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Search by strategy

Search by strategy. Arjen P. de Vries arjen@acm.org CWI, Spinque, Delft University of Technology. Interactive Information Access. Feedback: Interaction improves information representation Faceted Browsing: Interaction can let user take over where machine would fail Search by Strategy:

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Search by strategy

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  1. Search by strategy Arjen P. de Vriesarjen@acm.org CWI, Spinque, Delft University of Technology

  2. Interactive Information Access • Feedback: • Interaction improves information representation • Faceted Browsing: • Interaction can let user take over where machine would fail • Search by Strategy: • Interaction can let user take over where system designer would fail

  3. Two extreme views on ‘search’ What is DB? From business applications Deductive reasoning Precise and efficient query processing Users with technical skills (SQL, XQuery, etc) and precise information needs Selection Books where category=‘CS’ What is IR? From digital libraries, patent collections, etc Inductive reasoning Best-effort processing Users with low technical skills and imprecise information needs Ranking Books about CS Note: SemWeb more DB than IR!!!

  4. Complex search tasks(or, “not-so-simple” search tasks, if you like) • I want to buy a house in Amsterdam and I want it with ‘sfeer’ but still in good shape • I can afford about €350K. I need 3 bedrooms, the size should be about 80m2. It should have a balcony or a backyard • Thecloserto the station and an AH, thebetter. BUT… I do not want to live in Amsterdam-Noord, unless there is a quick bus connection to the ferry • I may be willing to drop some of these constraints, but I’m notsurewhich

  5. Search = IR + DB • Complex search tasks mix exact (DB) and ranked (IR) searches, on structured (DB) and unstructured (IR) data • Current technical solutions support either/or • SQL or Excel for structured data (DB) • Google or Lucene for unstructured data (IR) • Combining results requires significant effort • copy & paste result sets between interfaces, “human (probabilistic) joins”

  6. Search = IR on-top-of DB ? • IR on-top-of DB: let exact and ranked operations both be processed by the same engine, so they can be mixed freely • IR responsible for ranking models, using DB as a data-access layer; no physical details necessary • DB responsible for reliable, dynamically optimised, data access; no logical details necessary

  7. IR on-top-of DB??? • Traditional, general-purpose DB technology cannot compete with custom IR search tools • Working assumption: using column stores should solve the efficiency problem

  8. IR on-top-of DB – part I let $c := doc("papers.xml")//DOC[author = "John Smith"] let $q := "//text[about(.//Abstract, IR DB integration)]" for $res in tijah-query($c, $q) return $res/title/text() I am an IRandDB expert + + Hiemstra, Rode, Van Os, Flokstra, OSIR 2006PF/Tijah: text search in an XML database system

  9. IR on-top-of DB Rölleke, Tsikrika, KazaiA general matrix frameworkfor modelling Information Retrieval “IR concepts to matrix spaces” FORMALISM Data Model Mismatch!!!To implement IR taskson top of DB is a tough job! ENGINEERING Cornacchia, De Vries, Van Ballegooij, KerstenSRAM (Sparse Relational Array Mapping) “matrix spaces to relational tables” MAPPING DATA AND QUERIES Bla bla Bla bla bla, bla bla. Bla, bla bla Bla bla Blabla Bla bla Bla bla bla, bla bla. Bla. DATA MANAGEMENT

  10. SRAM – query lifecycle

  11. IR on-top-of DB – part II let $c := doc("papers.xml")//DOC[author = "John Smith"] let $q := "//text[about(.//Abstract, IR DB integration)]" for $res in tijah-query($c, $q) return $res/title/text() I am an IR expert (needn’t be a DB expert as well) + + + Cornacchia, De Vries, ECIR 2007A Parametrised Search System

  12. SRAM for IR # Language Modelling langmod(Q,d,λ) = sum( [ lm_t(d,t,λ) * Q(t) | t ] ) lm_t(d,t,λ) = log( λ* DTf(d,t) + (1-λ) * Tf(t) ) # Okapi BM25 bm25(Q,d,k1,b) = sum( [ w_t(d,t,k1,b) * Q(t) | t ] ) w_t(d,t,k1,b) = Tidf(t) * (k1+1) * DTf(d,t) / ( DTf(d,t) + k1 * ((1-b) + b * Dnl(d)/avgDl) ) Retrieval_Models.ram (excerpt) DTnl = mxMult(mxTrnsp(LT), LD) # doc-term freq. Dnl = mvMult(mxTrnsp(LD), L) # doc lenght Tnl = mvMult(mxTrnsp(LT), L) # term freq. DTf = [ DTnl(d,t)/Dnl(d) | d,t ] # doc-term norm. freq. Tf = [ Tnl(t)/nLocs | t ] # term norm. freq. Indexing.ram (excerpt) mxTrnsp(A) = [ A(j,i) | i,j ] mxMult(A,B) = [ sum([ A(i,k) * B(k,j) | k ]) | i,j ] mvMult(A,V) = [ sum([ A(i,j) * V(j) | j ]) | i ] Linear_Algebra.ram (excerpt)

  13. Parameterised Search System (PSS) Cannot we ‘remove’ this IR engineer from the loop, like DBMS software removes the data engineer from the loop? Cornacchia, De Vries, ECIR 2007A Parametrised Search System

  14. Search by Strategy • Visually construct search strategies by connecting building blocks

  15. Strategy Builder

  16. From Patent to Inventor

  17. Search by Strategy • Visually construct search strategies by connecting building blocks • Each block describes either data or actions upon that data • A search strategy may include multiple data sources • Data sources are internally represented as quadruples, triples extended with an additional probability value • Actions are actually scripts expressed in Fuhr and Roelleke’s PRA (TOIS 1997)

  18. 1. Which universities/colleges hold patents? 2. Who are the inventors named in those patents? Real-life patent search example: Which researchers associated to universities and colleges should our Human Resources manager know to hire the right people on time? 3. Which inventors are active in the area of our company?

  19. Generate Search Engine

  20. Work In Progress…

  21. How Strategies Help • Strategies improve communication between search intermediary and user • Encapsulate domain expert knowledge • Abstract representation of search expert knowledge • Analyze information seeking process at any stage • Strategies facilitate knowledge management • Store / share / publish / refine • Strategies mix exact (DB) and ranked (IR) searches • Avoid the need for “human (probabilistic) joins”

  22. Implementation • PRA translates into SQL (!) • Current system setup using CWI’s MonetDB column-store • Strategies are dynamically transformed into a REST API

  23. Conclusion • Hand over control to the user (or, most likely, the search intermediary) • Patent information specialists • Digital forensics detectives • Librarians / archivists • Real estate agents • Travel agency

  24. Open Issues • Assist the user make the best out of their increased level of control • Integrate usage data live system to help improve or adapt strategies • Handle “even larger” scale data • Patent demo fine on ~17GB semi-structured data (i.e., Fairview Research’s Green Energy collection), without specific optimizations, even with fairly large strategies • Close the loop!

  25. Current Situation • index ; • repeat { • specify ; • retrieve • } until  IE IR + DB

  26. Desirable Situation • repeat { • index ; • specify ; • retrieve • } until  IR + DB + IE

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