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LODE: Linking Open Descriptions of Events
Ryan Shaw1, Rapha¨el Troncy2,3and Lynda Hardman3
1University of California, Berkeley, USA, <ryanshaw@ischool.berkeley.edu>
2EURECOM, Sophia Antipolis, France, <raphael.troncy@eurecom.fr>
3CWI, Amsterdam, The Netherlands, <lynda.hardman@cwi.nl>
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 event models, looking at the different choices they
make of how to represent events. We describe a model for publishing
records of events as Linked Data. We present tools for populating this
model and a prototype “event directory” web service, which can be used
to locate stable URIs for events that have occurred, provide RDFS+OWL
descriptions and link to related resources.
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 Data4effort 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 phenom-
ena 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 cal-
endars and datebooks). Various standards and formats have been proposed for
representing the latter as structured data, usually for personal information man-
agement purposes. In this paper, we focus on the former category: phenomena
that have happened in the past.
This paper makes two contributions. First, we compare existing models for
representing historical events (Section 2). These models serve different communi-
ties 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
4http://linkeddata.org/
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”5web 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 1). In
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
Fhttp://events.semantic-multimedia.org/ontology/2008/12/15/model.owl
OpenCYC Ontology http://www.opencyc.org/
Table 1. Ontologies for representing events
this section, we present an analysis based on their main constituent properties:
type (Section 2.2), time (Section 2.3), space (Section 2.4), participation (Sec-
tion 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 [9].
2.1 Event Models Overview
Though all of the ontologies presented in Table 1 provide classes and properties
suitable for representing events, they were created to serve different purposes.
The CIDOC CRM [2] and ABC [6] ontologies aim at enabling 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) [7] 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 or sound generation, there is nothing spe-
cific to the music domain. It is currently the most commonly used event ontology
in the Linked Data community. EventsML-G2 has been developed by the Inter-
national Press Telecommunications Council (IPTC) for exchanging structured
5We provide an interface for searching and browsing linked descriptions of events at
http://www.linkedevents.org
information about events among news providers and their partners. It describes
both planned, past or breaking events as reported in the news.
DOLCE+DnS Ultralite (DUL) is a lightweight “upper” ontology for ground-
ing 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 so-
cial reality [3]. 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 correlations between
events. F also provides the ability to assert that multiple models represent views
upon or interpretations of the same event [8]. OpenCYC is also an “upper” on-
tology, 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 vary-
ing ways. Table 2 provides a comparison of the prose descriptions for the top-level
event classes. Furthermore, all of these ontologies, with the exception of EO,
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 cer-
tain properties or bearing certain relations to each other.”
Table 2. Definitions of top-level event-related classes
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 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 between two
types of E2.Temporal Entity:E3.Condition State and E5.Event. It is less
clear because CIDOC also introduces the concept E4.Period, a type of tempo-
ral entity that is not static, but does not necessarily involve a change of state.
E3.Condition State is also defined narrowly to denote only 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, in-
stances 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 distinction is whether an agent is identified as having produced
the event. 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 en-
gineer 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 agen-
tivity 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 Event6.
2.3 Events and Temporal Intervals
Temporality is a major distinguishing feature of events as entities, requiring
modeling spans of time and relating events to these. The relationship between
6DUL 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.
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. One approach to linking events to ranges
of time uses datatype properties, directly relating event instances with RDF
literals representing calendar dates (and thus typed using one of the date-related
XML Schema datatypes such as xsd:date or xsd:dateTime). Another 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 classes for temporal intervals, and EO using the TemporalEntity
class from OWL-Time [5]. DUL allows both approaches: 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 a
temporal calculus for reasoning about these more complex relationships. For
example, if the precise date of a historical event is not known but some bound-
aries 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 semantically significant places. ABC, CIDOC and EO only
support linking to spatial regions. CIDOC provides a class (E53.Place) for “ex-
tent 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 ex-
tent. 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 dis-
tinguish between real and mythical events. These scholars may wish to indicate
that some event is recorded as having occurred at a mythical place. Similar prob-
lems 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 named places and spatial regions enables one to deal properly with the
phenomenon of places changing their absolute spatial location over time.
2.5 Participation in Events
The event ontologies also provide properties for linking agents, such as people
and organizations, and the things involved in them.
Object Involvement in Events ABC defines two types of properties for relat-
ing 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 ex-
ists 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 involve-
ment. P12.occurred in the presence of is the root of a hierarchy of proper-
ties expressing 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 CIDOC’s 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.
Agent Participation in Events ABC defines a hasPresence property for
weakly asserting that an agent was present at an event without implying 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 what it offers for modeling par-
ticipation. Using DUL, one can assert that a given object or agent participated
in an event. F uses the descriptions and situations (DnS) pattern[3] 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 M´ael
M´orda 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 M´ael M´orda are classified as commanders.
CIDOC’s P14.1 in the role of property provides some support for classi-
fying an agent’s participation 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.
2.6 Events, Influence, Purpose and Causality
Event models vary in their approaches to modeling relations of causality, pur-
pose, or influence. Both EO and CIDOC provide properties for making broad
assertions linking events to any relevant thing (tangible or not). CIDOC de-
fines P15.was influenced by, while EO defines factor. EO does not distin-
guish 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 physically present
(and, by implication, a E77.Persistent Item). The only support that ABC
offers for making assertions about causality is the hasResult property.
In historical discourse there is often a lack of consensus about causality, pur-
pose, or influence. Thus simple properties like these are unlikely to be adequate
for modeling assertions about such relations. Here the F model’s DnS pattern
provides a 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 interpre-
tation pattern, one can assert that a given EventCausalitySituation is part
of a specific interpretation 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 Aas being part of some other event
B. While an event A’s being part of event Bimplies that event B’s timespan
contains event A’s timespan, event parthood is more than temporal contain-
ment. 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.
CIDOC distinguishes between time-spans and periods/events, and provides
the P86.falls within property to express containment relations among time-
spans, and the P9.consists of property to express part-of relationships among
events. EO defines a sub event property, and ABC defines an isSubEventOf
property for expressing mereological relationships among events. Since ABC con-
ceptualizes events as 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 relation-
ships such as “the 20th century contains year 1923” and for semantic relation-
ships 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 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
We propose a minimal model that encapsulates the most useful properties of the
models reviewed. Our goal is to enable interoperable modeling of the “factual”
aspects of events, where these can be characterized in terms of the four Ws:
What happened, Where did it happen, When did it happen, and Who was
involved. “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. Our model thus allows us to express
characteristics about which a stable consensus has been reached, whether these
are considered to be empirically given or rhetorically produced will depend on
one’s epistemological stance. We exclude 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.
Table 3 shows the main properties of our model, aligned with approximately
equivalent properties from the models discussed above. For the actual equiv-
alence relations, see the ontology itself 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
Agentivity. 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 agnos-
ticism 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. 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 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.
Time. 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 inter-
val 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 do-
main 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). We
also define atTime to be an OWL FunctionalProperty, meaning that an event
can be associated with at most one interval of time. Where there may be dis-
agreement 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.
Space. We follow DUL in making an explicit distinction between abstract spa-
tial 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 equiv-
alent 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. While the range of inSpace is an abstract spatial extent, it
is often desirable to express relationships to socially defined places. We define an
atPlace property 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).
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 be-
cause it restricts the range of the latter to include only objects and not, for
example, “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 in-
clude all relations to agents, whether or not their role is “active” or “passive.”
Judgments of activity or passivity are higher-level interpretations that go beyond
our goal of modeling only “factual” aspects.
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. Sim-
ilarly, we provide no properties for expressing parthood relations among events.
We believe these higher-level interpretations are best handled via a layer of de-
scriptions 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, we set up two experi-
ments. First, we extract events from Wikipedia timelines in order to test whether
we can represent these events accurately in the Web of Data (Section 4.1). Sec-
ond, 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, mak-
ing them a good challenge for modeling. We also demonstrate that Wikipedia
timelines provide a source of structured data not yet tapped by projects such as
DBpedia7and Freebase8. 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 tempo-
ral 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 gen-
erally contains words or phrases linked to other articles in the typical Wikipedia
manner. The second form of timeline found in Wikipedia is date-specific time-
line 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
7http://dbpedia.org/
8http://freebase.com/
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 generic parser and
scraper. 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 granu-
larity 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 descrip-
tions 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 type ontologies from DBpedia to determine what type of relation to
create between an event and another entity. For example, if an event has the de-
scription “Canada declares war on Germany” and the word “Canada” is linked
to the Wikipedia article of the same name, we then look up the corresponding
resource in DBpedia (http://dbpedia.org/resource/Canada) and see what
types have been assigned to it. http://dbpedia.org/resource/Canada has the
type http://dbpedia.org/ontology/Place assigned to it, so we relate it to our
event 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” provides seven year-specific timelines of global
events involving people at the granularity of single days.
–“Timeline of Irish History” provides events from a single geographic location
spread over a wide temporal 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.
–“Timeline of evolution” tested our ability to model very coarse-grained events
associated with times far in the past.
4.2 Interoperability with Legacy Event Collections
To evaluate the mappings between our model and other vocabularies, we com-
bined our Wikipedia events with two collections of events modeled using other
event vocabularies: the C4DM Event Ontology and the BIO9vocabulary for
biographical information. The goal was to be able to browse and view event de-
scriptions using Cliopatria, a generic semantic search web-server [12]. 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.
Congressional Biographies. The Biographical Directory of the U.S. Congress
provides short biographical articles, as a series of statements describing life
events, on every member of the United States legislature from 1774 to the
present. The consistent structure allows simple extraction and modeling of events.
In earlier work 69,228 events were modeled using the BIO vocabulary.
The Emma Goldman Chronology. The Emma Goldman Papers editors
maintain a day-by-day chronology detailing where Emma Goldman and her as-
sociates were and what they were doing. This chronology serves as an internal
reference tool, allowing the editors to make inferences about when or where doc-
uments may have been produced and to check for inconsistencies in historical
accounts. Starting with a text document for the years 1910 through 1916, we
produced an RDF data set by parsing dates, geocoding place names, and dis-
ambiguating personal names by linking them to DBpedia. These 1,041 Emma
Goldman events were modeled using the C4DM Event Ontology.
Issues Mapping Between Vocabularies. To combine these legacy event col-
lections with our Wikipedia events we used the mappings defined between our
event model and the BIO and EO vocabularies. We found that our mappings were
not sufficient to achieve our goal of using a single generic view to browse all three
data sets, as there is not yet widespread support for the owl:equivalentClass
and owl:equivalentProperty predicates, upon which our mappings rely. How-
ever, we were able to achieve our goal by making additional mapping statements
using rdfs:subClass and rdfs:subProperty. These mappings enable us to
work with multiple event collections as a unified whole without re-modeling.
9http://vocab.org/bio/0.1/
5 Conclusions and Future Work
There is a tremendous amount of timeline and chronology data on the web.
There is also increasing interest in mining descriptions of historical events from
narrative text, whether for temporal visualization of search results or for explo-
ration of archival records. Historians and journalists are increasingly interested
in presenting their work as structured data complementary to or in lieu of tradi-
tional 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 con-
sensus has been reached, leaving more interpretive relations to a higher-level,
application-specific models. But further application experience is needed be-
fore 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 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 ob-
jectively 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 ontologies 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.
6 Acknowledgments
The research leading to this paper was supported by the European Commission
under contract FP6-027026, Knowledge Space of semantic inference for auto-
matic annotation and retrieval of multimedia content – K-Space, and by the
U.S. Institute of Museum and Library Services under a National Leadership
Grant for Libraries (award number LG-06-06-0037-06).
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