Cross-language Linking of eGov Services to the LOD Cloud Fedelucio Narducci*, Matteo Palmonari*, Giovanni Semeraro° *DISCO, University of Milan-Bicocca, Italy °Department of Computer Science, University of Bari Aldo Moro, Italy Semantic Web Access & Personalization research group “Antonio Bello” AI for Smart Cities Workshop AI*IA 2013 - 25th Year Anniversary XIII Conference Turin (Italy), December 5, 2013
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Cross-language Linking of eGov Services to the LOD Cloud
Fedelucio Narducci*, Matteo Palmonari*, Giovanni Semeraro°
*DISCO, University of Milan-Bicocca, Italy°Department of Computer Science, University of Bari Aldo Moro, Italy
SemanticWeb Access & Personalization research group “Antonio Bello”http://www.di.uniba.it/~swap
AI for Smart Cities WorkshopAI*IA 2013 - 25th Year Anniversary
XIII Conference Turin (Italy), December 5, 2013
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EC 6 axesto evaluate ‘city smartness’
http://www.smart-cities.eu/model.html
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Cross-language Linking of Open Government Data
• A large amount of Open Government Data in many languages*:o 1,000,000+ datasets published online (February 2013)o 40 different countries o 24 different languages
*http://logd.tw.rpi.edu/iogds_data_analytics
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Cross-language Linking of eGov Service Descriptions
• Government service catalogs are part of the LOD cloudo Effective Service Delivery (ESD)-toolkit o European Local Government Service List (LGSL)
• 2000+ interlinked public services in 6 languages
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Cross-language Linking of eGov
Services Why is it useful?
• Advantages for PAso Compare local service offerings with best practices in other
countries o Support interoperability among PAs of different countries and other
service providerso Enrich service descriptions with additional information via links to
LGSL (e.g., link to life event ontologies)
• Advantages for citizenso Find eGov services when in a foreign countryo Towards cross-language service access
Costly and Error Prone ActivityCatalogs of several hundreds of services
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Cross-language Linking of eGov
Services Why is it challenging?
≈ sameAs links • Challenging cross-language matching problem
• Most of the approaches:• use structural information [Spohr
et al. 2011, Fu et al. 2011, Wang et al. 2009] or long textual descriptions [Knoth et al. 2011]
• or report problems when automatic translation returns descriptions with heterogeneous vocabulary [Hertling & Paulheim 2012]
Semantic heterogeneityo not a mere “translation”
problemo cultural bias
Ultra-short descriptions
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
TRY IT @ http://siti-rack.siti.disco.unimib.it:8080/croser/
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
• Load a catalog
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
10
CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
• Load a catalog
• Select a source service
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
• Load a catalog
• Look at the retrieved services(link recommendations)
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
• Select a source service
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
• Load a catalog
• LinkSKOS broader / exact / narrower match
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
• Select a source service
• Look at the retrieved services(link recommendations)
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CroSeRCross-language Service
Retriever• CroSeR
o A tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
o Based on automatic translation and Explicit Semantic Analysis
Web tool to support the linkage of a source eGov service catalog represented in any language to a target catalog represented in English
Based on Machine Translation and Explicit Semantic Analysis (ESA)
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CroSeR: Matching Approach
MachineTranslation of
Service Descriptions
Extraction of ESA-based representations and
indexing(Vector Space Model)
Top-k Service Retrieval
byCosine Similarity
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Experiments: Discussion
• CroSeR finds matchings that cannot be discovered by machine translation + keyword comparison
• CroSeR’s recommendations can support the users to refine the links
>>
=
GT:“Absentee Ballot”
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References (for details)
• Model and Experimental Evaluation @ISWC 2013o F. Narducci, M. Palmonari, G. Semeraro. Cross-Language Semantic
Retrieval and Linking of E-Gov Services. The Semantic Web - ISWC 2013 - 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, LNCS 8219, 130-145, Springer, 2013
• System Demo @ECIR 2014o F. Narducci, M. Palmonari, G. Semeraro. CroSeR: Cross-language
Semantic Retrieval of Open Government. 36th European Conference on Information Retrieval, Amsterdam, the Netherlands, April 13-16, 2014. To Appear
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