=Paper=
{{Paper
|id=Vol-2829/paper5
|storemode=property
|title=OEKG: The Open Event Knowledge Graph
|pdfUrl=https://ceur-ws.org/Vol-2829/paper5.pdf
|volume=Vol-2829
|authors=Simon Gottschalk,Endri Kacupaj,Sara Abdollahi,Diego Alves,Gabriel Amaral,Elisavet Koutsiana,Tin Kuculo,Daniela Major,Caio Mello,Gullal S. Cheema,Abdul Sittar,Swati,Golsa Tahmasebzadeh,Gaurish Thakkar
|dblpUrl=https://dblp.org/rec/conf/www/GottschalkKAAAK21
}}
==OEKG: The Open Event Knowledge Graph==
https://ceur-ws.org/Vol-2829/paper5.pdf
OEKG: The Open Event Knowledge Graph
Simon Gottschalk1 , Endri Kacupaj2 , Sara Abdollahi1 , Diego Alves3 , Gabriel
Amaral4 , Elisavet Koutsiana4 , Tin Kuculo1 , Daniela Major5 , Caio Mello5 ,
Gullal S. Cheema6 , Abdul Sittar7 , Swati7 , Golsa Tahmasebzadeh6 , and
Gaurish Thakkar3
1
L3S Research Center, Leibniz Universität Hannover, Germany
{gottschalk,abdollahi,kuculo}@L3S.de
2
University of Bonn, Germany
kacupaj@cs.uni-bonn.de
3
University of Zagreb, Croatia
dfvalio@ffzg.hr, gthakkar@m.ffzg.hr
4
King’s College London, United Kingdom
{gabriel.maia rocha amaral,elisavet.koutsiana}@kcl.ac.uk
5
School of Advanced Study, University of London, United Kingdom
{Daniela.Major,caio.mello}@sas.ac.uk
6
TIB – Leibniz Information Centre for Science and Technology, Hannover, Germany
{gullal.cheema,golsa.tahmasebzadeh}@tib.eu
7
Jožef Stefan Institute and Jožef Stefan International Postgraduate School, Slovenia
{abdul.sittar,swati}@ijs.si
Abstract. Accessing and understanding contemporary and historical
events of global impact such as the US elections and the Olympic Games
is a major prerequisite for cross-lingual event analytics that investigate
event causes, perception and consequences across country borders. In this
paper, we present the Open Event Knowledge Graph (OEKG), a mul-
tilingual, event-centric, temporal knowledge graph composed of seven
different data sets from multiple application domains, including ques-
tion answering, entity recommendation and named entity recognition.
These data sets are all integrated through an easy-to-use and robust
pipeline and by linking to the event-centric knowledge graph EventKG.
We describe their common schema and demonstrate the use of the OEKG
at the example of three use cases: type-specific image retrieval, hybrid
question answering over knowledge graphs and news articles, as well as
language-specific event recommendation. The OEKG and its query end-
point are publicly available.
1 Introduction
Contemporary and historical events such as the US presidential elections, the
Olympic Games and major earthquakes change the world. Their media coverage,
their varying perception by different communities, their historical evolution and
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 Gottschalk et al.
potentially global impact make cross-lingual event analytics a significant research
topic in various fields of studies, including social science, computer science and
digital humanities [4, 19].
When performing cross-lingual event analytics, the requirements towards
event knowledge representation are manifold, given the heterogeneity, dynamic-
ity and multilingualism of events [9]. Until now, there exists a large variety of
event-related data sets [1, 4, 11, 20] that may help understand specific character-
istics of events, but they are barely connected by now. This calls for new models
and processes that enable intuitive access to the event-related knowledge spread
across the world.
In this paper, we present the OEKG, the Open Event Knowledge Graph,
which makes a step towards a holistic representation of event knowledge by
the integration of event-related data sets from multiple and diverse application
domains such as Question Answering, entity recommendation and Named En-
tity Recognition. Also, these data sets originate from different data collections,
including knowledge graphs and news articles. One of these knowledge graphs
is EventKGlight , a new version of the event-centric and multilingual knowledge
graph EventKG [11]. The OEKG is built on top of EventKGlight , allowing for
easier integration of additional data sets using RDF named graphs. We propose
an efficient and robust pipeline facilitating this integration of several data sets
in an easy-to-use manner.
Fig. 1 shows four example resources of OEKG and thus demonstrates its
versatility resulting from the integration of several data sets:
– Events (Fig. 1a): Events are at the core of the OEKG. For example, the fire
of the Notre-Dame in Paris is covered with its locations, labels in multiple
languages, related events such as “The Notre Dame Cathedral holds its first
mass since the April 15 fire”, and more event characteristics.
– Places (Fig. 1b): Most events happen at specific event locations which are
also part of the OEKG. Such places do not only hold labels and coordinates,
but also images and further characteristics.
– News articles (Fig. 1c): Events are often reported in the media [5,13]. There-
fore, the OEKG provides access to annotated news articles. For example, the
news article entitled “Boris Johnson takes charge of Olympic Park’s future”
is related to the Olympic Games.
– Questions and answers (Fig. 1d): Question Answering over knowledge graphs
is an important natural language understanding task. The OEKG provides
questions about events such as the Apollo 11 spaceflight, plus their answers
(here, Neil Armstrong, Michael Collins and Buzz Aldrin).
Furthermore, the OEKG covers several other event-related aspects, including
but not limited to (temporal) event relations, language-specific relevance scores
and specialised class hierarchies. Put together, this makes the OEKG a versatile
resource targeting a variety of potential information needs.
OEKG: The Open Event Knowledge Graph 3
(a) Example event in the OEKG. (b) Example place in the OEKG.
(c) Example news article in the OEKG. (d) Example question in the OEKG.
Fig. 1. Example resources in the OEKG (not all triples are shown).8
The OEKG contains more than 400 million triples from seven data sets and
is publicly available: We provide the triple dumps for download, a SPARQL end-
point and access to all nodes on the OEKG website9 . We also provide permanent
access to the OEKG on Zenodo10 .
8
The photos of the example place are taken from Wikimedia Commons, with the
second photo being licensed under the Creative Commons Attribution-Share Alike
3.0 Unported license. The maps of the example event and the example place are
licensed under the Open Data Commons Open Database License (ODbL) by the
OpenStreetMap Foundation (OSMF).
9
http://oekg.l3s.uni-hannover.de
10
https://zenodo.org/record/4503163
4 Gottschalk et al.
The remainder of this paper is organised as follows: First, we present our
integration pipeline (Section 2). Then, we describe the data sets integrated into
the OEKG (Section 3) and the OEKG schema (Section 4). In Section 5, we
provide two example use cases of the OEKG. Finally, we conclude in Section 6.
2 Creation of the OEKG
The creation of the OEKG requires an integration pipeline where a set of data
sets is transformed into a single, integrated knowledge graph that provides links
between all the involved resources. EventKGlight – a multilingual, event-centric
knowledge graph later described in Section 3 – serves as the base data set of the
OEKG that contains nodes representing real-world entities and events.
Our integration pipeline is driven by the goal to make the inclusion of a new
data set into the OEKG as simple as possible, which allows a robust and efficient
process. Only then, it is possible to integrate a large variety of data sets in an
efficient and faultless way. To do so, we follow a strategy defined by Galkin et
al. [8] where the data from different sources is stored under respective named
graphs. Starting from EventKGlight , new data sets are added consecutively, each
accompanied by a unique named graph. Fig. 2 exemplifies this integration process
when adding the first new data set to EventKGlight , under the named graph
new graph.
new_graph event_kg
Entity Graph Graph
Linking Creation Upload
new_graph
OEKG
event_kg
Fig. 2. Example of the OEKG integration pipeline where a new, tabular, data set is
added to the OEKG under the named graph new graph.
In detail, the integration process follows the following three steps:
1. Entity Linking: We require that each graph added to OEKG is connected to
EventKGlight . That means any resource representing a real-world entity or
event is represented by an OEKG resource URI. To facilitate this linking,
we provide a web API that allows easy access to the OEKG resource URIs
given Wikidata or DBpedia URIs. In our example in Fig. 2, some input table
cells are successfully linked to EventKGlight .
OEKG: The Open Event Knowledge Graph 5
Algorithm 1 Example: Extension of the OEKG with a data set news that has
an article about Barack Obama
1: procedure ExtendOEKG(e)
2: graphName ← ”news” .
. Entity Linking
3: entityId ← getId(”en”,”Barack Obama”)
. Graph Creation
4: G ← new Graph(graphName)
5: articleId ← ”article1”
6: G.add(oekg-r:articleId, rdf:type, so:Article)
7: G.add(oekg-r:articleId, so:mentions, oekg-r:entityId)
8: fileName ← storeGraphIntoFile(G)
. Graph Upload
9: uploadGraph(fileName, graphName)
2. Graph Creation: After retrieval of the OEKG resource URIs, a set of triples
is created for each data set and serialised as an N-Triples11 file, using the
RDFLib Python library12 . In our example, a graph consisting of five nodes
is created, two of them being already part of the OEKG.
3. Graph Upload: We provide another API method that allows uploading an
N-Triples file together with the identifier of a named graph. The respective
triples are then added to the OEKG. In our example, the resulting graph
consists of two subgraphs that can be queried in isolation or together.
2.1 Example
Consider Algorithm 1 for an example of our integration pipeline. In this example,
the new data set to be added to the OEKG under the named graph news contains
one news article about Barack Obama. First, the OEKG URI of Barack Obama
is retrieved via the provided API method using the English Wikipedia label (line
3). Second, a graph is created consisting of two triples and serialised into an RDF
file (lines 4 - 8)13 . Third, this file is uploaded via the provided API method (line
9). In this example, one new node is added to the OEKG (oekg-r:articleId)
connected to an existing node (oekg-r:entityId).
2.2 Schema Extension
If possible, the data sets were transformed into triples using the EventKGlight
schema of the base graph. Otherwise, the use of standard vocabularies such as
schema.org14 was encouraged. In every other case, schema extensions were up-
loaded into the OEKG through separate schema files using the same procedure.
We will present the resulting OEKG schema in Section 4.
11
https://www.w3.org/TR/n-triples/
12
https://rdflib.dev/
13
Relevant prefixes used by the OEKG are later defined in Table 2.
14
https://schema.org/
6 Gottschalk et al.
3 Data Sets
The OEKG integrates seven data sets which are described in this section. Table
1 provides an overview of these data sets, including the number of triples in the
OEKG within their respective named graph. While some of these data sets are
implicitly related to events, others add to the event knowledge from a different
perspective, which will also prove useful as we will later show at the example of
three use cases.
Table 1. Statistics of the different data sets contained in the OEKG.
Data Set Short Description Triples
EventKGlight [11] A light-weight version of EventKG, a multilingual, event- 434, 752, 387
centric, knowledge graph.
EventKG+Click [1] A data set of language-specific event-centric user inter- 118, 662
action traces
VQuAnDa [12] A verbalization question answering dataset 38, 243
MLM [3] A benchmark dataset for multitask learning with multi- 942, 753
ple languages and modalities
InfoSpread [20] A data set for information spreading over the news 277, 992
TIME [4] Two collections of news articles related to the Olympic 70, 754
legacy and Euroscepticism
UNER [2] The universal named-entity recognition framework 206, 622
OEKG The Open Event Knowledge Graph 436, 407, 413
– EventKGlight [11]: The EventKG is a multilingual resource incorporat-
ing event-centric information extracted from several large-scale knowledge
graphs such as Wikidata, DBpedia and YAGO, as well as less structured
sources such as the Wikipedia Current Events Portal and Wikipedia event
lists in 15 languages. It contains nodes representing real-world entities and
events and (temporal) relations between them. For the OEKG, we have cre-
ated EventKGlight , a light-weight version of EventKG that omits provenance
information denoting the origin of relations, favouring an easier integration
with other data sets.
In the OEKG, EventKGlight serves as the base graph that other data sets
are connected to. That is to establish an agreement concerning the identifi-
cation of event-related real-world objects such as persons, places and events
themselves.
– EventKG+Click [1]: EventKG+Click is a cross-lingual dataset that reflects
the language-specific relevance of events and their relations and aims to
OEKG: The Open Event Knowledge Graph 7
provide a reference source to train and evaluate novel models for event-
centric cross-lingual user interaction. It directly builds upon EventKG and
language-specific information on user interactions with events, entities, and
their relations derived from the Wikipedia clickstream.
In the OEKG, EventKG+Click can be used for recommending events to
users based on actual user interaction traces. Examples of particular relevant
events from a language-specific view include the 2016 Berlin truck attack
from the German perspective and the 2009 Russian Premier League from
the Russian perspective [1].
– VQuAnDa [12]: The Verbalization QUestion ANswering DAtaset is a dataset
for Question Answering (QA) over knowledge graphs that includes the ver-
balization of each answer. Through this verbalisation, VQuAnDa intends to
completely hide any semantic technologies and provides a fluent experience
between the users and the knowledge graph. VQuAnDa consists of 5, 000
questions accompanied by SPARQL queries and DBpedia entity links.
QA over Knowledge Graphs is a common task in natural language processing
[6]. Via the integration of question/answer pairs into the OEKG, both the
question/answers pairs and the background knowledge are encapsulated into
the same resource, enabling seamless training and application of QA systems.
– MLM [3]: The Multiple Languages and Modalities data set is a resource
for training and evaluating multitask systems in multiple modalities, for
example, cross-modal (text/image) retrieval and location estimation. MLM
comprises text in three languages, images and location data, extracted from
the Wikidata entries of 236, 000 human settlements.
MLM is added to the OEKG for adding images as an additional modality to
the knowledge graph. As locations are typical event characteristics, photos
of locations are an immediate benefit to the representation of events.
– InfoSpread [20]: The data set for Information Spreading over the News
provides news articles covering three contrasting events (Global Warming,
FIFA world cups and earthquakes). Initially, the goal of this data set was
to understand information spreading patterns over news articles. InfoSpread
contains 7, 773 news articles related to these events in five languages.
News articles are often used as a means to identify events [13] and oftentimes
it is the media itself that makes events known to the public [5]. Therefore,
the inclusion of news articles into the OEKG is an important step towards
coverage of event-centric data from different viewpoints.
– TIME [4]: The temporal discourse analysis applied to media articles data
set is a collection of Brazilian, British and Spanish news articles covering the
concept of Olympic legacy and the concept of Euroscepticism.
With the collection of news articles to specified events, the OEKG serves as
an example for in-depth analysis of single events through knowledge graphs.
– UNER [2]: The Universal Named Entity Recognition framework proposes
a 4-level class hierarchy for training and testing Named Entity Recognition
tools. For example, UNER contains the class Earthquake, which is a leaf
node of the following branch of superclasses: Natural, NaturalPhenome-
non, Event and Name.
8 Gottschalk et al.
In the OEKG, UNER adds to the already given class hierarchy from the
DBpedia ontology. Given how challenging it is to recognise named events in
texts [16], we envision that the inclusion of UNER classes into the OEKG
can help training and evaluating NER systems in the specific context of
event-centric data.
Following the integration pipeline described in Section 2, the described data
sets were added to the OEKG. For additional information or increased interlink-
age with EventKGlight , some data sets were extended before:
– Via the Wikifier15 and spaCy16 , entities and events mentioned in news arti-
cles (TIME and InfoSpread ) were identified. This is to establish a connection
between the news articles and EventKGlight : Given this connection, one may
query for news articles about specific events or entities.
– Sentiment analysis, i.e., the computational study of people’s opinions, sen-
timents, emotions, moods, and attitudes [14], contributes towards the un-
derstanding of natural-language texts and can, in particular, facilitate an
analysis of news articles across languages [15]. In the OEKG, we enrich news
articles by employing the sentiment detection system SentiStrength [21] on
their headlines. That way, the OEKG enables queries for particularly pos-
itive or negative news articles, potentially initiating further event-centric
analyses of the news articles in the context of specific events.
– To further increase the linkage between different sources, the UNER classes
were aligned to the DBpedia ontology using the skos vocabulary17 when
possible.
4 Schema
Fig. 3 shows the OEKG schema. As described in Section 2.2, this schema is
based on the EventKGlight schema and then extended by demand. Prefixes used
in the OEKG schema and in the remainder of this paper are listed in Table 218 .
In detail, the different data sets contribute to the following parts of the OEKG
schema:
– EventKGlight : The EventKG schema is based on the Simple Event Model
(sem)19 and its three main classes sem:Event, sem:Actor and sem:
Place, that are connected via sem:hasPlace and (temporal) relations
modeled by oekg-s:Relation (omitted from Fig. 3 for brevity). Event-
KG further distinguishes between different types of events (oekg-s:Text-
Event, oekg-s:EventSeries and oekg-s:EventSeriesEdition). In
15
http://wikifier.org/
16
https://spacy.io/
17
https://www.w3.org/TR/swbp-skos-core-spec/
18
For a full list of prefixes used in the OEKG, see oekg.l3s.uni-hannover.de/
sparql.
19
https://semanticweb.cs.vu.nl/2009/11/sem/
OEKG: The Open Event Knowledge Graph 9
Table 2. Selected prefixes used by the OEKG.
Prefix URI
oekg-r: http://oekg.l3s.uni-hannover.de/resource/
oekg-s: http://oekg.l3s.uni-hannover.de/schema/
oekg-g: http://oekg.l3s.uni-hannover.de/graph/
uner: http://oekg.l3s.uni-hannover.de/uner/
so: http://schema.org/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
xs: http://www.w3.org/2001/XMLSchema#
sem: http://semanticweb.cs.vu.nl/2009/11/sem/
onyx: http://www.gsi.dit.upm.es/ontologies/onyx/ns#
skos: http://www.w3.org/2004/02/skos/core#
comparison to the EventKG schema, EventKGlight omits link count relations
and adds the skos:prefLabel to entities for a more efficient access to their
labels.
– EventKG+Click : To model language-specific, weighted relations for the
representation of event-centric cross-lingual user interaction, we have intro-
duced two new classes: oekg-s:LanguageSpecificRelation that as-
signs one or more instances of oekg-s:LanguageSpecificRelation-
Score to a source entity and a target entity. Such instances hold the score
between the source and target entity in a specific language.
– VQuAnDa: A question, its suggested answer and their verbalisation are
represented using schema.org’s classes so:Question and so:Answer. En-
tities that appear in the question text are linked to EventKGlight instances
via so:mainEntity, entities in the answer via so:mentions.
– MLM : Images are assigned to places via so:image, descriptions via so:
description.
– InfoSpread and TIME : News articles are represented via so:Article
and the respective properties denoting the headline (so:headline), for
instance. News articles are connected to EventKGlight instances via so:
mentions, which denote the appearance of an OEKG entity or event in
the text. For the representation of news articles’ sentiment, we follow the
schema of the TweetsKB [7], using the onyx vocabulary and its classes
onyx:EmotionSet, onyx:Emotion and onyx:EmotionCategory to
assign a set of emotions of different strengths to a news article.
– UNER: Entities are assigned UNER classes using rdf:type. Furthermore,
the UNER class hierarchy and its connection to the DBpedia ontology are
established using the owl and the skos vocabulary.
10 Gottschalk et al.
so:inLanguage
xs:language
TIME & onyx:Emotion
so:date
xs:date InfoSpread Category
Published
so:publisher onyx:hasEmo
xs:string tionCategory
VQuAnDa UNER
skos:narrower
so:head onyx:
xs:string onyx:hasEmo Emotion
xs:string line
rdfs:sub tionIntensity
ClassOf owl:Class
so:text so:url
xs:anyURI xs:double onyx:has
owl:equivalent Emotion
Class
so:Question
so
:m rdf:type
onyx:has
onyx:
so:suggested ain so:Article Emotion
En ns EmotionSet
Answer ntio Set
tity
so :me
so:Answer so:mentions sem:Core
rdf:
su
so:text skos: rdf: bject
obje /
oekg-s:lang oekg-s:
prefLabel ct oekg-s:Language
uageScore LanguageSpecific
xs:string SpecificRelation
RelationScore
xs:string sem:Actor
oekg-s:score oekg-s:
dbo:previousEvent / score
so: Language
dbo:nextEvent / Value
sem:hasSubEvent sem:Event sem:hasPlace sem:Place contained
InPlace
xs:language xs:double
so:image so:
EventKG description
EventKG+
xs:anyURI xs:string Click
oekg-s:
oekg-s: EventSeries
oekg-s:
TextEvent EventSeries
Edition MLM
Fig. 3. Excerpt of the OEKG schema. _ marks owl:subClassOf relations. Regular
arrows mark the rdfs:domain and rdfs:range restrictions on properties. Classes are
coloured w.r.t. the data set for which they have been added. For brevity, we have
omitted classes regarding relations between entities and events, as well as temporal
attributes from the EventKG schema.
5 Example Use Cases
In this section, we demonstrate the OEKG and its ability to enable integrated
access over multiple datasets via three example use cases.
5.1 Image Retrieval: EventKGlight , MLM & UNER
Event classification in images is an important task for various applications in
the fields of computer vision, including geolocation estimation and place clas-
sification [17]. Such tasks typically rely on the existence of a well-defined class
hierarchy and the availability of images. The OEKG facilitates queries both for
the UNER type hierarchy specifically designed for Named Entity Recognition,
and for images of locations, using the MLM data. In combination, event loca-
tions in EventKGlight , MLM ’s image links, and the UNER type hierarchy enable
retrieval of images relevant for specific event types.
OEKG: The Open Event Knowledge Graph 11
We demonstrate the OEKG’s potential for image retrieval by an example
query for images from earthquake regions shown in Listing 1.1: It queries for
entities typed as earthquakes using the uner:Earthquake class, their loca-
tions (EventKGlight ) and the images assigned to such locations (MLM ). Table 3
presents selected results of this query, including a photo of the port of Messina
and more.
SELECT DISTINCT ?Location ?Image WHERE {
?earthquake rdf:type uner:Earthquake ;
sem:hasPlace ?Location .
?Location so:image ?Image .
}
Listing 1.1. SPARQL query: Images of locations where earthquakes happened.
Table 3. Selected OEKG results of the SPARQL query in Listing 1.1.20
Location Ferrara Messina Guaranda
Image
5.2 Question Answering over News Articles: EventKGlight ,
VQuAnDa, InfoSpread & TIME
Question Answering (QA) is the task of supplying precise answers to questions,
posed by users in natural language, and is typically divided into QA over free
text and QA over knowledge graphs [6]. Through the integration of EventKGlight ,
VQuAnDa, TIME and InfoSpread into the OEKG, the OEKG facilitates a com-
bination of these two tasks, i.e., hybrid approaches: We can query for news ar-
ticles which specifically mention the entities part of the question/answer pair.
20
These photos are taken from Wikimedia Commons. They are licensed under the
following licenses. Ferrara: Creative Commons Attribution 2.5 Italy license. Messina:
Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic
and 1.0 Generic. Guaranda: Creative Commons Attribution 2.0 Generic license.
12 Gottschalk et al.
This way, two sources for answering the question can be provided: the OEKG
itself, as well as the news article potentially holding the answer to the initially
posed question.
For example, the query in Listing 1.2 asks for a question in VQuAnDa (?ques-
tion) that is about an event (?questionEntity rdf:type sem:Event).
The query then searches for news articles (?article) mentioning both that event
and one of the suggested answer entities. It returns the question “Whose wife is
a presenter at WWE? (en)” and its verbalised answer “The people whose part-
ners are presenters at WWE are John Cena, Dwayne Johnson.” together with
the Spanish news articles entitled “¿Qué luchador tiene el mayor porcentaje de
victorias en la historia de WWE?” (Which wrestler has the highest percentage
of victories in in the history of WWE? ). The question entity “WCE (en)” is
mentioned in the news article, as well as both answers: John Cena and Dwayne
Johnson.
SELECT DISTINCT ?questionText ?answerText ?headline
?questionEntity ?answerEntity WHERE {
?question so:suggestedAnswer ?answer;
so:mainEntity ?questionEntity ;
so:text ?questionText .
?questionEntity rdf:type sem:Event .
?answer so:mentions ?answerEntity ;
so:text ?answerText .
?article rdf:type so:Article ;
so:mentions ?questionEntity, ?answerEntity ;
so:headline ?headline .
}
Listing 1.2. SPARQL query: News articles that mention entities of a question/answer
pair.
5.3 Event Recommendation: EventKGlight & EventKG+Click
As defined by Ni et. al, entity recommendation is the problem of suggesting
a contextually-relevant list of entities in a particular context [18]. This task
is particularly relevant in Web search. With the OEKG, we can specifically
create language-specific recommendations for events and further enrich them
with relevant event characteristics.
The query in Listing 1.3 asks for events relevant to the First World War,
from the Russian point of view. We filter for the most relevant related events
(FILTER(?value >= 0.8)) and retrieve EventKGlight ’s event characteristics
OEKG: The Open Event Knowledge Graph 13
SELECT ?Label ?StartDate WHERE {
?event owl:sameAs dbr:World_War_I.
?r oekg-s:source ?event ;
oekg-s:target ?target ;
oekg-s:hasLanguageSpecificRelationScore [
oekg-s:scoreValue ?value ;
oekg-s:scoreLanguage ’ru’ˆˆxsd:language
] .
?target skos:prefLabel ?Label ;
sem:hasBeginTimeStamp ?StartDate .
FILTER(?value >= 0.8) .
}
ORDER BY ?StartDate
Listing 1.3. SPARQL query: Events related to the First World War from a Russian
point of view.
to order the resulting list of events chronologically. Table 4 lists the results of this
query, that clearly show a Russian focus. This result could be used for creating a
language-specific event timeline similar to the link-based EventKG+TL system
[10], but now inferred from actual user interaction traces in EventKG+Click .
Table 4. All OEKG results for the SPARQL query in Listing 1.3.
Label StartDate
Brusilov Offensive (en) 1916-05-22
Russian Civil War (en) 1917-11-07
Treaty of Brest-Litovsk (en) 1918-03-03
6 Conclusion
In this paper, we have introduced the OEKG – the Open Event Knowledge
Graph21 . The OEKG comprises event-related knowledge from seven data sets
of various application domains. We have presented an easy-to-use, efficient and
robust pipeline that facilitated a seamless integration of seven data sets into the
OEKG. At the examples of image retrieval, question answering over text and
event recommendation, we have exemplified three use cases of the OEKG.
Acknowledgements The project leading to this publication has received fund-
ing from the European Union’s Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie grant agreement No. 812997 (Cleopatra).
21
http://oekg.l3s.uni-hannover.de/
14 Gottschalk et al.
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