=Paper=
{{Paper
|id=Vol-2611/paper3
|storemode=property
|title=EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces
|pdfUrl=https://ceur-ws.org/Vol-2611/paper3.pdf
|volume=Vol-2611
|authors=Sara Abdollahi,Simon Gottschalk,Elena Demidova
|dblpUrl=https://dblp.org/rec/conf/esws/AbdollahiGD20
}}
==EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces==
https://ceur-ws.org/Vol-2611/paper3.pdf
EventKG+Click: A Dataset of Language-specific
Event-centric User Interaction Traces
Sara Abdollahi, Simon Gottschalk, and Elena Demidova
L3S Research Center, Leibniz Universität Hannover, Germany
{abdollahi,gottschalk,demidova}@L3S.de
Abstract. An increasing need to analyse event-centric cross-lingual in-
formation calls for innovative user interaction models that assist users in
crossing the language barrier. However, datasets that reflect user inter-
action traces in cross-lingual settings required to train and evaluate the
user interaction models are mostly missing. In this paper, we present the
EventKG+Click dataset that aims to facilitate the creation and evalua-
tion of such interaction models. EventKG+Click builds upon the event-
centric EventKG knowledge graph and language-specific information on
user interactions with events, entities, and their relations derived from
the Wikipedia clickstream.
1 Introduction
With a rapidly growing number of events with significant international impact,
cross-lingual analytics gains increased importance for researchers and profession-
als in many disciplines, including digital humanities, media studies, and journal-
ism. The most prominent recent examples of such events include the COVID-19
outbreak, the migration crisis in Europe, and Brexit. From the information sci-
ence perspective, research on event-centric information spread across languages
and communities, as well as cross-cultural and cross-lingual differences in report-
ing, are of particular interest. However, very often, the language barrier hinders
such research.
The development of novel methods for user interaction with event-centric
cross-lingual information can help to overcome the language barrier in this con-
text. Such methods can facilitate researchers with limited knowledge of target
languages to narrow down the search space and to obtain an overview of the
cross-lingual differences effectively and efficiently. However, currently, user in-
teraction in multilingual settings is not sufficiently studied. The benchmarks
and datasets suitable for the evaluation of new methods for user interaction
with cross-lingual information are mostly missing.
With the recent development of knowledge graphs that provide cross-lingual
information, such as Wikidata, DBpedia, and the event-centric EventKG know-
ledge graph [6], the availability of semantic event-centric cross-lingual informa-
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 S. Abdollahi et al.
tion has significantly increased. These knowledge graphs contain semantic infor-
mation regarding events and their relations while providing labels in different
languages along with the properties extracted from language-specific sources.
For example, EventKG, in its version 2.1 released in February 2020, includes
information on more than 1, 200, 000 events in nine languages. We believe that
knowledge graphs containing event-centric cross-lingual data can build a back-
bone for the development of user interaction methods that can assist users in
crossing the language barrier.
In this paper, we present a novel cross-lingual dataset that reflects the lan-
guage-specific relevance of events and their relations. This dataset aims to pro-
vide a reference source to train and evaluate novel models for event-centric cross-
lingual user interaction, with a particular focus on the models supported by
knowledge graphs. Our dataset EventKG+Click is based on two data sources:
1) the Wikipedia clickstream2 that reflects real-world user interactions with
events and their relations within language-specific Wikipedia editions; and 2) the
EventKG knowledge graph that contains semantic information regarding events
and their relations that partially originates from Wikipedia. EventKG+Click is
available online3 to enable further analyses and applications.
Without loss of generality, we adopt a language-specific event ranking as an
envisioned user interaction paradigm to illustrate our discussion. For example,
Table 1 reveals the different language-specific focus when ranking events. In each
of the three languages contained in EventKG+Click, the list of most language-
specific related events is clearly representing language-specific views (e.g., “2016
Berlin truck attack” for German) that can be used for further exploration of
events from language-specific viewpoints. In the case of English, we see that the
Southeast Asian Games are of high language-specific relevance, which can be
explained by the large percentage of Asian users of the English Wikipedia4 .
Table 1. Events with highest language-specific relevance per language in EventKG+-
Click.
Rank English German Russian
Southeast 2016 Berlin 2009 Russian
1
Asian Games truck attack Premier League
2017 Southeast German student 1993 Russian
2
Asian Games movement Top League
2014 United States 2006 Austrian 2012–13 Russian
3
Senate elections legislative election Premier League
In EventKG+Click, we enrich the information obtained from the Wikipedia
clickstream with event and entity references from EventKG. Furthermore, we
2
https://meta.wikimedia.org/wiki/Research:Wikipedia_clickstream
3
https://github.com/saraabdollahi/EventKG-Click
4
https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCo
untryBreakdown.htm
EventKG+Click 3
create a cross-lingual view on the clickstream by combining information obtained
from three Wikipedia language editions, namely English, German, and Russian.
Moreover, we compute scores that reflect the language-specific relevance of events
and their relations, as indicated by the user interactions in the clickstream.
Finally, to support further development of the event-centric user interaction
methods in the cross-lingual settings, we analyse the correlations of the proposed
scoring function and selected influence factors.
We structure the rest of the paper as follows: First, we review related work
regarding cross-lingual analytics, knowledge graphs, and the Wikipedia click-
stream in Section 2. Then, we introduce our EventKG+Click dataset in Section
3. In Section 4, we propose scores to represent the language-specific relevance of
events and their relations. Given the EventKG+Click dataset and these scores,
we analyse how selected factors influence the language-specific relevance in Sec-
tion 5. Finally, we provide a conclusion in Section 6.
2 Related Work
In this section, we briefly summarise related work in the areas of cross-lingual
analytics, along with the aspects related to knowledge graphs and the Wikipedia
clickstream.
Cross-lingual analytics and interaction. With the rise of the Web, there
came an uprise of user-generated content accessible over the whole world, leading
to knowledge diversity across languages [7]. The identification and analysis of
such knowledge diversity is an important method to understand language com-
munities better. For example, Oeberst et al. identified different types of ”collec-
tive biases” such as biased representations of intergroup conflicts that appear
under collaborative circumstances [14]. Miz et al. identified how Wikipedia re-
flects cultural particularities [11]. Mocanu et al. have identified linguistic trends
in Twitter usage in more than 100 countries [12].
In the context of cross-lingual analytics, events play a particularly important
role: When an event breaks out, this event is usually reported by a large number
of sources, whose coverage highly varies across language communities [3]. This
phenomenon becomes visible when using EventRegistry, a tool that allows cross-
lingual exploration of news articles which are assigned to event clusters [15].
Event-centric cross-lingual analytics are also viable across different Wikipedia
language editions as illustrated by two case studies about the Brexit and the US
withdrawal from the Paris Agreement, where researchers identified language-
specific viewpoints [4].
With EventKG+Click, our goal is to promote further cross-lingual analytics
and interaction, facilitated by a combination of semantic information given in
knowledge graphs and user interaction traces obtained from a clickstream.
Knowledge graphs. An essential resource to facilitate interaction with
cross-lingual information are knowledge graphs, in particular those containing
language-specific labels and relations. Kaffee et al. [8] developed metrics that
measure the multilingualism of knowledge graphs to identify those suitable for
4 S. Abdollahi et al.
usage in multilingual applications and to gain cross-lingual insights. For example,
Marie et al. [10] discovered a ”cultural prism” between the different DBpedia
language editions when querying for entities related to facets of interest.
The importance of multilingualism in knowledge graphs becomes even more
evident in the case of event-based applications. EventKG [6] is a knowledge graph
that is tailored not only to the interaction with event-centric information but
also contains information coming from several languages. An example application
that makes use of this cross-lingual event knowledge is EventKG+TL [5] that
relies on Wikipedia link counts present in EventKG to model the importance of
events related to a given concept.
In our analysis, we observed that the closeness of event locations extracted
from EventKG is an essential indicator to explain language-specific relevance.
Thus, we confirm the importance of event-centric and multilingual knowledge
graphs in the context of cross-lingual analytics.
Wikipedia clickstream. The Wikipedia clickstream has been used as a
ground-truth to evaluate entity recommendation and relatedness in several ex-
amples, as it reveals the navigationâl behaviour of users and their preferences
while exploring Wikipedia pages. Existing work, however, has not considered
language-specific differences and mainly focused on the English Wikipedia click-
stream: For example, Tran et al. used the English Wikipedia clickstream as
ground truth for constructing entity-context queries [16] and Bhatia et al. con-
structed their query dataset based on the English Wikipedia clickstream [1].
Nguan et al. evaluated their relatedness ranking method by using the raw num-
ber of navigations in Wikipedia clickstream [13]. With the usage of the Wikipedia
clickstream in different languages, EventKG+Click adds a new perspective onto
EventKG, as it reflects real user behaviour across language communities, which
goes beyond the consideration of knowledge graph relations and Wikipedia link
counts.
3 EventKG+Click Dataset
The Wikipedia clickstream holds the interaction of real users with the articles
representing events and entities in the specific Wikipedia language editions and
their relations. In particular, the clickstream contains the counts of the (source,
target) pairs extracted from Wikipedia’s request logs. The clickstream contains
all the requests to a Wikipedia page, including links from and to external web
pages. As EventKG+Click and our analysis are based on Wikipedia click be-
haviour, we only consider those (source, target) click pairs in the clickstream
where both the source and target are Wikipedia articles connected by a hyper-
link.
In this work, we adopt the Wikipedia clickstream that covers the period from
December 1, 2019, to December 31, 2019, and contains nearly 19, 521, 580 click
pairs for the English, 2, 902, 878 click pairs for the German, and 2, 752, 340 click
pairs for the Russian Wikipedia.
EventKG+Click 5
EventKG is an event-centric knowledge graph that contains more than 1.2
million events and more than 4 million temporal relations in nine languages in
its release from February 2020. Knowledge graphs such as EventKG, DBpedia,
and Wikidata include information extracted from the multilingual Wikipedia as
the basis. This way, data regarding user interaction with Wikipedia articles and
links, available from the Wikipedia clickstream dataset, can be directly mapped
to the events, entities and their relations in these knowledge graphs.
When creating the proposed EventKG+Click dataset, we assume that: 1)
the events of global importance are reflected in Wikipedia clickstreams of sev-
eral languages, and 2) a clickstream in a specific language reflects the importance
of events and their relations as perceived by the users of the specific Wikipedia
language edition. Based on these assumptions, we employ the intersection of
language-specific clickstreams to build a dataset for training and evaluation of
cross-lingual user interaction. In particular, we map the events and entities in-
cluded in the Wikipedia clickstream to EventKG and extract relations for these
events from all language-specific clickstreams. Furthermore, we compute scores
that represent the language-specific relevance of events and their relations. These
scores are presented in Section 4. To enable further cross-lingual analysis, we en-
rich EventKG+Click with several influence factors extracted from EventKG and
Wikipedia, which are presented in Section 5.
In EventKG+Click, we only consider entities that are clicked at least 10 times
per language, so that we capture those entities that are of global importance and
do not consider entities solely present in single Wikipedia language versions. We
also only consider pairs which exist in the clickstreams of all considered languages
and in which the target page is an event.
The resulting EventKG+Click dataset is available online5 and contains rel-
evance scores for more than 4 thousand events, and nearly 10 thousand event-
centric click-through pairs.
4 Scores to Assess Language-Specific Relevance
To allow cross-lingual analytics with EventKG+Click, we need to capture the
language-specific relevance of events and their relations. Based on the Wikipedia
clickstream, we propose two scores that rule out language-independent relevance.
To describe our scores, we first define the concepts used for the computation:
– L is the set of languages under consideration. The current release of Event-
KG+Click comes in English, German, and Russian: L = {EN, DE, RU }.
– E is the set of entities contained in EventKG+Click, that are all represented
by specific Wikipedia pages and EventKG resources. Formally, named events
considered in this work are a specific type of entity and thus included in E.
– clicks(es , et , l) represents the number of clicks from the source entity es ∈ E
to the target event et ∈ E in the clickstream of the given language l ∈ L.
5
https://github.com/saraabdollahi/EventKG-Click
6 S. Abdollahi et al.
We distinguish between two scores defined in the following: language-specific
event relevance and language-specific relation relevance.
4.1 Language-specific Event Relevance
Wikipedia language versions differ a lot concerning the number of their active
users, edits, and articles. For example, the English Wikipedia has 7.2 times as
many active users as the German Wikipedia6 . The clickstream also reflects this
imbalance: There are 7 times more clicks in the English clickstream than in
the German one. To observe language-specific behaviour, we first need to level
the effects that originate from the popularity of the specific Wikipedia language
versions. To do so, we normalise the number of clicks with respect to the total
number of clicks in the respective language, which leads to normalised scores in
the range [0, 1]. In order to create balanced click counts, we then multiply the
normalised score by the total number of clicks in the clickstreams, as follows:
0 0 0
P P P
l0 ∈L e0s ∈E e0t ∈E clicks(es , et , l )
balanced clicks(es , et , l) = clicks(es , et , l) · P P 0 0
e0s ∈E e0t ∈E clicks(es , et , l)
The popularity of an event can be inferred by the number of user interactions
with its Wikipedia page. That way, we can identify the most popular events in
a given language l ∈ L by summing up all clicks from and to an event e ∈ E:
X X
balanced clicks(e, l) = balanced clicks(e, et , l)+ balanced clicks(es , e, l)
et ∈E es ∈E
As we focus on the language-specific relevance in EventKG+Click, we need
to rule out the events that are highly ranked across all languages under consid-
eration. Therefore, we normalise the language-specific click count by the overall
number of clicks in all languages:
balanced clicks(e, l)
event relevance(e, l) = P ∈ [0, 1]
l0 ∈Lbalanced clicks(e, l0 )
With this relevance score, events that are clicked often in a given language
l ∈ L, but rarely clicked in the other languages are assigned a relevance score
close to 1.
4.2 Language-specific Relation Relevance
To identify events relevant to a given source entity, we define the language-
specific relation relevance score. This score assigns a relevance score to the re-
lation between a source entity es and a target event et in a given language.
6
https://en.wikipedia.org/wiki/List_of_Wikipedias
EventKG+Click 7
Similarly to the language-specific event relevance, the language-specific relation
relevance is computed as the fraction of clicks in the given language compared
to all languages:
balanced clicks(es , et , l)
relation relevance(es , et , l) = P 0
∈ [0, 1]
l0 ∈L balanced clicks(es , et , l )
Note that this score rules out the effects resulting from the relevance of the
source entity: Events that are highly related to an entity e can obtain relevance
scores close to 1 independent of e’s click count.
4.3 Examples of Scores
In Table 1 in Section 1, we have given an example of the language-specific event
relevance, i.e., that table provides the top-ranked events per language, accord-
ing to our language-specific event relevance score. As discussed before, we can
clearly observe events which are intuitively important for the respective language
community.
Table 2. The three events most relevant to the 2012 Summer Olympics for English,
German and Russian.
Rank English German Russian
2012 Summer Olympics 2020 Summer Olympics
1 Olympic Games
opening ceremony opening ceremony
Swimming at the Modern pentathlon at the
2 Olympic Games
2012 Summer Olympics 2012 Summer Olympics
Badminton at the Equestrian at the Weightlifting at the
3
2012 Summer Olympics 2012 Summer Olympics 2012 Summer Olympics
Table 2 presents the language-specific relation relevance by showing the con-
crete example of events relevant to the Summer Olympics in 2012. According
to our score, the opening ceremony that happened in London is the most rele-
vant event for the 2012 Summer Olympics from the English perspective. Apart
from that, we can observe that sports particularly popular in a specific language
community are ranked higher (e.g., swimming for English, equestrian sports for
German, and weightlifting for Russian).
Our examples illustrate that user click behaviour is not only based on globally
relevant entities but takes the language-specific relevance into account. Both
relevance scores can be used in language-specific contexts, e.g. for event retrieval
or recommendation.
5 Influence Factors for Language-Specific Relevance
Given the EventKG+Click dataset with the relevance scores defined in the pre-
vious section, we now discuss several influence factors that can potentially im-
8 S. Abdollahi et al.
pact the language-specific relevance of events and analyse their correlations with
the proposed relevance scores. As influence factors we consider language com-
munity relevance, event location closeness and event recency, as defined in the
following. In future work, we plan to investigate the role of further influence
factors, as for example the event type that has been shown to influence the
click-behaviour [2].
5.1 Language Community Relevance
The language community relevance factor reflects the importance of an event for
the community that speaks this language. We assume that events relevant for
the language community should be mentioned and referred to more often in a
language-specific corpus.
Based on this assumption, we measure the language community relevance
by counting the links to the event article and mentions of the event within the
specific Wikipedia language edition7 . Dependent on the context (i.e., event or
relation relevance), we make use of two influence factors:
– Links pointing to the event: The number of links in the whole Wikipedia
language edition that link to the event article.
– Co-mentions of a relation: The number of sentences in the whole Wikipedia
language edition that jointly mentions the (source, target) pair participating
in the relation.
5.2 Event Location Closeness
The event location closeness factor expresses the intuition that users are likely
to be interested in the exploration of local events, i.e., events located in spatial
proximity of the user. To reflect this intuition, we introduce a binary influence
factor that indicates whether an event happened in a location where the respec-
tive language l ∈ L is an official language. For example, the Battle of Stalingrad
may be particularly important from the Russian perspective in the context of
the Second World War. To compute this factor, we first identify event location(s)
using the sem:hasPlace8 property of EventKG and then derive the official lan-
guages of the location’s country.
5.3 Event Recency
Wikipedia is heavily influenced by recent events: Users tend to edit and read
articles about events that are happening right now [9]. To observe the im-
pact of recency on the language-specific user click behaviour, we introduce a
recency score, which is computed as the number of days between the event start
date and the start date of the clickstream dataset (the dates of the specific en-
tries in the dataset are not available). To identify the event start dates, we use
sem:hasBeginTimeStamp values in EventKG.
7
We derive these counts from EventKG that contains link and mention counts [6].
8
http://semanticweb.cs.vu.nl/2009/11/sem/hasPlace
EventKG+Click 9
5.4 Correlations with Influence Factors
Given EventKG+Click and the influence factors, we now investigate the correla-
tions between such influence factors and the language-specific relevance scores.
To this end, we compute the Pearson correlation coefficients in several configu-
rations.
First, we compute the correlations of influence factors with language-specific
event relevance scores of the events covered in the Wikipedia clickstream of all
considered languages (i.e., event relevance, as defined in Section 4). As influ-
ence factors we select the event location closeness (Location), the number of
links pointing to the respective event (Links), and the event recency (Recency).
Results are shown in Table 3.
Table 3. Correlations of influence factors with event relevance scores in EventKG+-
Click.
Influence Factors
Location Links
Recency
EN DE RU EN DE RU
Language- EN 0.4 -0.13 -0.25 0 -0.02 0.01 -0.19
specific DE -0.18 0.20 0.02 -0.01 0 0.01 0.12
relevance RU -0.19 -0.08 0.26 -0.01 -0.02 0.03 0.07
The Location influence factor for events indicates the largest positive corre-
lation, which confirms the existence of different language viewpoints. This effect
can be most notably observed in the case of English, which has a correlation
of 0.4 between the event relevance score and the Location closeness influence
factor. The other two influence factors, namely Links and Recency, do not show
any notable correlation. We assume that this is because the users are interested
in both, recent and historical events, whereas recent events might not be well
interlinked in Wikipedia yet.
Until now, we have considered the language-specific event relevance scores,
i.e., scores assigned to each event in isolation. Now, we investigate the user click
behaviour from the perspective of the event relations (i.e., relation relevance,
as defined in Section 4). In particular, we focus on the properties of the target
event, as the language-specific relation relevance score is independent of the
source entity’s relevance.
The following influence factors are used in this correlation analysis:
i Location: The location closeness of the target event.
ii Links: The number of links to the target event in Wikipedia.
iii Recency: The recency of the target event.
iv Co-Mentions: The number of co-mentions of the relation source and target
in Wikipedia.
The correlation results are shown in Table 4. The correlation coefficient for
the language-specific relation relevance confirms our observations concerning the
10 S. Abdollahi et al.
Table 4. The Pearson correlation coefficient of the relation relevance score with se-
lected influence factors in EventKG+Click.
Influence Factors
Location Links Co-mentions
Recency
EN DE RU EN DE RU EN DE RU
Language- EN 0.41 -0.09 -0.27 0 0 -0.01 -0.16 0.11 -0.06 -0.04
specific DE -0.13 0.10 0.01 0 0 -0.01 0.1 -0.01 0 0.01
relevance RU -0.15 -0.05 0.18 0 0 0.02 0.06 -0.07 -0.07 0.13
language-specific event relevance. The closeness of the target event location has
the largest influence on language-specific relevance. The links, recency and co-
mentions do not correlate with the relevance scores in any of the three languages.
That means, if the user reads a particular Wikipedia article, there is a higher
chance that the next click leads to a spatially close event than to an event that
is mentioned many times together with the source entity.
6 Conclusion and Outlook
In this paper, we presented the EventKG+Click dataset and suggested scores for
capturing language-specific relevance scores for events and their relations. Event-
KG+Click builds upon the EventKG knowledge graph and language-specific
traces of user interaction with events derived from the Wikipedia clickstream.
The resulting EventKG+Click dataset contains click counts and relevance scores
for more than 4 thousand events and more than 10 thousand (source, target)
pairs in English, German, and Russian. Furthermore, we analysed several influ-
ence factors of language-specific relevance. We believe that the EventKG+Click
dataset is a valuable resource to evaluate event relevance in language-specific
contexts. In future work, we plan to develop novel user interaction models sup-
porting cross-lingual event-centric analytics, where we will adopt the EventKG+-
Click dataset for training and evaluation.
Acknowledgements This work was partially funded by H2020-MSCA-ITN-
2018-812997 under “Cleopatra”.
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