presentation at the EKAW 2010 workshop on knowledge injection and extraction from linked data on http://loted.eu.

1. LOTED: Exploiting Linked Data in Analyzing European Procurement Notices Francesco Valle, Mathieu d’Aquin, Tommaso Di Noiaand EnricoMotta Technical University of Bari, Electrical and Electronics Engineering Department Information Systems Research Group francescovalle84@gmail.com, t.dinoia@poliba.it Knowledge Media Institute, The Open University, Milton Keynes, UK {m.daquin, e.motta}@open.ac.uk

2. TED: European eProcurement A portal with daily updates about tenders in 27 European countries 14 Sectors All available in a collection of RSS feeds

4. TED LOTED Ontology SPARQL Endpoint … UK_Trans CZ_Comp DE_Agfo SE_Educ Every day: Updates from RSS feeds Enriched RDF repr. of tenders RDF representation of tenders Linker Entity Reconciliation New tender documents RDFExtractor geo-names DBPedia

5. http://loted.eu

6. <rdf:Descriptionrdf:about="http://loted.eu/data/tender/295984-2010"> <rdf:typerdf:resource="http://loted.eu/ontology#Tender"/> <loted:OJrdf:resource="http://loted.eu/data/officialJournal/194-2010"/> <loted:ND>295984-2010</loted:ND> <loted:hasSectorrdf:resource="http://loted.eu/data/sector/tran"/> <loted:PD>2010-10-06T00:00:00</loted:PD> <loted:hasSectorrdf:resource="http://loted.eu/data/sector/teeq"/> <loted:CYrdf:resource="http://loted.eu/data/country/UK"/> <loted:TWrdf:resource="http://sws.geonames.org/2653225/"/> <loted:AUrdf:resource="http://loted.eu/data/authorityName/Royal_Mail_Group_Limited"/> <loted:PRrdf:resource="http://loted.eu/data/procedure/2_-_Restricted_procedure"/> <loted:OLrdf:resource="http://loted.eu/data/language/EN"/> <loted:TDrdf:resource="http://loted.eu/data/document/7_-_Contract_award"/> <loted:PC>34911100_-_Trolleys</loted:PC> <loted:hasSectorrdf:resource="http://loted.eu/data/sector/mapr"/> <loted:ACrdf:resource="http://loted.eu/data/awardCriteria/2_-_The_most_economic_tender"/> <loted:TYrdf:resource="http://loted.eu/data/typeOfBid/9_-_Not_applicable"/> <loted:DS>2010-10-04T00:00:00</loted:DS> <loted:NCrdf:resource="http://loted.eu/data/contract/2_-_Supply_contract"/> <loted:HD>Member_states_-_Supply_contract_-_Contract_award_-_Restricted_procedure</loted:HD> <loted:TI>UK-Chesterfield:_trolleys</loted:TI> <loted:OC>34911100_-_Trolleys</loted:OC> <loted:RPrdf:resource="http://loted.eu/data/regulation/4_-_European_Communities"/> </rdf:Description> <rdf:Descriptionrdf:about="http://loted.eu/data/authorityName/Royal_Mail_Group_Limited"> <loted:IA>http://www.royalmailgroup.com/portal/rmg/jump1?catId=23200531&amp;amp;mediaId=23300561</loted:IA> <loted:IA>www.royalmailgroup.com</loted:IA> <loted:IA>www.royalmail.com</loted:IA> <loted:IA>http://www.royalmailgroup.com</loted:IA> <loted:IA>http://www.royalmail.com</loted:IA> <rdfs:label>Royal Mail Group Limited</rdfs:label> <rdf:typerdf:resource="http://loted.eu/ontology#4_-_Utilities"/> <rdf:typerdf:resource="http://loted.eu/ontology#6_-_Body_governed_by_public_law"/> <rdf:typerdf:resource="http://loted.eu/ontology#8_-_Other"/> </rdf:Description>

7. Some Details Website: http://loted.eu SPARQL endpoint: http://loted.eu:8081/LOTED1Rep/sparqlpage.jsp URI scheme: http://loted.eu/<data|ontology>/<type>/<ID> http://loted.eu/data/tender/295984-2010 http://loted.eu/ontology#Tender http://loted.eu/data/authorityName/Royal_Mail_Group_Limited http://loted.eu/data/country/UK http://sws.geonames.org/2653225/ (Chesterfield, UK) Triple store and query engine: Jena with TDB persistent storage. Updated everyday

8. But… This is just another interface to the data We could mostly have done the same with a database and some geolocation It is not so useful in terms of data analysis We have not learn much, we have no new knowledge We have not really used the links

9. So… Try mine Data+Links+LOD Discover knowledge in the connection between the local data and LOD datasets A first step: visual interface for data analysis based on “dimensions” coming both from the local data and from external data

10. Tender profiles

11. Generating data overviews Ranking criteria Distribution of the data

12. Using the links… Tender profiles dependent on a DBPedia property for the city in which the tender is 2 examples A general approach

13. Using the region from DBPedia Can also do manual ranking (e.g., north to south, east to west)

14. Using the political party from DBPedia Becomes crucial to assess the bias introduced by incomplete data/lack of coverage

15. Lessons Learned – Linked Data Extracting new data from the connection with external linked datasets is feasible And Valuable But is hard because The “Linked Data Infrastructure” is not ready: entity reconciliation, linking basic sameAs reasoning… Still difficult to find “exploitable” data, and this is only the first step of the challenge

16. Lessons Learned – Extracting knowledge from linked data New challenges: You don’t know what you will get You don’t know how much you will get You don’t know if what you get is good How do we match to user need? How can we reduce the effort in finding extracting something which might not be useful? How can we discover what needs to be discover?

17. Next Steps More advanced knowledge discovery techniques Detecting trends Identifying automatically the relevant dimensions Using more links Using the links more! Investigate the specific challenges of Knowledge Discovery from Linked Data

18. Thank You! m.daquin@open.ac.uk @mdaquin