Dagstuhl Seminar 08111 on Ranked XML Querying Outline

Dagstuhl Seminar 08111 on Ranked XML Querying

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems – Personalization – Relevance Feedback for Interactive Query Refinement • Conclusions

About Dagstuhl Seminars • Participants: renowned researchers of international standing and promising young scholars. • Report on current, as yet unconcluded research work and ideas, and conduct indepth discussions. • Almost every week throughout the year. – http: //www. dagstuhl. de/en/program/dagstuhlseminars/

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems • Making DB and IR (Socially) Meaningful – Personalization – Relevance Feedback for Interactive Query Refinement • Conclusions

Making DB and IR (Socially) Meaningful Sihem Amer-Yahia (Yahoo Research - New York)

Social Content Sites Motivation • Lots of data and opinions – Collaborative tagging sites: Flickr, del. icio. us, etc. – Collaborative reviewing sites: Y! Movies, Y! Local, etc. • Key features of these sites – User-contributed content, user relationships (user’s network), user ratings • Hotlists, search results, recommendations are offered to users with lists of ranked content. • The accuracy of ranking is tied to not only relevance (in a traditional Web sense) but also people whose opinion matters

Recommendations (Amazon) But who are these people ?

Explaining Recommendations in x. qui. site 8

Yahoo! Movies Now

Reviewers Biases in Yahoo! Movies

New Ranking Semantics • Not only relevance (in a traditional Web sense) but also about people whose opinion matters. • flowers Take into account social connection information • Relevance Factors – – – Text features: title, description (TF-IDF) Timeliness and freshness. Incoming links and tags. Popularity. Social distance: user’s social network Relevance for people

Conclusions • Efficient and effective recommendation platform – Serve (socially! ) relevant content to users – Better recommendations • relevance determined by people who matter to you! • social context, explanation, diversity, temporality, etc – Characterize users’ interests and connections • I enjoy watching Schindler’s list with my parents • and very different movies with my friends!

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems – Personalization • Personalizing XML Search with PIMENTO • Multidimensional Search for Personal Information Systems – Relevance Feedback for Interactive Query Refinement • Conclusions

Personalizing XML Search with PIMENTO Irini Fundulaki (ICS-FORTH, Greece)

Motivation • XML search has become popular • Personalization is becoming important – Large number of users, with focused and different needs. • XML Personalization is essential!

Example car dealer car des location make good condition NJ car price color des Mustang $1000 red low mileage location color YNC red price $1000 Looking for a car, with a price lower than $2000, in good condition User resides in NYC, and prefers red cars with low mileage A different user prefers Mustangs

Summary • XML queries are both on structure and content • Given a user interest: – Customize query context: modify candidate set of answers using conditions on both structure and keywords – Customize ranking of answers • Adapt top-k processing to account for user interests.

PIMENTO car * • Query: des • User Profiles: price location ftcontains(“good condition”) <2000 NYC – Scoping rules: If true then add parent (car, location) & ftcontains (location, ”NYC”) – Ordering rules: x. tag=car & y. tag=car & x. color=‘red’ & y. color≠’red’ →x� y • Query Personalization: – Rewriting a user query using scoping rules – Ranking query answers using ordering rules

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems – Personalization • Personalizing XML Search with PIMENTO • Multidimensional Search for Personal Information Systems – Relevance Feedback for Interactive Query Refinement • Conclusions

Multi-Dimensional Search for Personal Information Systems Amélie Marian (Rutgers University)

Motivation • Large collections of heterogeneous data. • Need simple and efficient search approach • Typical desktop search tools use – Keyword search for ranking – Possibly some additional conditions (e. g. metadata, structure) for filtering • e. g. Find a pdf file created on March 21, 2007 that contains the words “proposal draft” – Filtering conditions: *. pdf, 03/21/2007 – Ranking expression: “proposal draft” • Miss some relevant files: *. txt documents created on 03/21/2007 contain words “proposal draft”

Multi-Dimensional Search • Allow users to provide fuzzy structure and metadata conditions in addition to keyword conditions. • Three query dimensions: (content, structure, metadata) • Example: – For $i In /File[File. Sys. Metadata/File. Date=’ 03/21/07’] For $j In /File[Content. Summary/Word. Info/Term=‘proposal’ AND Content. Summary/Word. Info/Term=‘draft’] For &m In /File[File. Sys. Metadata/File. Type=‘pdf’] WHERE $i/@file. ID=$j/@file. ID AND $i/@file. ID=$m/@file. ID RETURN $i/file. Name • Individually score each dimension and then integrate three dimension scores into a meaningful unified score.

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems – Personalization – Relevance Feedback for Interactive Query Refinement • Conclusions

Relevance Feedback in Top. X Search Engine Ralf Schenkel

Users vs. Structural XML IR I need information about //professor[contains(. , SB) a professor in SB who and contains(. //course, IR] teaches IR. Structural query languages System supports to generatedo not work in practise: good structured queries: is unknown or search) • • Schema User interfaces (advanced • heterogeneous Natural language processing Languagequery is toorefinement complex • • Interactive • Results often unsatisfying

Relevance Feedback for Interactive XML not (Fagin) Query Refinement Query evaluation XML IR 2 index IR 3 Fagin index 4 IR index XML Feedback for XML IR: • Start with keyword query • Find structural expansions • Create structural query … 1. User submits query 2. User marks relevant and nonrelevant docs 3. System finds best terms to distinguish between relevant and nonrelevant docs 4. System submits expanded query 1

Structural Features article frontmatter body sec Sec Semistructured data author: Baeza-Yates subsec XML has evolved User marks relevant result backmatter p p subsec p With the advent of XSLT Possible features: Content of result C: XML Tag + Content of descendants D: p[XSLT] Tag + Content of descenof ancestors dants of ancestors A: sec[data] AD: article//author[Baeza]

Query Construction Initial query: query evaluation author[Baeza] article sec[data] descendantor-self axis needs schema information! *[query evaluation] XML] p[XSLT] Content of result Tag + Content of descendants of ancestors C: XML D: p[XSLT] A: sec[data] AD: article//author[Baeza]

Conclusions • Queries with structural constraints to improve result quality • Relevance Feedback to create such queries • Structure of collection matters a lot

Outline • About Dagstuhl Seminars • Interesting Talks – Social Recommender Systems – Personalization – Relevance Feedback for Interactive Query Refinement • Conclusions

Conclusions (1) • XQuery and exact matches for querying XML documents are not likely to be sufficient. • Techniques based on approximate matching of query content and structure, scoring potential answers, and returning a ranked list of answers are more appropriate. • Trend: blending together the techniques addressed by DB, IR and the Web/Applications communities.

Conclusions (2) • Opinions on ranking: – Ranking for XML Search should take into account the structure (not contents only) – Add scoring to ranking with preferences – Declaring properties of scoring functions/process so they can be matched against application needs. – The accuracy of ranking in social networks is tied to users behavior. – Data uncertainty presents interesting issues in uncertain top-k queries.

Thank you! Questions?