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NLP2RDFIntegration of Data, Tools andApplicationswith RDF/OWL in the Areas of Textmining andLinguistics PhD Thesis, Sebastian Hellmann
Extensive Topic – Whatisthecore? Features forMachine Learning Whichfeatures do I needfor a certain Textmining task? An introductoryexample : Resources:  ,[object Object]
Database withAgeandOccupationGoal: predictincomeofpersons ,[object Object],=> Color ofeyesandhaircutprobably irrelevant!
Basic idea: a benchmarkingframework Input:  ,[object Object]
Text
Training/testdataOutput: ,[object Object],Do I need POS tags toclassifyTourismdocuments? Prerequisites: ,[object Object]
Data needs a standardizedformat,[object Object]
Basic idea: a benchmarkingframework Google Code project was created ,[object Object]
Ontologieswerefoundandintegrated
Pipeline implemented
Pluginsystemimplemented
SomeresultswereachievedBut… ,[object Object]
Integration boundto Java
Data sourceswere not sufficient
Wikipedia/DBpediatoocourse-grained
Speed ofintegrationtooslow,[object Object]
Why RDF and OWL ? RDF makesdataintegration easy: URIref, LinkedData OWL isbased on Description Logics (Guarded Fragment) Availabilityof open datasets (accessandlicence) Diverse serializationsforannotations: XML, Turtle, RDFa+XHTML Scalabletoolsupport (Databases, Reasoning) 6.     Iftheonlytoolyouhaveis a hammer, everythinglookslike a nail.
LOD Cloud - over 26 Billion Facts DBpediaiscentral: ,[object Object]
Crystalizationpoint (earlybird)Linking Open Data clouddiagram, by Richard CyganiakandAnja Jentzsch. http://lod-cloud.net/
Simplified: ,[object Object]
Links areHTTP-Foreign Keys,[object Object]
URIs pointingtosubjects in the same table
URIs pointingtosubjects in anyothertable,[object Object]
SPARQL – optimizationsfortablejoins

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NLP2RDF Wortschatz and Linguistic LOD draft