DYNAMIC ELEMENT RETRIEVAL IN A STRUCTURED ENVIRONMENT

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DYNAMIC ELEMENT RETRIEVAL IN A STRUCTURED
ENVIRONMENT

• MAYURI UMRANIKAR

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CONTENTS

• Introduction
• Retrieval Environment
• - The Vector Space Model
• - INEX Environment
• - Flexible Retrieval System
• Method Used for Retrieval
• - Document Tree Construction
• - Ranking of Elements
• - Output
• Experiments
• Conclusions

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INTRODUCTION

• Extensible Markup Language (XML) preferred for
  representing documents and due to increase of
  documents, issue of element retrieval arises
• Focus on retrieval of relevant elements rather
  than entire document
• INEX INitiative for Evaluation of XML Retrieval
  
• Flexible Mechanisms
• Different Approaches
• Term Weighting

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RETRIEVAL ENVIRONMENT

• 2 Factors Issues when focus moves from
  documents to components and Saltons Vector Space
  Model
• Vector Space Model Weight number of times a
  term occurs in the document
• Foxs Extended Vector Space Model Incorporation
  of objective identifiers
• Document vector consists of subvectors
• Contain text independently indexed, weighted,
  searched and retrieved
• Term Weighting weighting within subjective
  vectors
• Smart Experimental Retrieval System

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INEX ENVIRONMENT

• Content Only (CO) ignore document structure,
  like typical queries, specify only content of
  search
• Content and Structure (CAS) explicitly refer to
  structure, exhaustive and specific
• CO query directly to user, CAS additional
  filtering and search of body portion
• CAS returns rank ordered list of elements
• INEX-EVAL uses measures of recall and precision
• ( fig, exhaustivity, specificity mapped to a
  single relevance)
• results are ranked

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FLEXIBLE RETRIEVAL SYSTEM

• Smart Format documents and topics translated,
  indexed as extended vectors
• Subjective vectors contain content bearing
  terms
• Objective vectors serve as filters on result
  returned by CAS queries
• Extended vector subjective vector, terms having
  a paragraph in body subvector
• Lnu-ltu weighting
• Dynamic flexible retrieval- tree representation,
  rank ordered list by lnu weights

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METHOD FOR FLEXIBLE RETRIEVAL

• Input Query Q given and paragraph, retrieve
  rank ordered list, terminal modes
• N top ranked paragraphs as input selected
• Set of paragraphs used to identify documents
  elements generated and returned as output
• Document Tree Needs information of structure
• Terminal nodes
• Pre-order traversal
• Terminal nodes found in paragraph index

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SIMPLE XML DOCUMENT AND ITS SCHEMA
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CONSTRUCTION OF DOCUMENT TREE

• For query Q, n top ranked paras used to build
  trees
• Leaf elements or terminal nodes - paragraph nodes
• Each leaf represented by term-freq weighted
  frequency vector
• 1st gather all leaf nodes, terminal nodes done
• 2nd merge children vectors for parents
• Document schema determine merging
• Parent unique terms of children, term freq
  weighted parent vector( has content of children)
• Process in recursive manner done

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RANKING OF ELEMENTS

• Set of elements of document tree generated
• Problem- structured retrieval rank ordered list
  of elements
• Method used All-element index( separate
  representation for each element of each document
  and weighting information)
• Lnu weights - elements variable length, do not
  require global frequency
• Normalization and length failing results in
  biased values
• Pivot document length probability of relevance
  probability of retrieval
• Slope- amount of tilting
• Pivoted Normalization reduces difference
• Lnu term weights
• ((1log(term_freq))/ (1log(avg_term_freq))
  )/((1-slope)slope((no_unique_terms)/pivot)

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• Ltu weighting N collection size, nk no of
  elements
• ((1log(term_freq))/log(N/nk))/
• ((1-slope)slope(no_unique_terms)/pivot))
• N,nk element dependent, should be known through
  indexing
• We move up N count elements of each type
• Nk inverted file entry in paragraph index,
  mapping identifiers and xpaths (given)

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OUTPUT OF FLEXIBLE RETRIEVAL

• Select another leaf node, gather siblings,
  construct document tree, calculate Lnu term
  weights, Ltu weighted query produce another rank
  ordered list
• After n top ranked exhausted, last list produced,
  merge lists
• Single set of elements rank ordered correlation
  Q
• Comparison flexible retrieval all-element
  index
• identical set of n paragraphs i/p to
  flexible retrieval have all paragraphs same
  values used for Lnu-ltu

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ALGORITHM
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EXPERIMENTS

• Paragraph result set of extended vectors
  representing paragraph
• CO subvector represents subjective portion,
  body subvector important (content of element and
  not type) contained in body
• Tree Representation

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FACTORS OF INTEREST

• Slope, pivot for Lnu-ltu
• Effective structure retrieval
• Can be determined empirically, applied from one
  collection to other Generic
• N- no of paragraphs input, sets upper bound on
  number per query
• Actual trees depend on number of paragraphs
  having same group or same document

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EXPERIMENTS DONE

• All-element and dynamic/flexible retrieval
  experiments and results
• - body-only retrieval
• Correlation between element and query vector
  produced correlation of body elements only
• Table 1

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RESULTS

• Tables

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• Result equivalent
• Flexible more efficient file space
• Time required for indexing is half
• Dynamic- Per query basis cost more n total
  trees not exact required specified
• Another factor value of nk

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DISCUSSIONS AND CONCLUSIONS

• Flexible retrieval dynamically, rank ordered list
  of elements, single indexing at level - basic
  indexing node (paragraph)
• Basic functions- SMART extended vector model
• Results flexible capabilities
• Attempt to incorporate other subvectors, internal
  node, weight
• INEX exhaustivity and specificity results
  exhaustive specificity research going on
  results are reflection
• It is the better way of retrieval than
  all-indexing

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THANK YOU!!!