The recent blow up of crowd computing initiatives on the web calls for smarter methodologies and tools to annotate, query and explore repositories. There is the need for scalable techniques able to return also approximate results with respect to a given query as a ranked set of promising alternatives. In this paper we concentrate on annotation and retrieval of software components, exploiting semantic tagging relying on DBpedia. We propose a new hybrid methodology to rank resources in this dataset. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results, social tagging systems and wikipedia-related information. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users. The system is available at http://sisinflab.poliba.it/not-only-tag/ SEBD 2010 - 18th Italian Symposium on Advanced Database Systems presented by Roberto Mirizzi (http://sisinflab.poliba.it/mirizzi - roberto.mirizzi -at- gmail.com) Rimini (ITALY), June 20-23, 2010

1. Semantictaggingforcrowdcomputing Roberto Mirizzi1, Azzurra Ragone1,2, Tommaso Di Noia1, Eugenio Di Sciascio1 1Politecnico di Bari Via Orabona, 4 70125 Bari (ITALY) 2Universityof Trento Via Sommarive, 14 38100 Trento (ITALY)

2. Who is using tags? and manymore…

3. WhynottouseSemantictags? Pluggedinto the Web 3.0 Disambiguation Relations amongtags Machineunderstandable NOT:NotOnlyTag http://sisinflab.poliba.it/not-only-tag/

4. DBpedia-Ranker: architecture Runtimesearch Offline classification System architecture Linked Data graph exploration Rank nodes exploiting external information Store results as pairs of nodes together with their similarity Start typing a tag Query the system for relevant tags (corresponding to DBpedia resources) Show the semantic tag cloud DBpedia 1 2 1 SPARQL 3 1 1 GRAPH EXPLORER TAGS ExternalInformation Sources 2 3 2 3 Google 3 2 WEB INTERFACE STORAGE Yahoo! RANKER Bing Delicious

5. DBpedia-Ranker: ranking ?r1 ?r2 isSimilar hasValue v

6. DBpedia-Ranker: contextanalysis The samesimilaritymeasureisused in the contextanalysis C ?c1 Algorithm: If(v>THRESHOLD) then r1belongsto the context; add r1to the graphexplorationqueue Else r1doesnotbelongto the context; exclude r1fromgraphexploration EndIf ?c2 belongsTo ?r1 ?c… ?cN hasValue Example: C = {ProgrammingLanguages, Databases, Software} DoesDennis Ritchiebelongsto the givencontext? v

7. Evaluation (I) http://sisinflab.poliba.it/evaluation

8. Evaluation (II) http://sisinflab.poliba.it/evaluation/data

10. Extract more fine grainedcontexts

11. Enrich the extractedcontextusingalsorelevantproperties

12. Integrateourapproachwithrealexistingsystems