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Query Models

Query Models. Use Types What do search engines do. What we have covered. What is IR Evaluation Tokenization and properties of text Vector models of documents Web crawling This time Query models. Index. Query Engine. Interface. Indexer. Users. Crawler. Web.

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Query Models

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  1. Query Models • Use • Types • What do search engines do

  2. What we have covered • What is IR • Evaluation • Tokenization and properties of text • Vector models of documents • Web crawling • This time • Query models

  3. Index Query Engine Interface Indexer Users Crawler Web A Typical Web Search Engine

  4. Query Engine Index Users Interface On-line Indexer Crawler Off-line Web Online vs offline processing

  5. Queries Index Query Engine Interface Indexer Users Crawler Web A Typical Web Search Engine

  6. Why the interest in Queries? • Queries are ways we interact with IR systems • Expression of an information need • Nonquery methods? • Types of queries?

  7. Issues with Query Structures Matching and ranking criteria • Given a query, what documents are retrieved? • In what order (rank)?

  8. Types of Query Structures Query Models (languages) – most common • Boolean Queries • Extended-Boolean Queries • Vector space Boolean • Vector queries • Natural Language Queries • Others?

  9. Simple query language: Boolean • Earliest query model • Terms + Connectors (or operators) • terms • words • normalized (stemmed) words • phrases • thesaurus terms • connectors • AND • OR • NOT • Ex: Beethoven AND sonata

  10. Truth Tables – Boolean Logic Presence of P, P = 1 Absence of P, P = 0 True = 1 False = 0

  11. Problems with Boolean Queries • Ranking? • Incorrect interpretation of Boolean connectives AND and OR • Example - Seeking Saturday entertainment Queries: • Dinner AND sports AND symphony • Dinner OR sports OR symphony • Dinner AND sports OR symphony

  12. Order of precedence of operators Example of query. Is • A AND B • the same as • B AND A • Why?

  13. Sample Boolean Queries • Cat • Cat OR Dog • Cat AND Dog • (Cat ANDDog) • (Cat AND Dog) OR Collar • (Cat AND Dog) OR (Collar AND Leash) • (Cat OR Dog) AND (Collar OR Leash)

  14. Satisfaction of Boolean Query • (Cat OR Dog) AND (Collar OR Leash) • Each of the following column combinations works: • Cat x x x x • Dog x x x x x • Collar x x x x • Leash x x x x Others?

  15. Order of Preference • Define order of preference • EX: a OR b AND c • Infix notation • Parenthesis evaluated 1st with left to right precedence of operators • Next NOT’s are applied • Then AND’s • Then OR’s • a OR b AND c becomes • a OR (b AND c)

  16. Infix Notation • Usually expressed as INFIX operators in IR • ((a AND b) OR (c AND b)) • NOT is UNARY PREFIX operator • ((a AND b) OR (c AND (NOT b))) • AND and OR can be n-ary operators • (a AND b AND c AND d) • Some rules - (De Morgan revisited) • NOT(a) AND NOT(b) = NOT(a OR b) • NOT(a) OR NOT(b)= NOT(a AND b) • NOT(NOT(a)) = a

  17. DNFs and CNFs All queries can be rewritten as • Disjunctive Normal Forms (DNFs) • Conjunctive Normal Forms (CNFs) • DNF Constituents: • Terms (words or phrases) • Conjuncts (terms joined by ANDs) • Disjuncts (conjuncts joined by ORs) • Ex: (A AND B) OR (A ANDNOTC) • CNF Constituents: • Terms (words or phrases) • Disjuncts (terms joined by ORs) • Conjuncts (disjuncts joined by ANDs) • Ex: (A OR B) AND (A ORNOTC)

  18. Effect of CNFs • All complex Boolean queries can be simplified • Why do reference librarians like CNFs? • AND’s reduce the size of the set returned and are easily expandable • So do minus’s

  19. Boolean Searching Formal Query: cracksANDbeams ANDWidth_measurement ANDPrestressed_concrete “Measurement of the width of cracks in prestressed concrete beams” Cracks Width measurement Beams Relaxed Query: (C AND B AND P) OR (C AND B AND W) OR (C AND W AND P) OR (B AND W AND P) Prestressed concrete

  20. Ordering (ranking) of Retrieved Documents • Pure Boolean has no ordering • Term is there or it’s not • In practice: • order chronologically • order by total number of “hits” on query terms • What if one term has more hits than others? • Is it better to have one of each term or many of one term?

  21. Boolean Query - Summary • Advantages • simple queries are easy to understand • relatively easy to implement • Disadvantages • difficult to specify what is wanted • too much returned, or too little • ordering not well determined • Dominant language in commercial systems until the WWW

  22. Vector Space Model • Queries treated as small documents • Documents and queries are represented as vectors in term space • Terms are usually stems • Documents represented by binary vectors of terms • Query and Document weights are based on length and direction of their vector • A vector distance measure between the query and documents is used to rank retrieved documents

  23. Document Vectors • Documents are represented as “bags of words” • Words are terms with no order • Represented as vectors when used computationally • A vector is like an array of floating point values • Has direction and magnitude • Each vector holds a place for every term in the collection • Therefore, most vectors are sparse

  24. Queries Vocabulary (dog, house, white) Queries: • dog (1,0,0) • house (0,1,0) • white (0,0,1) • house and dog (1,1,0) • dog and house (1,1,0) • Show 3-D space plot

  25. Documents (queries) in Vector Space t3 D1 D9 D11 D5 D3 D10 D4 D2 t1 D7 D6 D8 t2

  26. Documents in 3D Space Assumption: Documents that are “close together” in space are similar in meaning.

  27. Vector Query Problems • Significance of queries • Can different values be placed on the different terms – eg. 2dog 1house • Scaling – size of vectors • Number of words in the dictionary? • 100,000

  28. Proximity Searches • Proximity: terms occur within K positions of one another • pen w/5 paper • A “Near” function can be more vague • near(pen, paper) • Sometimes order can be specified • Also, Phrases and Collocations • “United Nations” “Bill Clinton” • Phrase Variants • “retrieval of information” “information retrieval” Proximity - wikipedia

  29. Filters/field limiters • Filters: Reduce set of candidate docs • Often specified simultaneous with query • Usually restrictions on metadata • restrict by: • date range • internet domain (.edu .com .berkeley.edu) • author • size • limit number of documents returned

  30. Natural Language Queries • The “Holy Grail” of information retrieval • Issues in Natural Language Processing • syntax • semantics • pragmatics • speech understanding • speech generation

  31. What do search engines do? • Tags • Title • Meta • Term frequency and location • Popularity • Others

  32. What do search engines do? • Collection of various methods, sometimes called pseudo-Boolean • quotes, minus, plus • pseudo AND • truth in vs in truth • stop words?

  33. What does Google do? Basic search Search operators

  34. UC Berkeley Search Engine Guide http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/SearchEngines.html

  35. UC Berkeley Search Engine Guide http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/SearchEngines.html

  36. Old:Search Engine Query Differences

  37. Older: Search engine query models

  38. Search query string The portion of a dynamic URL that contains the search parameters when a dynamic Web site is searched. Query strings do not exist until a user plugs the variables into a database search, at which point the search engine will create the dynamic URL with the query string based on the results. Query strings typically contain ? and % characters.

  39. Lucene Basics • Searches are supported through a wide range of Query options • Keyword • Terms • Phrases • Wildcards • Many, many more

  40. QueryParser syntax examples

  41. Types of Query Structures Query Models (languages) – most common • Boolean Queries • Old model • Vector queries • Very common - in all search engines to some extent • Web queries • Search engines • Probabilistic models • Mostly research (Indri) • Holy grail of search • Natural Language Queries

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