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Liquid Query: Multi-domain Exploratory Search on the Web


The presentation about Liquid Queries performed at WWW2010

The presentation about Liquid Queries performed at WWW2010

Published in Technology
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  • complex search is characterized by: multiple searches, possibly over multiple sessions and spanning multiple sources of information; a combination of exploration and more directed information finding activities; the need of note-taking, the variation of the search goal during the search process.


  • 1. Liquid QueriesMulti-domain Exploratory Search on the Web
    Alessandro Bozzon, Marco Brambilla, Piero Fraternali, Stefano Ceri
    WWW 2010, Raleigh (NC)
    April, 29th 2010
  • 2. Context
    The Web is a huge, heterogeneousdata source:
    Unstructured information
    Structured and semi-structured data
    Linked Data
    Documents are integrated with semi-structured or structured data and enriched with semantics
    Search as a service
    Google, Yahoo, Amazon, Twitter, YouTube, MySpace, and Facebook
    Viability of systems based upon search service orchestration
  • 3. Problem Statement
    Web queries get increasingly complexand specialized as the searcher’s expertise grows
    Exploratory search
    Multi-step search processes
    From document searchto object/entity search
    Multi-domain queries
    multiple semantic fields of interest (e.g., travels, music, shows, food, movies, health, genetic diseases, etc.)
    domain-specific (aka, vertical) search engines provide more focused results than general-purpose ones
    the challenge is to match the results coming fromdifferentverticals
  • 4. An Example of multi-domain query
    “… search for upcoming concertsclose to an attractive location (like a beach, lake, mountain, natural park, and so on), considering also availability of good, close-byhotels …”
    Current approach the user can adopt:
    • Independently explore search services
    • 5. Manually combine findings
  • An Example of multi-domain query
    “… expand the search to get information about available restaurantsnear the candidate concert locations, news associated to the event and possible options to combine further events scheduled in the same days and located in a close-by place with respect to the first one…”
  • 6. Related work
    Exploratory Search: user’s intent is primarily to learn more on a topic of interest
    Marchionini, G. Exploratory search: from finding to understanding. Communications ACM 49(4): 41-46 (2006)
    “… exploratory search, that blends querying and browsing strategies, from retrieval that is best served by analytical strategies…”
    Definition and analysis of the problem
    White, R. W., and Drucker, S. M. Investigating behavioral variability in web search. 16th WWW Conf. (Banff, Canada, 2007)
    Complex Search Vs. Exploratory Search
    Aula, A., and Russell, D.M. Complex and Exploratory Web Search. ISSS: Information Seeking Support Systems Workshop (Chapel Hill, NC, USA, June 2008)
  • 7. Related work
    Topic based search: instance of exploratory search centered on the goal of collecting information on a subject matter of interest from multiple sources
    • Kosmix: topic discovery engine, keyword search, a topic page summarizes the most relevant information on the subject
    • 8. Hakia: resume pages for topics associated with user’s queries, natural language processing techniques
  • Related work
    Structured Object Search:process queries and present results that address entities or real world objects described in Web pages
    • Google Squared: keyword search, results collected in a table (called a square) featuring all the attributes relevant to the result items as columns headers
    • 9. Google Fusion Tables: upload data tables (e.g., spreadsheet files) and join (or “fuse”) the data in some column with other tables
  • The Liquid Query Approach
    “ A new paradigm allowing users to formulate and get responses to multi-domain queries through an exploratory information seeking approach, based upon structured information sources exposed as software services…”
    • Compositeanswers obtained by aggregating search results from various domains
    • 10. Highlightthe contribution of each search service
    • 11. Joinof results based on the structural information afforded by the search service interfaces
    • 12. Refinethe user query
    • 13. Re-shape result list
  • 14. Liquid Query Life Cycle
  • 15. Liquid Query Template
    • service interfaces: high level representation of a data provisioning service which comprises input parameters, output parameters, and ranking attributes
    • 16. connection patterns for joining the involved service interfaces
    • 17. additional selection or join predicates
    • 18. a default ranking function defined over the scores of service interfaces
    • 19. sorting and clustering options that can be applied on the extracted result set
    • 20. the size for the result pages
    • 21. a set of available query expansions towards further domains
    Example scenario: Initial query on Concert and Hotel objects; possible expansions on Events, related News, Restaurants; distance ranking for Concert, Restaurant and Hotel; date ranking on News and Photos
  • 22. Query Submission
    query conditions
    query conditions
  • 23. Query Execution
  • 24. Result Exploration (SaaP - Search as a Process)
    • The current set of combinations is not satisfactory, so user asks for more values for a service (more one) or for all services (more all)
    • 25. More concerts, more hotels, or more combinations
    • 26. Add new information about further domains for selected combinations (expand)
    • 27. Find close-by restaurants or co-located events
    • 28. Aggregate information to ease analysis and readability (clustering, grouping)
    • 29. Group events by venue
    • 30. Reduce the number of shown items through filtering
    • 31. Total walked distance for the night
    • 32. Re-order (ranking or sorting)
    • 33. Calculate derived values from existing ones
    • 34. Total walked distance for the night
    • 35. Alternative data visualization
    • 36. Map, parallel coords, ...
  • The back-end: YQL - Yahoo Query Language
    A language and a platform that let Web application query, filter, and combine data from different sources across the Internet through SQL-like statements
    Remote Query Interaction PrimitivesYQL commands + locally computed join operation
    Local Resultset Interaction and Local Manipulation Primitives client side (GoogleGears DB)
    Data Visualization Primitives mashups with visualization libraries
    SELECT title, city, Rating.AverageRating,
    latitude, longitude
    FROM Local.Search (0,100)
    WHERE (latitude,longitude) IN
    (SELECT latitude, longitude
    FROM Upcoming.Events
    WHERE category_id=“1” AND
    min_date="2009-12-01" AND
    max_date="2009-12-31" AND
    location = "San Francisco" AND
    )AND query=“Hotel” AND radius = “0.5”
    AND Rating.AverageRating > 2
    AND Rating.TotalRatings > 30
    Yahoo Upcoming
    Yahoo Search
    Select / Create / Delete
    Remote Filter, Sort, Limit
    Local Filter, Sort, Limit
  • 37. Liquid Query Prototype Demo
  • 38. Overview of Search Computing and Liquid Query
  • 39. Liquid Queries Prototype Architecture
    Query, Table and Action Configuration
    Local Data Configuration
    LQ Widget Configuration
    Query Configuration
    YQL Service Configuration
  • 40. Liquid Query over Linked Data Demo
    Example Scenario: Scientific Publication search
  • 41. Conclusions and future work
    Liquid Query
    • exploits the power of underlying vertical search services by joining and matching results
    • 42. grants multi-domain exploratory search through a combination of search interfaces and data visualization
    • 43. Details: “Search Computing – Challenges and Directions”Ceri, Brambilla (eds.), book by Springer, April 2010
    Future and ongoing works
    • thorough user evaluation studies
    • 44. design of additional interaction and visualization options
    • 45. implementation of visual tools for Liquid Query configuration
    • 46. Integration of the Search Computing backend engine
    • 47. Application of diversification and personalization to results
  • Thanks! Questions?