Chapter 4 Query Languages

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Chapter 4Query Languages

• ..
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Introduction

• Cover different kinds of queries posed to text
  retrieval systems
• Keyword-based query languages
• include simple words and phrases as well as
  Boolean operators
• Pattern matching
• complement keyword searching with data retrieval
  capabilities
• Structural queries
• querying on structure of text

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Keyword-Based Querying

• Query is formulation of user information need
• Keyword-based queries are popular
• intuitive
• easy to express
• allow for fast ranking
• Query can be simply a word
• in general more complex combination of operations
  involving several words

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Single-Word Queries

• Most elementary query is a word
• Word is sequence of letters surrounded by
  separators
• some characters are not letters but do not split
  a word, e.g. hyphen in on-line
• Result of word queries is
• set of documents containing at least one of the
  words in query
• resulting documents are ranked
• term frequency (count of word in document)
• inverse document frequency (count of no. of
  documents in which word appears)

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Context Queries

• Complement single-word queries with ability to
  search words in given context, I.e. near other
  words
• words near each other signal higher likelihood of
  relevance than if they appear apart
• form phrases of words or find words which are
  proximal in text

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Phrase

• Sequence of single-word queries
• for instance, possible to search for word
  enhance and then word retrieval
• uninteresting words in text are not considered at
  all
• e.g. above example query could match text such as
  enhance the retrieval

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Proximity

• Sequence of single words or phrases is given
  together with maximum allowed distance between
  them
• For instance, above query stated as
• enhance and retrieval should occur within
  four words
• a possible match could be enhance the power of
  retrieval
• Distance can be measured in characters or words

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Boolean Queries

• Oldest form of combining keyword queries is to
  use Boolean operators
• Boolean query has following syntax
• atoms (I.e. basic queries) that retrieve
  documents, and of
• Boolean operators which work on their operands
  (sets of documents)
• query syntax tree can be defined
• leaves are basic queries
• internal nodes are operators

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Boolean Queries (Cont.)

• Retrieve all documents that contain the word
  translation as well as either the word syntax
  or the word syntactic

AND
OR
translation
syntactic
syntax
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Boolean Queries (Cont.)

• No ranking of retrieved documents provided
• document either satisfies query (retrieved) or
  does not (not retrieved)
• does not allow partial matching between document
  and user query
• to overcome this limitation, idea of fuzzy
  Boolean set of operators proposed
• instead of all the operands (AND) or at least in
  one of operands (OR), retrieve elements in some
  operands

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Natural Language

• Distinction between AND and OR completely blurred
  
• simply an enumeration of words and context
  queries
• all documents matching portion of user query are
  retrieved
• higher ranking assigned to documents matching
  more parts of query
• eliminated any reference to Boolean operators

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Pattern Matching

• Query formulation based on concept of pattern
  that allow retrieval of pieces of text that have
  some property
• Pattern is set of syntactic features that occur
  in text segment
• Segments satisfying pattern specification said to
  match the pattern
• We are interested in documents containing
  segments that match given search pattern

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Pattern Matching (Cont.)

• Most used types of pattern are
• words
• string (sequence of characters) that is a word in
  text
• prefixes
• string that form beginning of text word
• prefix comput retrieve documents with words
  such as computer, computation
• suffixes
• string that form termination of word
• suffix ters retrieve documents with words such
  as testers, computers

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• substrings
• string which can appear within word
• substring tal retrieve documents with words
  such as coastal, talk, metallic
• ranges
• pair of strings that match any word lying between
  them in lexicographical order
• alphabets sorted to order string into
  lexicographical order (dictionary order)
• range between words held and hold retrieve
  strings such as hoax, hissing

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• allowing errors
• word together with error threshold
• retrieves all text words similar to given word
• pattern may have errors (typing, spelling) and
  documents with words with erroneous variants are
  retrieved (with edit distance)
• if typing error splits flower into flo wer,
  still found with one error
• regular expression (r.e.)
• r.e. is built up by simple strings and operators
  like union, concatenation and repetition
• query like pro (plem tein) (s ?) (0 1
  2) will match words like problem02,
  proteins

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• Extended patterns
• subset of regular expressions expressed with
  simpler syntax
• classes of characters, I.e. some position in
  pattern matched by any character from pre-defined
  set (e.g. some characters must be digit, not a
  letter, vowel, etc.)
• conditional expressions, I.e. part of pattern may
  or may not appear
• wild characters, I.e. match any sequence in text
  (e.g. any word starts as flo and ends with
  ers which match flowers as well as flounders

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Structural Queries

• Allowing user to query texts based on structure,
  and not content
• mixing contents and structure in queries can pose
  powerful queries (much more expressive)
• An example
• select set of documents that satisfy certain
  constraints on content (using word, phrase, or
  patterns) and then
• structural constraints expressed using
  containment, proximity, or chapters, sections
  present in documents

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• Types of structures
• fixed structure
• hypertext
• hierarchical structure

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Fixed Structure

• Document has fixed set of fields
• each field has some text inside
• some fields not present in all documents
• fields not allowed to nest or overlap
• retrieval activity restricted to specifying that
  given pattern was to be found only in given
  fields
• this model reasonable when text collection has
  fixed structure

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Hypertext

• Retrieval from hypertext began as navigational
  activity
• user manually traverse hypertext nodes following
  links to search what he wanted
• not possible to query hypertext based on its
  structure
• WebGlimpse - interesting proposal to allow
  navigation plus ability to search by content in
  neighborhood of current node

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Hierarchical Structure

• Represent recursive decomposition of text
• natural model for many text collections
• Figure 4.3 shows example of hierarchical
  structure that consists of page of a book, its
  schematic view and parsed query to retrieve figure

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Hierarchical ModelsPAT Expressions

• Structure marked in the text by tags (as in HTML)
• defined in terms of initial and final tags
• each pair of initial and final tags defines a
  region, set of contiguous text areas
• area of region cannot nest or overlap
• possible to select areas containing other areas,
  contained in other areas, or followed by other
  areas

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Overlapped Lists

• Allows area of regions to overlap, but not to
  nest
• considers use of inverted list where words and
  also regions are indexed
• allows to perform set union, and to combine
  regions

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List of References

• Attempt to make definition and querying of
  structured text uniform, using common language
• the language allows for querying on path
  expressions, which describe paths in structure
  tree
• answers to queries are list of references
• reference is pointer to region of database

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Proximal Nodes

• Tries to find good compromise between
  expressiveness and efficiency
• specifies fully compositional language where
  leaves of query syntax tree formed by basic
  queries on contents or names of structural
  elements (e.g. all chapters)
• internal nodes combine results
• for efficiency, operations at internal nodes must
  relate nodes close in text

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Tree Matching

• Relies on single primitive tree inclusion
• interpret structure of text database and query as
  trees
• determine embedding of query into database which
  respects hierarchical relationships between nodes
  of query
• simple queries return roots of the matches