This demo abstract describes the SmartWeb Ontology-based Information Extraction System (SOBIE). A key feature of SOBIE is that all information is extracted and stored with respect to the SmartWeb ontology. In this way, other components of the systems, which use the same ontology, can access this information in a straightforward way. We will show how information extracted by SOBIE is visualized within its original context, thus enhancing the browsing experience of the end user

Buitelaar, Paul

Cimiano, Philipp

Eigner, Thomas

Gulrajani, Greg

Ladwig, Günter

Mantel, Matthias

Schutz, Alexander

Siegel, Melanie

Weber, Nicolas

Zhu, Honggang

English

Hochschulschriftenserver - Universität Frankfurt am Main

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 Information Extraction Sys-tem (SOBIE). A key feature of SOBIE is that all information is extracted and stored with respect  to  the  SmartWeb  ontology.  In  this way, other components of the systems, which use the same ontology, can access this infor-mation  in  a  straightforward  way.  We  will show how information extracted by SOBIE is visualized  within  its  original  context,  thus enhancing  the  browsing  experience  of  the end user.   1  Introduction SmartWeb1  is  a  complex,  open-domain  and multi-modal  question  answering  system,  which derives  answers  from  unstructured  resources such  as  the  Web,  from  automatically  acquired knowledge bases and from web services.  The  SmartWeb  component  that  we  describe here automatically populates a knowledge base by information extraction from soccer game re-ports as found on the web. The extracted infor-mation is defined with respect to the underlying SmartWeb ontology in order to be smoothly in-tegrated  into  the  system  -  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). The ability to extract information and de-scribe it ontologically is a basic requirement for more complex processing tasks such as reasoning and discourse analysis.                                                   1 http://www.smartweb-projekt.de/start_en.html In this paper we describe the current status of the  SmartWeb  ontology-based  information  ex-traction (SOBIE) system.  2  System Overview The  SOBIE  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/events  with  ontology classes/properties. Finally, an automatic hyperlinking component is  used  for  the  visualization  of  extracted entities/events. This component is based on the VieWs  system,  which  was  developed independently  of  SmartWeb  (Buitelaar  et  al., 2005).  2.1  Web Crawler The crawler enables the automatic creation of a football  corpus,  which  is  kept  up-to-date  on  a                                                  2 http://fifaworldcup.yahoo.com/ 3 http://www.uefa.com/ daily basis. The crawler data is compiled from texts, semi-structured data and copies of original 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 is 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  contains about  400  direct  classes  onto  which  named-entities and other, more complex structures are mapped.  The  mapping  is  represented  in  a  de-clarative  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:   sobie#player124:sportevent#FootballPlayer  [sportevent#impersonatedBy ->    sobie#Guido_BUCHWALD].   sobie#Guido_BUCHWALD:dolce#"natural-person"  [dolce#"HAS-DENOMINATION" ->    sobie#Guido_BUCHWALD_Denomination].   sobie#Guido_BUCHWALD_Denomination":dolce#" natural-person-denomination"  [dolce#LASTNAME -> "Buchwald";      dolce#FIRSTNAME -> "Guido"].  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 he is now supported by the automatic hyperlinking system that adds additional information from a (gener-ated) 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  centralized  as  well  as decentralized. Data communication is handled by XML-based exchange formats. Because of 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 visualizing the ex-tracted information through automatic hyperlink-ing. We argue that such an approach is innova-tive and enhances the user experience. Future work includes expansion into the ex-traction of more complex events, for which deep linguistic analysis and/or semantic inference over the ontology and knowledge base is required. For this purpose we will use an HPSG-based parser that is available within the HoG architecture and combine this with a semantic inference approach based  on  discourse  analysis  (Cimiano  et  al., 2005).  Acknowledgements   This research has been supported by grants for the projects SmartWeb (by the German Ministry                                                  4 http://views.dfki.de 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,  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, Manchester. 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.        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  

Bootstrapping an Ontology-Based Information Extraction System. In:

Logical Foundations of Object-Oriented and Frame-Based Languages.

Ontological Semantics.

Ontology-driven discourse analysis for information extraction

Ontology-driven Question Answering in AquaLog In

Shallow processing with unification and typed feature structures – foundations and applications. Künstliche Intelligenz,

Stefania Racioppa Semantic Navigation with VIeWs In:

Textpresso: An ontology-based information retrieval and extraction system for biological literature.

The DeepThought Core Architecture Framework In

Generating and visualizing a soccer knowledge base

Generating and visualizing a soccer knowledge base

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