Generating and Visualizing a Soccer Knowledge Base
Paul Buitelaar, Thomas Eigner, Greg Gul-
rajani, Alexander Schutz, Melanie Siegel,
Nicolas Weber
Language Technology Lab, DFKI GmbH
Saarbrücken, Germany
{paulb,siegel}@dfki.de
Philipp Cimiano, Günter Ladwig,
Matthias Mantel, Honggang Zhu
Institute AIFB, University of Karlsruhe
Karlsruhe, Germany
cimiano@aifb.uni-karlsruhe.de
Abstract
This demo abstract describes the SmartWeb
Ontology-based Annotation system (SOBA).
A key feature of SOBA is that all informa-
tion is extracted and stored with respect to
the SmartWeb Integrated Ontology
(SWIntO). In this way, other components of
the systems, which use the same ontology,
can access this information in a straightfor-
ward way. We will show how information
extracted by SOBA is visualized within its
original context, thus enhancing the browsing
experience of the end user.
1 Introduction
SmartWeb1 is a multi-modal dialog system,
which derives answers from unstructured re-
sources such as the Web, from automatically ac-
quired knowledge bases and from web services.
In this paper we describe the current status of
the SmartWeb Ontology-Based Annotation
(SOBA) system. SOBA automatically populates
a knowledge base by information extraction from
soccer match reports as available on the web.
The extracted information is defined with respect
to SWIntO, the underlying SmartWeb Integrated
Ontology (Oberle et al., in preparation) in order
to be smoothly integrated into the system.
The ability to extract information and describe
it ontologically is a basic requirement for more
complex processing tasks such as reasoning and
discourse analysis (for related work on ontology-
based information extraction see e.g. Maedche et
al., 2002; Lopez and Motta, 2004; Müller et al.,
2004; Nirenburg and Raskin, 2004).
1 http://www.smartweb-projekt.de/start_en.html
2 System Overview
The SOBA system consists of a web crawler,
linguistic annotation components and a compo-
nent for the transformation of linguistic annota-
tions into an ontology-based representation.
The web crawler acts as a monitor on relevant
web domains (i.e. the FIFA2 and UEFA3 web
sites), automatically downloads relevant
documents from them and sends them to a
linguistic annotation web service.
Linguistic annotation and information
extraction is based on the Heart-of-Gold (HoG)
architecture (Callmeier et al. 2004), which
provides a uniform and flexible infrastructure for
building multilingual applications that use
semantics- and XML-based natural language
processing components.
The linguistically annotated documents are
further processed by the transformation
component, which generates a knowledge base
of soccer-related entities (players, teams, etc.)
and events (matches, goals, etc.) by mapping
annotated entities or events to ontology classes
and their properties.
Finally, an automatic hyperlinking component
is used for the visualization of extracted entities
and events. This component is based on the
VieWs system, which was developed
independently of SmartWeb (Buitelaar et al.,
2005). In what follows we describe the different
components of the system in detail.
2.1 Web Crawler
The crawler enables the automatic creation of a
football corpus, which is kept up-to-date on a
daily basis. The crawler data is compiled from
texts, semi-structured data and copies of original
2 http://fifaworldcup.yahoo.com/
3 http://www.uefa.com/
123
HTML documents. For each football match, the
data source contains a sheet of semi-structured
data with tables of players, goals, referees, etc.
Textual data comprise of match reports as well as
news articles.
The crawler is able to extract data from two
different sources: FIFA and UEFA. Semi-
structured data, news articles and match reports
covering the WorldCup2006 are identified and
collected from the FIFA website. Match reports
and news articles are extracted from the UEFA
website. The extracted data are labeled by IDs
that match the filename. The IDs are derived
from the corresponding URL and are thus
unique.
The crawler is invoked continuously each day
with the same configuration, extracting only data
which is not yet contained in the corpus. In order
to distinguish between available new data and
data already present in the corpus, the URLs of
all available data from the website are matched
against the IDs of the already extracted data.
2.2 Linguistic Annotation and Information
Extraction
As mentioned before, linguistic annotation in the
system is based on the HoG architecture, which
provides a uniform and flexible infrastructure for
building multilingual applications that use
semantics- and XML-based natural language
processing components.
For the annotation of soccer game reports, we
extended the rule set of the SProUT (Drozdzyn-
ski et al. 2004) named-entity recognition compo-
nent in HoG with gazetteers, part-of-speech and
morphological information. SProUT combines
finite-state techniques and unification-based al-
gorithms. Structures to be extracted are ordered
in a type hierarchy, which we extended with soc-
cer-specific rules and output types.
SProUT has basic grammars for the annotation
of persons, locations, numerals and date and time
expressions. On top of this, we implemented
rules for soccer-specific entities, such as actors in
soccer (trainer, player, referee …), teams, games
and tournaments. Using these, we further imple-
mented rules for soccer-specific events, such as
player activities (shots, headers …), game events
(goal, card …) and game results. A soccer-
specific gazetteer contains soccer-specific enti-
ties and names and is supplemented to the gen-
eral named-entity gazetteer.
As an example, consider the linguistic annota-
tion for the following German sentence from one
of the soccer game reports:
Guido Buchwald wurde 1990 in Italien Welt-
meister (Guido Buchwald became world cham-
pion in 1990 in Italy)
<FS type="player_action">
<F name="GAME_EVENT">
<FS type="world champion"/>
<F name="ACTION_TIME">
<FS type="1990"/>
<F name="ACTION_LOCATION">
<FS type="Italy"/>
<F name="AGENT">
<FS type="player">
<F name="SURNAME">
<FS type="Buchwald"/>
<F name="GIVEN_NAME">
<FS type="Guido"/>
2.3 Knowledge Base Generation
The SmartWeb SportEventOntology (a subset of
SWIntO) contains about 400 direct classes onto
which named-entities and other, more complex
structures are mapped. The mapping is repre-
sented in a declarative fashion specifying how
the feature-based structures produced by SProUT
are mapped into structures which are compatible
with the underlying ontology. Further, the newly
extracted information is also interpreted in the
context of additional information about the
match in question.
This additional information is obtained by
wrapping the semi-structured data on relevant
soccer matches, which is also mapped to the on-
tology. The information obtained in this way
about the match in question can then be used as
contextual background with respect to which the
newly extracted information is interpreted.
The feature structure for player as displayed
above will be translated into the following F-
Logic (Kifer et al. 1995) statements, which are
then automatically translated to RDF and fed to
the visualization component:
soba#player124:sportevent#FootballPlayer
[sportevent#impersonatedBy ->
soba#Guido_BUCHWALD].
soba#Guido_BUCHWALD:dolce#"natural-person"
[dolce#"HAS-DENOMINATION" ->
soba#Guido_BUCHWALD_Denomination].
soba#Guido_BUCHWALD_Denomination":dolce#"
natural-person-denomination"
[dolce#LASTNAME -> "Buchwald";
dolce#FIRSTNAME -> "Guido"].
124
2.4 Knowledge Base Visualization
The generated knowledge base is visualized by
way of automatically inserted hyperlink menus
for soccer-related named-entities such as players
and teams. The visualization component is based
on the VIeWs4 system. VIeWs allows the user to
simply browse a web site as usual, but is addi-
tionally supported by the automatic hyperlinking
system that adds additional information from a
(generated) knowledge base.
For some examples of this see the included
figures below, which show extracted information
for the Panama team (i.e. all of the football play-
ers in this team in Figure 1) and for the player
Roberto Brown (i.e. his team and events in which
he participated in Figure 2).
3 Implementation
All components are implemented in Java 1.5 and
are installed as web applications on a Tomcat
web server. SOAP web services are used for
communication between components so that the
system can be installed in a centralized as well as
decentralized manner. Data communication is
handled by XML-based exchange formats. Due
to a high degree of flexibility of components,
only a simple configuration over environment
variables is needed.
4 Conclusions and Future Work
We presented an ontology-based approach to
information extraction in the soccer domain that
aims at the automatic generation of a knowledge
base from match reports and the subsequent
visualization of the extracted information
through automatic hyperlinking. We argue that
such an approach is innovative and enhances the
user experience.
Future work includes the extraction of more
complex events, for which deep linguistic analy-
sis and/or semantic inference over the ontology
and knowledge base is required. For this purpose
we will use an HPSG-based parser that is avail-
able within the HoG architecture (Callmeier,
2000) and combine this with a semantic infer-
ence approach based on discourse analysis
(Cimiano et al., 2005).
4 http://views.dfki.de
Acknowledgements
This research has been supported by grants for
the projects SmartWeb (by the German Ministry
of Education and Research: 01 IMD01 A) and
VIeWs (by the Saarland Ministry of Economic
Affairs).
References
Paul Buitelaar, Thomas Eigner, Stefania Racioppa
Semantic Navigation with VIeWs In: Proc. of the
Workshop on User Aspects of the Semantic Web at
the European Semantic Web Conference, Herak-
lion, Greece, May 2005.
Callmeier, Ulrich (2000). PET – A platform for ex-
perimentation with efficient HPSG processing
techniques. In: Natural Language Engineering, 6
(1) UK: Cambridge University Press pp. 99–108.
Callmeier, Ulrich, Eisele, Andreas, Schäfer, Ulrich
and Melanie Siegel. 2004. The DeepThought Core
Architecture Framework In Proceedings of LREC
04, Lisbon, Portugal, pages 1205-1208.
Cimiano, Philipp, Saric, Jasmin and Uwe Reyle.
2005. Ontology-driven discourse analysis for in-
formation extraction, Data Knowledge Engineering
55(1).
Drozdzynski, Witold, Hans-Ulrich Krieger, Jakub
Piskorski, Ulrich Schäfer, and Feiyu Xu. 2004.
Shallow processing with unification and typed fea-
ture structures – foundations and applications.
Künstliche Intelligenz, 1:17-23.
Kifer, M., Lausen, G. and J.Wu. 1995. Logical Foun-
dations of Object-Oriented and Frame-Based Lan-
guages. Journal of the ACM 42, pp. 741-843.
Lopez, V. and E. Motta. 2004. Ontology-driven Ques-
tion Answering in AquaLog In Proceedings of 9th
International Conference on applications of natural
language to information systems.
Maedche, Alexander, Günter Neumann and Steffen
Staab. 2002. Bootstrapping an Ontology-Based In-
formation Extraction System. In: Studies in Fuzzi-
ness and Soft Computing, editor J. Kacprzyk. Intel-
ligent Exploration of the Web, Springer.
Müller HM, Kenny EE and PW Sternberg. 2004.
Textpresso: An ontology-based information re-
trieval and extraction system for biological litera-
ture. PLoS Biol 2: e309.
Nirenburg, Sergei and Viktor Raskin. 2004. Ontologi-
cal Semantics. MIT Press.
Oberle et al. The SmartWeb Integrated Ontology
SWIntO, in preparation.
125
Figure 2: Generated hyperlink on „Roberto Brown“ with extracted information on his
team and events in which he participated
Figure 1: Generated hyperlink on „Panama“ with extracted information on this team
126
