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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. Semantic Tags Generation and Retrieval for Online Advertising
    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. Outline
    • Tags in Web 2.0 -> 3.0
    • 3. Computational advertising
    • 4. NOT (Not Only Tag): semantic tag cloud generation
    • 5. DBpediaRanker: RDF ranking in DBpedia
    • 6. Conclusion and Future work
  • Who is using tags nowadays?
    and manymore…
  • 7. What about Tags in Online Advertising?
    meat
    cooking
    fish
    spaghetti
    pasta
    dessert
    food
    recipes
  • 8. BigG (& co.) helps you… in half (i)
    Keyword Tool
    • Based on actual Google search queries
    • 9. Generates keywords based on the content of a URL, words or phrases
    1
    2
    3
    …nice, butthereis no “semantics” in it.
    You can notexpandyourkeywordslistexploiting the meaningof a term (keyword/tag/query)
    https://adwords.google.com/select/KeywordToolExternal
  • 10. BigG (& co.) helps you… in half (ii)
    Keyword Tool
    • Based on actual Google search queries
    • 11. Generates keywords based on the content of a URL, words or phrases
    …nice, butthereis no “semantics” in it.
    You can notexpandyourkeywordslistexploiting the meaningof a term (keyword/tag/query)
  • 12. WhynottouseSemantictags?
    Pluggedinto the Web 3.0
    Disambiguation
    Relations amongtags
    Machineunderstandable
    NOT:NotOnlyTag
    http://sisinflab.poliba.it/not-only-tag/
  • 13. NOT: NotOnlyTag
    Objectives
    • Assist advertisers to create more efficient ads campaigns
    • 14. Support ads providers to properly match ads content to keywords in search engines
    Improve
    advertiserexperienceand ad selection
  • 15. Whatisbehind NOT? (i)
  • 16. Whatisbehind NOT? (ii)
    Comments
    • DBpedia resources are highly interconnected in the RDF graph
    • 17. Not all the relevant resources for a given node are its direct neighbors
    Explore the neighborhood of a resource to discover new relevant resources not directly connected to it
    Rank the results
  • 18. DBpedia graph exploration in NOT
    Magento


    Content_management_systems
    Free_business_software

    Web_development
    Web_applications
    PHP

    Open_source_CMS
    Web_application_frameworks
    Zend_Framework

    JavaServer_Faces
    Python_web_application_frameworks
    Joomla_extensions
    Drupal


    Legend
    skos:subject
    skos:broader
    Category
    Article
  • 19. The functionalarchitecture
    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
    Back-end
    Google
    1
    Bing
    SPARQL
    Ext. Info Sources
    Yahoo!
    Graph Explorer
    Context Analyzer
    1
    2
    Offline computation
    Delicious
    Ranker
    2
    3
    DBpedia Lookup Service
    Storage
    3
    1
    2
    2
    1
    Query engine
    Tag Cloud Generator
    GUI
    Runtime search
    3
    3
    Interface
  • 20. DBpediaRanker: ranking
    Graph-based and text-based ranking
    ?r1
    ?r2
    isSimilar
    hasValue
    v
    Ranking based on external sources
  • 21. DBpediaRanker: anexample (i)
    wikilinkScore(Zend_Framework, PHP) = 2
    abstractScore(Zend_Framework, PHP) = 1.0
  • 22. delicious
    sim(Zend_Framework, PHP)Google = 1.53e6 / 2.96e6 + 1.53e6 / 1.71e9 ≈ 0.52 + 0
    DBpediaRanker: anexample (ii)
  • 23. DBpediaRanker: 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 Ritchie belong to the givencontext?
    v
  • 24. Evaluation (i)
    • Comparison of 5 different algorithms
    • 25. 50 volunteers
    • 26. Researchers in the ICT area
    • 27. 244 votes collected (on average 5 votes for each users)
    • 28. Average time to vote: 1min and 40secs
    http://sisinflab.poliba.it/evaluation
  • 29. Evaluation (ii)
    3.91 - Good
    http://sisinflab.poliba.it/evaluation/data
  • 30. Conclusion
    • NOT: a prototype system for tag cloud generation in semantic advertising
    • 31. DBpediaRanker: ranking algorithms for resources in DBpedia
    Future work
    • Use the back-end of the system to develop new interfaces for exploratory browsing
    • 32. Improve ranking algorithms
    • 33. Combine a content-based recommendation and a collaborative-filtering approach
    • 34. Develop a platform to test our system with real ads about different domains
  • Q&A
    Semantic Tags Generation and Retrieval for Online Advertising (CIKM 2010)
    Thanksforyourattention!
    See you tomorrow at PIKM 2010in Room Alberta at 4pmwith…
    From Exploratory Search to Web Search and back
    If you're interested in learning more…
    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)
    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)
    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)
    Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio. Semantic tagging for crowd computing. 18th Italian Symposium on Advanced Database Systems (SEBD 2010)
    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