=Paper=
{{Paper
|id=Vol-1486/paper_54
|storemode=property
|title=The DBpedia Events Dataset
|pdfUrl=https://ceur-ws.org/Vol-1486/paper_54.pdf
|volume=Vol-1486
|dblpUrl=https://dblp.org/rec/conf/semweb/KnuthLKSS15
}}
==The DBpedia Events Dataset==
https://ceur-ws.org/Vol-1486/paper_54.pdf
The DBpedia Events Dataset
Magnus Knuth1 , Jens Lehmann2 , Dimitris Kontokostas2 ,
Thomas Steiner3 , and Harald Sack1
1
Hasso Plattner Institute, University of Potsdam, Germany
{firstname.lastname}@hpi.uni-potsdam.de
2
Universität Leipzig, Institut für Informatik, AKSW, Germany
{lastname}@informatik.uni-leipzig.de
3
CNRS, Université de Lyon, LIRIS – UMR5205, Université Lyon 1, France
tsteiner@liris.cnrs.fr
Abstract. Wikipedia is the largest encyclopedia worldwide and is fre-
quently updated by thousands of collaborators. A large part of the knowl-
edge in Wikipedia is not static, but frequently updated, e. g., political
events or new movies. This makes Wikipedia an extremely rich, crowd-
sourced information hub for events. However, currently there is no struc-
tured and standardised way to access information on those events and
it is cumbersome to filter and enrich them manually. We have created
a dataset based on a live extraction of Wikipedia, which performs this
task via rules for filtering and ranking updates in DBpedia Live.
1 Introduction
Since 2007, the DBpedia project has been extracting metadata and structured
data from Wikipedia and made it publicly available as RDF triples [3]. DBpedia
also offers a live synchronized version of extracted data – DBpedia Live [4].
The English Wikipedia alone has hundreds of updates per minute [6] that are
processed via the Live framework. Changes in Wikipedia articles are often con-
nected to real life events, such as news related events from politics, cultural life,
or sports. Due to the large user base of Wikipedia, these events are often quickly
updated – in many cases quicker than in other Web sources [7,1].
However, currently there is no structured and standardised way to access
information about these events and it is cumbersome to filter and enrich them
manually. While there are previous efforts to extract events from Wikipedia such
as [2,5,7,8], associated data about these events is not always available as RDF or
even archived. Providing an RDF dataset has the benefit of being able to rely on
standards for accessing and querying information. Furthermore, events can be
readily combined with background knowledge from DBpedia and other sources,
which enables mashups of events with further structured data.
The most important challenges when extracting events from DBpedia are
(i) detecting events, (ii) providing context, and (iii) ranking events according to
their importance. Since by far not all changes in Wikipedia are events, we need
a mechanism to detect those. In our case, we rely on a semi-automatic approach
2 Magnus Knuth et al.
Wikipedia extract DBpedia Live
Changesets SPARQL Endpoint
(1) transform
GUO model
Digest (2) query
Templates (3) contextQuery
Digest Items Snapshots
Fig. 1. The extraction process
based on extensible rule sets, which are executed over DBpedia Live changesets.
If a rule fires, it triggers another query obtaining contextual information. The
detected event is ranked according to the resource’s PageRank and the edit
activity of the Wikipedia page. The output of the processing pipeline is stored
in RDF preserving all provenance information.
2 Conversion Process
Figure 1 shows the underlying workflow, which has three major steps: (1) DBpedia
Live changesets are transformed to an RDF representation, (2) relevant changes
are retrieved according to queries defined in Digest Templates, and (3) Digest
Items are made up with contextual information, e. g., snapshots taken from the
DBpedia Live SPARQL endpoint.
DBpedia Live constantly monitors updates to Wikipedia articles and re-
extracts corresponding resources using the DBpedia extraction framework. The
resource descriptions are diffed with their current revision and changed RDF
triples are published in form of changeset files, i. e., gzipped N-triples dumps
of added and removed triples4 . These changeset files are primarily intended for
synchronization of RDF stores. In order to make these changesets queryable, they
are transformed to an RDF representation using the Graph Update Ontology
(GUO)5 . For each re-extracted resource a guo:UpdateInstruction is created, that
contains the added and removed subgraphs, aggregated for a given time-span.
Relevant changes are extracted from this model by executing SPARQL queries,
which are defined in so-called Digest Templates. These queries can exclusively se-
lect patterns from inserted and deleted triples. The structure of a digest template
is shown exemplary in Listing 1.1. The context query is executed on the DBpedia
Live SPARQL endpoint to validate the result based on context information that
is not available in the changesets. This allows to also consider unchanged state-
ments about the resource for the event selection, e. g. the dig:PRESIDENT tem-
plate in Listing 1.1 only updates of resources are allowed, which are typed as
4
http://live.dbpedia.org/changesets/
5
http://purl.org/hpi/guo#
The DBpedia Events Dataset 3
dbo:Organisation and having a label. More complex restrictions that can be
expressed with SPARQL may apply. The dig:descriptionTemplate is used to
generate a natural language headline for the detected event by substitution of
the placeholders (enclosed in %%) with the respective resource labels.
Listing 1.1. The PRESIDENT digest template
1 dig : PRESIDENT a dbe : DigestTe mplate ;
2 dcterms : identifier " PRESIDENT " ;
3 dcterms : description " " " President changed . " " " @ en ;
4 dbe : queryString " " " SELECT ? u ? res ? oldPres ? newPres
5 { ? u guo : target_ subject ? res ;
6 guo : delete [ dbo : president ? oldPres ] ;
7 guo : insert [ dbo : president ? newPres ] . } " " " ;
8 dbe : c o n t e x t Q u e r y S t r i n g " " " SELECT ? label
9 { %% res %% a dbo : Organisation ; rdfs : label ? label . } " " " ;
10 dbe : d e s c r i p t i o n T e m p l a t e " " " %% newPres %% succeeds %% oldPres %% as the
,→ president of %% res %%. " " " .
From the validated result the final event or so-called Digest Item is created.
These items contain all necessary information to understand the change that
occurred in DBpedia Live.
Listing 1.2. An event created from the LEADER template
1 item :2015/04/25/ C h r i s t i a n _ D e m o c r a t s _ ( Sweden ) - LEADER a dbe : Event ;
2 dbe : context snapshot :2015/04/25/ C h r i s t i a n _ D e m o c r a t s _ ( Sweden ) ;
3 dbe : update update :2015/04/25/ C h r i s t i a n _ D e m o c r a t s _ ( Sweden ) ;
4 dcterms : description " " " Ebba Busch succeeded Goran Hagglund as the leader of
,→ Christian Democrats ( Sweden ) . " " " @ en ;
5 dbe : rank 1.82421 e -06 ;
6 prov : ge ne r at ed A tT im e " 2015 -04 -30 T13 : 4 5 : 3 5 . 7 9 8 + 0 2 : 0 0 " ^^ xsd : dateTime ;
7 prov : wa sDerive dFrom dig : LEADER ,
8 changesets : 2 0 1 5 / 0 4 / 2 5 / 1 4 / 0 0 0 2 0 1 . removed . nt . gz ,
9 changesets : 2 0 1 5 / 0 4 / 2 5 / 1 4 / 0 0 0 2 0 1 . added . nt . gz .
3 Dataset Description
The dataset consists of daily digest dump files, which contain the descriptions
of events (c.f. Listing 1.2) occurring on this day as well as the resource updates
related to them. The resource snapshots that are linked in the event descriptions
might be relevant for further investigation. Thus, they are kept separately in
individual snapshot dumps. The daily generated event dumps can be accessed at
http://events.dbpedia.org/dataset/ and additionally a SPARQL interface
is offered at http://events.dbpedia.org/sparql for querying the full dataset.
The resource snapshots corresponding to the events are published in a separate
path at http://events.dbpedia.org/snapshot/.
At the current stage 12 digest templates have been defined and fired6 . Table 1
shows the number of events that matched the templates.
4 Conclusion
This paper presents an automated means to detect events and extract relevant
data changes within DBpedia Live on the one hand, and on the other hand make
these events available as Linked Data for others to consume and build upon.
6
Defined digest templates: http://events.dbpedia.org/data/digests.ttl.
4 Magnus Knuth et al.
Count Template Count Template
4509 AWARDED 146 LEADER
3252 HEADHUNTED 118 PODIUM
2539 RELEASED 89 PRESIDENT
1991 JUSTMARRIED 22 VOLCANO
785 DEADPEOPLE 7 EUROPE2015
782 JUSTDIVORCED 1 INTRODUCED
Table 1. Extracted events per template
Potential use cases for our constantly growing dataset include, but are not
limited to, (breaking) news detection systems for news agencies, brand monitor-
ing systems for so-called digital war rooms, but also more mundane use cases such
as celebrity trackers (who married whom), or mashups in general. The dataset
provides a comprehensible overview on usually rather complex data changes and
may give valuable insights into dataset dynamics. Having stable identifiers for
events further allows for interesting reasoning use cases.
Some information can simply not be deduced from discrete state resource
descriptions, e. g. that a person moved from Germany to France can not be ex-
tracted from the separated facts that she lived in Germany and lives in France,
rather both states need to be regarded and compared. This is what this project
makes possible. The application supports an individual selection of changes of
interest by the free definition of digest templates, which allows monitoring cus-
tomized data change events.
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