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Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010

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One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not …

One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines.
We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems.
We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.

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  • 1. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada SEMANTIC TAGS GENERATION AND RETRIEVAL FOR ONLINE ADVERTISING 1Politecnico di Bari Via Orabona, 4 70125 Bari (ITALY) 2University of Trento Via Sommarive, 14 38100 Trento (ITALY) Roberto Mirizzi1, Azzurra Ragone1,2, Tommaso Di Noia1, Eugenio Di Sciascio1
  • 2. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Outline Tags in Web 2.0 → 3.0 Computational advertising NOT (Not Only Tag): semantic tag cloud generation DBpediaRanker: RDF ranking in DBpedia Conclusion and Future work
  • 3. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Who is using tags nowadays? and many more…
  • 4. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada What about Tags in Online Advertising?
  • 5. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada BigG (& co.) helps you… in half (i) …nice, but there is no “semantics” in it. You can not expand your keywords list exploiting the meaning of a term (keyword/tag/query) https://adwords.google.com/select/KeywordToolExternal Keyword Tool  Based on actual Google search queries  Generates keywords based on the content of a URL, words or phrases 1 2 3
  • 6. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada BigG (& co.) helps you… in half (ii) …nice, but there is no “semantics” in it. You can not expand your keywords list exploiting the meaning of a term (keyword/tag/query) Keyword Tool  Based on actual Google search queries  Generates keywords based on the content of a URL, words or phrases
  • 7. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Why not to use Semantic tags? Plugged into the Web 3.0 Disambiguation Relations among tags Machine understandable NOT: Not Only Tag http://sisinflab.poliba.it/not-only-tag/
  • 8. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada NOT: Not Only Tag Objectives  Assist advertisers to create more efficient ads campaigns  Support ads providers to properly match ads content to keywords in search engines Improve advertiser experience and ad selection
  • 9. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada What is behind NOT? (i)
  • 10. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada What is behind NOT? (ii) Comments  DBpedia resources are highly interconnected in the RDF graph  Not all the relevant resources for a given node are its direct neighbors 1. Explore the neighborhood of a resource to discover new relevant resources not directly connected to it 2. Rank the results
  • 11. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada DBpedia graph exploration in NOT Open_source_CMS Web_application_frameworks Content_management_systems Free_business_software … … Web_development Web_applications JavaServer_Faces Python_web_application_frameworks Zend_Framework Joomla_extensions skos:subject skos:broaderCategoryArticle Legend … …… Magento … PHP Drupal …
  • 12. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada The functional architecture Back-end Query engine Storage Tag Cloud Generator GUI Ext.InfoSources DBpedia Lookup Service Interface Delicious Yahoo! Bing Google Graph Explorer SPARQL Context Analyzer Ranker Offline computation Linked Data graph exploration Rank nodes exploiting external information Store results as pairs of nodes together with their similarity Runtime Search Start typing a query Query the system for relevant tags (corresponding to DBpedia resources) Show the semantic tag cloud 1 2 3 1 2 3 OfflinecomputationRuntimesearch 1 2 3 1 2 3
  • 13. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada DBpediaRanker: ranking ?r1 ?r2 isSimilar v hasValue einfo_sourc2 21 1 21 einfo_sourc21 )( ),( )( ),( ),( rf rrf rf rrf rrsim        viceversaandrandrbetweenwikilink,2 saor viceverrandrbetweenkwikilin,1 randrbetweenwikilinkno,0 ),( 21 21 21 21 rrorewikilinkSc )( ),( ),( 2 12 21 rl rrl rroreabstractSc  Graph-based and text-based ranking Ranking based on external sources
  • 14. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada DBpediaRanker: an example (i) wikilinkScore(Zend_Framework, PHP) = 2 abstractScore(Zend_Framework, PHP) = 1.0
  • 15. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada DBpediaRanker: an example (ii) sim(Zend_Framework, PHP)Google = 1.53e6 / 2.96e6 + 1.53e6 / 1.71e9 ≈ 0.52 + 0 delicious
  • 16. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada DBpediaRanker: context analysis The same similarity measure is used in the context analysis ?r1 ?c1 belongsTo v hasValue ?c2 ?c… ?cN C Example: C = {Programming Languages, Databases, Software} Does Dennis Ritchie belong to the given context? Algorithm: If(v>THRESHOLD) then r1 belongs to the context; add r1 to the graph exploration queue Else r1 does not belong to the context; exclude r1 from graph exploration EndIf
  • 17. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Evaluation (i) http://sisinflab.poliba.it/evaluation  Comparison of 5 different algorithms  50 volunteers  Researchers in the ICT area  244 votes collected (on average 5 votes for each users)  Average time to vote: 1min and 40secs
  • 18. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Evaluation (ii) http://sisinflab.poliba.it/evaluation/data 3.91 - Good
  • 19. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Conclusion  NOT: a prototype system for tag cloud generation in semantic advertising  DBpediaRanker: ranking algorithms for resources in DBpedia Future work  Use the back-end of the system to develop new interfaces for exploratory browsing  Improve ranking algorithms  Combine a content-based recommendation and a collaborative-filtering approach  Develop a platform to test our system with real ads about different domains
  • 20. CIKM 2010 – 19th ACM Internation Conference on Information and Knowledge Management October 29, 2010 – Fairmont Royal York, Toronto, Canada Q&A Thanks for your attention! SEMANTIC TAGS GENERATION AND RETRIEVAL FOR ONLINE ADVERTISING (CIKM 2010) If you're interested in learning more… 1. Roberto Mirizzi, Tommaso Di Noia. From Exploratory Search to Web Search and back. 4th Workshop for Ph.D. Students in Information and Knowledge Management (PIKM 2010) 2. Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Ranking the Linked Data: the case of DBpedia. 10th International Conference on Web Engineering (ICWE 2010) 3. Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tag cloud generation via DBpedia. 11th International Conference on Electronic Commerce and Web Technologies (EC-Web 2010) 4. Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tagging for crowd computing. 18th Italian Symposium on Advanced Database Systems (SEBD 2010) 5. Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic Wonder Cloud: exploratory search in DBpedia. 2th International Workshop on Semantic Web Information Management (SWIM 2010) - Best Workshop Paper at International Conference on Web Engineering (ICWE 2010) Roberto Mirizzi - mirizzi@deemail.poliba.it See you tomorrow at PIKM 2010 in Room Alberta at 4pm with… From Exploratory Search to Web Search and back