E2.TemporalEntity). Note that we also define atTime to be an OWL FunctionalProperty, meaning that
an event can associated with at most one interval of time. Where there may be disagreement about the
interval of time associated with an event, this disagreement should be modeled at an interpretive level beyond
the scope of our model, and the value of atTime should either be specified as the shortest temporal interval
that includes the conflicting interpretations, or left unspecified.
3.0.3 Space.
We follow DUL in making an explicit distinction between abstract spatial regions and semantically significant
places. Our inSpace property relates an event to some subjectively imposed spatial boundaries, i.e. a region
of space. Like atTime, inSpace is a FunctionalProperty, so an event can be related to at most one such
region of space. inSpace is a sub-property of DUL’s hasRegion because it restricts its domain to include only
events, not all entities, and because it restricts its range to include only spatial regions, not any dimensional
space. In keeping with EO, we use SpatialThing from the Basic Geo (WGS84 lat/long) Vocabulary as our
spatial region class, so our inSpace property is directly equivalent to EO’s place property. Because our
concept of an event is broader than the one defined by the CIDOC CRM, inSpace is a super-property of
CIDOC’s P7.took place at.
The range of inSpace is an abstract spatial extent. But often it is more convenient or desirable to
express relationships to socially defined places rather than to physically defined spaces. Neither EO nor
CIDOC provides a property for expressing such relationships. We have defined an atPlace property as a
more flexible way to associate an event with some meaningful place(s), whether or not it is possible to define
spatial boundaries for those places. Unlike inSpace, atPlace is not a FunctionalProperty, so an event can
be related to any number of places. atPlace is a sub-property of DUL’s hasLocation property, because it
restricts the latter such that the domain includes only events and the range includes only places (not any
entity).
3.0.4 Participation.
Like DUL, we define a property for linking events to arbitrary things (involved) and a single specialization
of this property for linking events to agents (involvedAgent). These two properties are directly equivalent
to DUL’s hasParticipant and involvesAgent, respectively. They are roughly equivalent to CIDOC’s
P12.occurred in the presence of and P11.had participant (though not directly equivalent given our
broader event concept). The mapping to EO is more complicated. involved is more specific than EO’s
factor property because it restricts the range of the latter to include only objects and not, for example,
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“abstract causes.” But it is also more general, because it does not imply (as factor does) a “passive”
role for the involved object. Thus there is no formal equivalence relationship stated between the two.
involvedAgent is a super-property of EO’s agent property because it generalizes the latter to include all
relations to agents, whether or not their role is “active” or “passive.” Judgments of activity or passivity are
higher-level interpretations of an event that we feel ought to be kept out of a basic event model.
3.0.5 Causality.
Finally, as discussed above, our model contains no properties for expressing relations of influence, purpose,
or causality. Therefore, there are no properties equivalent to CIDOC’s P15.was influenced by or EO’s
factor. Similarly, we provide no properties for expressing parthood relations among events. We believe
these higher-level interpretations are best handled via a layer of descriptions and situations over the basic
statements expressible using our model. The F event model provides an exemplary blueprint.
4 Applications
For demonstrating the usefulness of our proposed model for describing linked description of events, we
setup two experiments. First, we extract events from Wikipedia Timelines in order to test whether we
can represent accurately these events in the Web of Data (Section 4.1). Second, we load existing instances
of events represented according to the various event models reviewed in this paper in order to test the
interoperability we claim our model brings (Section 4.2). We provide an interface for searching, browsing
and visualizing all these events at http://www.linkedevents.org.
4.1 Extracting Events from Wikipedia Timelines
The events found in Wikipedia timelines vary widely in scope and domain, making them a good challenge for
modeling. Furthermore, we aim at demonstrating that Wikipedia timelines provide another potential source
of structured data not yet tapped by projects such as DBpedia and Freebase that provides already some
Wikipedia’s information as structured data. Finally, since timelines on related topics are spread throughout
Wikipedia, extracting their events and modeling them as linked data is useful for enabling aggregated views
of these events and for exploring related topics.
Timelines appear in Wikipedia in two major forms. Dedicated topic-specific timeline articles, such as
“Timeline of historic inventions”, take the form of a list or table of events. As of October 2008, there were
approximately 1000 such articles in Wikipedia. The list or table of events is usually divided into temporal
groups (e.g. September 1939 or 12th century) by subheadings. Each event consists of (at a minimum) a date
and a short description. The description generally contains words or phrases linked to other articles in the
typical Wikipedia manner. The second form of timeline found in Wikipedia is date-specific timeline articles,
such as “1996 in Ireland”. In addition to short lists of events in the form described above, these articles
usually also include some type-specific lists of events such as births, deaths, and sporting events that took
place in that year. The most general form of this type of article is the “Year” article (e.g. “1979”). Uses of
a given year in any Wikipedia article are usually linked to the corresponding “Year” article. Similarly, uses
of a given day of the month (e.g. “May 24”) are usually linked to the corresponding “Month Day” article.
These two types of article are highly mutually interlinked.
Date-specific timeline articles have a more standard format, making them more amenable to the extraction
of structured data. But the events in date-specific timelines rarely have anything in common other than
the year or day of the month with which they are associated. Since we were interested in linking events
to one another via places, people, and other topics, we decided to focus on topic-specific timeline articles.
Unfortunately, the formats for topic-specific timeline articles vary widely, making it difficult to create a
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generic parser and scrapper. Many topic-specific timelines add additional fields for each event. For example,
the “Timeline of Chinese history” includes a field for ruler or Emperor as well as the standard date and
description. Other timelines group events in idiosyncratic ways, such as the “Timeline of punk rock” which
categorizes the events of each year into “Bands formed”, “Disbandments”, “Albums [released]”, and “Singles
[released]”. Furthermore, the timelines vary in the temporal granularity of their events: while some timelines
specify specific days for their events, others only specify months or years. These variations illustrate how
the structure of events can vary according to the topical context and the need for a flexible data model to
accommodate them.
To populate instances of our event model, we wrote article-specific parsers for a number of the most active
timeline articles. The parsers identify individual event entries within articles and from each entry extract
the date and textual description. The parsers also extract the article subheading under which each entry
appears for two reasons. First of all, the date specified in an entry is often given relative to the subheading.
For example, events listed under the subheading September 1939 may only specify a day of the month, with
the month and year left implicit. Second, the subheadings provide a convenient means of linking back to the
specific article section from which the event was extracted.
After the article-specific extraction, we use the extracted dates and descriptions to model our events.
Dates are modeled using OWL-Time and linked to the event using the atTime property. Links to other
Wikipedia articles found within the descriptions are used to identify other entities related to the event. We use
various type ontologies from DBpedia to determine what type of relation to create between an event and an-
other entity. For example, suppose an event has the description “Canada declares war on Germany” and the
word “Canada” is linked to the Wikipedia article of the same name. We can then look up the corresponding
resource in DBpedia (http://dbpedia.org/resource/Canada) and see what types have been assigned to it.
In this case, http://dbpedia.org/resource/Canada has the type http://dbpedia.org/ontology/Place
assigned to it, so we relate it to our event instance with the atPlace property. If DBpedia does not assign
any usable types to the entity, we default to creating an involves relation.
Our initial set of events were extracted from four Wikipedia timelines:
• “Timeline of World War II” is spread over seven year-specific timeline articles and provides events
involving people and places around the world at the granularity of single days.
• “Timeline of Irish History” provides events from a single geographic location spread over a wide tem-
poral range, from the Stone Age to present day.
• “Timeline for the day of the September 11 attacks” provides a set of 147 very fine-grained events from
a single day.
• Finally, “Timeline of evolution” tested our ability to model very coarse-grained events associated with
times far in the past.
All the events extracted from these timelines, as well as the events discussed in the following section, can
be explored at http://www.linkedevents.org/. This site provides an interface for searching and browsing
over our modeled events and exploring their relations to people, organizations, places, and times.
4.2 Interoperability with Legacy Event Collections
To evaluate the mappings between our model and other vocabularies, we combined our Wikipedia events
with two collections of events modeled using other event vocabularies: the C4DM Event Ontology and the
BIO6 vocabulary for biographical information. The goal was to be able to browse and view event descriptions
using Cliopatria, a generic semantic search web-server[14]. We defined views and facets only in terms of our
event model but rely on our mappings to translate the legacy event collections to these views.
6http://vocab.org/bio/0.1/
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4.2.1 Congressional Biographies.
The Biographical Directory of the United States Congress provides short biographical articles on every
member of the United States legislature from 1774 to the present. Each article is structured as a series of
statements describing life events, and their highly standardized style makes them amenable to automatic
extraction and modeling of these events. In an earlier project we extracted 69,228 events from these articles
and modeled them using the BIO vocabulary.
4.2.2 The Emma Goldman Chronology.
The Emma Goldman Papers editors, like many editors of historical papers, maintain a day-by-day chronology
detailing where Emma Goldman and her associates were and what they were doing. This chronology serves
as an internal reference tool for the project, allowing the editors to make inferences about when or where
documents may have been produced and to check for inconsistencies in historical accounts. Starting with a
text document containing the chronology for the years 1910 through 1916, we produced an RDF data set
by parsing dates, geocoding place names, and disambiguating personal names by linking them to DBpedia.
These 1 041 Emma Goldman events were modeled using the C4DM Event Ontology.
4.2.3 Issues Mapping Between Vocabularies.
To combine these legacy event collections with our Wikipedia events we used the mappings defined be-
tween our event model and the BIO and EO vocabularies. We found that our mappings were not suffi-
cient to achieve our goal of using a single generic view to browse all three data sets, as there is not yet
widespread support for these predicates. This is due to a lack of support for the owl:equivalentClass
and owl:equivalentProperty predicates, upon which our mappings rely. However, we were able to achieve
our goal by making additional mapping statements using rdfs:subClass and rdfs:subProperty. These
mappings are conceptually not as clear as we would like since they assert inheritance rather than equivalence,
but they enable us to work with multiple event collections as a unified whole without re-modeling. We look
forward to more widespread support for the OWL predicates so that we can achieve the same results with
conceptually clearer mappings.
5 Conclusion and Future Work
There are a tremendous amount of timeline and chronology data on the web, including a number of sites
offering tools with which users can build timelines of historical events. There is also increasing interest in
mining descriptions of historical events from narrative text, whether for temporal visualization of search
results or for exploration of archival records. Finally, historians and journalists are increasingly interested
in presenting their work as structured data complementary to or in lieu of traditional narrative text. Yet,
without some effort to bridge the various data models being developed and employed within these various
applications, it will remain difficult to build the dense network of relations among them that could lead
to new discoveries or novel modes of experiencing historical narrative. In this paper, we have presented a
principled model for linking event-centric data that draws upon a close analysis of existing event ontologies.
Our initial investigations show that it is useful for modeling a variety of timeline events and for mapping
between events modeled using other vocabularies.
A number of questions remain to be answered. We have argued that a core event model should include
only those relations about which a stable consensus has been reached, leaving more interpretive relations
to a higher-level, application-specific models. But further application experience is needed before we can
determine whether we have correctly identified those relations that are intersubjectively stable, or whether
(for example) participation relations are interpretation-specific and ought to be moved outside the core
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model. A related problem is the question of event identification. In the applications discussed above, an
event is identified with a single textual description. We have made no attempt to map multiple textual
descriptions to the “same” event identifier. The reason for this is that it is not clear when (if ever) we
should consider two textual descriptions to be of the “same” event. If we consider (as many contemporary
philosophers of history do) events to be linguistic phenomena rather than objectively existing in the past,
then there is no basis for arguing that two textual descriptions of an event refer to the same thing. At best
we could say that they share a name, or that they refer to the same people, places, or spans of time. On the
other hand, we clearly would like to say that two descriptions of past occurrences only differing in spelling
or punctuation are the same event. These are deep philosophical questions about the nature of events that
will likely only be answerable pragmatically, as we see which approaches are or are not useful for specific
applications.
In future work, we plan on finding and working with more event collections modeled using the other on-
tologies discussed here, and putting these collections to use in a variety of applications. Current applications
in development include event-centric searching and browsing of full-text historical scholarship, retrieval and
display of historical context for documents by querying for related events, and interfaces for exploration,
visualization, and comparison of events from a particular period or region.
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