1. Web Query Analysis: A Functional Faceted Classification WING group meeting Nguyen Viet Bang

2. Last time story.. Classify queries into categories according to user search goals “A taxonomy by [Rosen & Levinson]”  Navigational: locate a specific website Example: “Stanford University”  Informational: find out about a topic (any pages containing the information) Example: “European history”  Resources: find a resource Example: “download Beatles lyrics” Various sub-categories

3. Related Work Lee et al, Kang & Kim  Used various computational methods to classify web queries using the above taxonomy  Only representational features (Part of Speech, etc..)  Most up-to-date work only able to classify into 2 broad categories: Navigational Information

4. Motivation Explore new facets of web queries to gain better understanding users’ information needs. Not only representational features

5. A faceted classification {Temporal sensitivity, Location sensitivity, Authoritativeness, Ambiguity} Each defines a supplementary search strategy of the search engine, that may be applied directly to help users  Examples: knowing that a query is temporally sensitive, the search engine wants to present to users a timeline of events. A possible application: guessing users’ search goals from this new taxonomy

6. Temporal sensitivity Value = {Yes, No} Temporal sensitivity (Query q) = ‘Yes’ if the concept q refers to has different interpretations at different time periods  Examples: “NUS world universities ranking”  Example: “US Presidential election”

7. Location sensitivity Value = {Yes, No} ‘Yes’ if the concept q refers to has different interpretations when taking into account different location factors.  Examples: “pizza restaurants”  Example: “tax laws”

8. Requirement for authoritativeness Value = {Yes, No} If the query requires authoritative answers, or requires transactions (e.g. downloads) from trusted sources.  Examples includes factoid queries Yes: “what is Russian capital” (only 1 acceptable answer) Yes: Software downloads No: “china history” (anything will do)

9. Ambiguity Value = (Polysemous, low specificity, High Specific_or_Indeterminate}. (Mutually exclusive)  Polysemous: The query takes more than one strong senses “UI”, “mustang”  Low specificity A too general query: “health” (the health industry? Health advices? Etc..??)  The rest “PS 2” (a particular product) “kelly blue book” (some vague queries)

10. Evaluation: A pilot survey To measure how people would agree on our definition of the facets Facets used (Ambiguity, Requirement for Special Collection, Temporal sensitivity, Authoritativeness) Results Agreement Average Ambiguity0.8 Req. for Special Col.0.77 Temporal sensitiv.0.95 Authoritativeness0.81 wang slashdot duke university hospital world war II start 2004 election dates in U.S. help quitting smoking travel italian renaissance-art independence day baseball death and injury

11. Ongoing work Validate the definitions Feasibility study of the implementation of each facet  Implemented: recognized the “Requirement for Authoritativeness”