Gkay Burak AKKUS Ece AKSU

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XRANK

• XRANK Ranked Keyword Search over
• XML Documents
• Ece AKSU
• Gökay Burak AKKUS

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This Paper...

• Describes the architecture, implementation and
  evaluation of the XRANK system
• The contributions of the paper are
• (a) the problem definition and system
  architecture
• (b) an algorithm for computing the ranking of
  XML elements
• (c) new inverted list index structures and
  associated query processing algorithms
• (d) an experimental evaluation of XRANK

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Overview

• Problem Efficiently producing ranked results for
  keyword search queries over hierarchical XML
  documents.
• New challanges
• Returns deeply nested XML elements.
• Ranking is at the granularity of an XML element
  (not the document)
• Keyword proximity is more complex.

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Overview - 2

• This paper pesents XRANK system to handle these
  features of XML keyword search.
• XRANK offers both space performance benefits
• XRANK generalizes a hyperlink based HTML search
  engine such as Google.
• XRANK can be used to query both HTML and XML
  documents.

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Keyword Search Querying - 1

• Keyword search querying
• Adv simple
• users do not have to learn a complex query
  language
• can issue queries without any prior knowledge
  about the structure of the underlying data.
• Consequence Interface is fexible
• Queries may not always be precise and can return
  large number of query results.

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Keyword Search Querying - 2

• An important requirement for keyword search is to
  rank the query results so that the most relevant
  results appear first.
• Certain limitations of the HTML data model make
  such systems ineffective in many domains.
• HTML is a presentation language
• HTML cannot capture much semantics

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Keyword Search Querying - 3

• The XML data model addresses this limitation by
  allowing for extensible element tags. (Example
  Figure.1)

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Querying XML Documents

• One approach is the sophisticated query language
  XQUERY
• Effective in some cases
• Users have to learn a complex query language and
  understand the schema of underlying XML
• An alternative approach is XRANK
• Retain the simple keyword search query interface
• Exploit XMLs tagged and nested structure during
  query processing.

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New Challanges

• Keyword searching over XML introduces many new
  challenges.
• 1. The result of the keyword search query can be
  a deeply nested XML element.
• return the deepest node
• 2. Ranking is not solely based on hyperlinks.
• semantics of containment links (relating parent
  and child elements) is very different from that
  of hyperlinks (such as IDREFs and XLinks)

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New Challanges

• 3. The notion of proximity among keywords is
  more complex
• In HTML, proximity among keywords translates
  directly to the distance between keywords in a
  document.
• For XML there is a 2-dimensional proximity
  metric.
• Keyword distance
• Ancestor distance

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XML Data Model

• XML is a hierarchical format for data
  representation and exchange.
• An XML document consists of
• Root element, nested sub-elements, attributes and
  values,
• supports intra-document and inter-document
  references.

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XML Data Model-2

• Intra-document referencees are represented using
  IDREFs.
• Inter-document references are represented using
  XLink.
• Both IDREFs and XLinks are reffered as
  hyperlinks!

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Definitions

• A collection of hyperlinked XML documents can be
  defined as a directed graph
• G (N, CE, HE)
• N The set of nodes N NE U NV
• NE The set of elements
• NV The set of values
• CE The set of containment edges relating nodes
• HE The set of hyperlink edges relating nodes

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Definitions - 2

• The edge (u, v) ?CE iff v is a value/nested
  sub-element of u.
• The edge (u, v) ? HE iff u contains a hyperlink
  reference to v.
• An element u is a sub-element of an element v if
  (v,u) ? CE.
• An element u is the parent of node v if (u,v) ?
  CE.
• The predicate contains(v, k) is true if the node
  v directly or indirectly contains the keyword k.

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Keyword Query Results

• There are two possible semantics for keyword
  search queries
• conjunctive keyword query semantics
• contain all of the query keywords are returned.
• disjunctive keyword query semantics
• contain at least one of the query keywords are
  returned
• This paper focuses on conjunctive keyword query
  semantics.

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Keyword Query Results - 2

• Qk1,, kn.
• R0 v ?v ? NE ? ? k ? Q(contains(v,k))
• the set of elements that directly or indirectly
  contain all of the query keywords.
• Result(Q)v ? ? k ? Q ?c ? N ((v,c) ? CE ? c ?R0
  ? contains(c,k))
• ensures that only the most specific results are
  returned.
• ensures that an element that has multiple
  independent occurrences of the query keywords is
  returned,
• CE are considered for result set, HE are
  considered for ranking

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Keyword Query Results - 3

• XML elements provides more context information
• Also poses interesting user-interface challenges.
• One solution is to allow the user to navigate up
  to the ancestors of the query result
• Another solution, is to predefine a set of
  answer nodes AN.
• XRANK supports both
• may require knowledge of the domain and
  underlying XML schema

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Ranking Keyword Query Results

• Desired Properties of Ranking Function
• 1) Result specificity more specific results
  higher than less specific results. one dimension
  of result proximity.
• 2) Keyword proximity another dimension of
  result proximity.
• 3) Hyperlink Awareness hyperlinked structure of
  XML documents.

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Ranking Function Definition

• ElemRank is defined at the granularity of an
  element and takes the nested structure of XML
  into account.
• Similar to Googles PageRank
• Q (k1, k2, , kn)
• R Result(Q)
• A result element v1 ? R
• First define the ranking of v1 with respect to
  one query keyword ki, r(v1,ki) before defining
  the overall rank, rank(v1, Q).

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Ranking with respect to one keyword

• There exists a sub-element/value node
• v2 of v1 such that
• v2 ?R0 and contains(v2, ki).
• There is a sequence of containment edges
• in CE of the form (v1, v2), (v2, v3), , (vt,
  vt1) such that vt1 is a value node that
  directly contains the keyword ki.

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Ranking with respect to one keyword

• r(v1, ki) does not depend on the ElemRank of the
  result node v1, except when v1 vt for 2
  reasons
• 1. less specific results indeed get lower ranks.
• 2. in fact related to ElemRank(v1) due to certain
  properties of containment edges.
• For multiple occurences of ki in v1 combined rank
  is
• f max

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Overall Ranking

• The overall ranking is the sum of the ranks with
  respect to each query keyword, multiplied by a
  measure of keyword proximity p(v1, k1, k2, ,
  kn).

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XRANK System Architecture
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XRANK System Architecture-2

• ElemRank Computation Module
• Computes the ElemRanks of XML elements
• Combined with ancestor info
• HDIL
• Generates an index structure called HDIL
• The Query Evaluator Module
• Evaluates queries using HDIL
• Returns ranked results.

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ElemRank Computational Module

• ElemRank is a measure of the objective importance
  of an XML element and is based on the hyperlinked
  structure of XML docs.
• PageRank function is sum of 2 probabilities
• Visiting v at random (d0.85)
• Visiting v by navigating

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ElemRank Computational Module

• PageRank is unidirectional
• Forward ElemRank propagation
• Paper ? section
• Reverse ElemRank propagation
• Paper -- gt workshop

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Refinements of PageRank

• Bi-directional transfer of ElemRanks
• Discrimination between containment and hyperlink
  edges
• Aggregate ElemRanks for reverse containment
  relationships

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Bi-directional Transfer of ElemRanks

• A simple solution is to add reverse containment
  edges,
• does not distinguish between containment and
  hyperlink edges

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Discrimination between containment and hyperlink
edges

• It weights forward and reverse containment
  relationships similarly.

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Aggregate ElemRanks for reverse containment
relationships
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XRANK System

• Efficiently Evaluating XML Keyword Search Queries

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Efficiently Evaluating XML Keyword Search Queries

• Naïve Approach
• Dewey Inverted List (DIL)
• Ranked Dewey Inverted List (RDIL)
• Hybrid Dewey Inverted List (HDIL)

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Naïve Approach

• Main Difference between XML and HTML keyword
  search
• The granularity of query results
• XML keyword search returns elements
• HTML keyword search returns documents
• One way to do XML keyword search
• Treat each element as a document

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Problems of Naïve Approach

• Space Overhead
• Spurious Query Results
• Inaccurate ranking of results

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Space Overhead

• An inverted list contains for each keyword, the
  list of documents that contain the keyword
• For XML documents, the list of elements
• A large space overhead because each inverted
  list contains
• XML element that directly contains the keyword(1)
• All of (1)s ancestors redundantly

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Spurious Query Results

• The naïve approach ignores ancestor-descendant
  relationships.
• All elements treated as independent documents
• Results will not correspond to the desired
  semantics for XML keyword search

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Inaccurate Ranking of Results

• Existing approaches do not take result
  specificity into account when ranking results.

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Dewey Inverted List (DIL)

• Naïve approach has drawbacks
• Decouples representation of ancestors and
  descendants.
• Dewey encoding of Element IDs jointly captures
  ancestor and descendant information.

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DIL

• An interesting feature
• ID of an ancestor is a prefix of the ID of a
  descendant.
• Ancestor-descendant relationships are implicitly
  captured in the Dewey ID.

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DIL Data Structure

• The inverted list for a keyword k contains the
  Dewey IDs of all the XML elements that directly
  contain the keyword k.
• For multiple documents
• First component of each Dewey ID is the document
  ID

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DIL Data Structure -2

• An entry in DIL
• ElemRank of corresponding XML element
• The list of all positions where the keyword k
  appears in that element.
• Entries are sorted by Dewey IDs
• The size of DIL is smaller than that of Naïve
  Approach.

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DIL Query Processing

• An algorithm that works in a single pass over the
  query keyword inverted lists.
• The key idea
• Merge the query keyword inverted lists
• Simultaneously compute the longest common prefix
  of the Dewey IDs in different lists.

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Ranked Dewey Inverted List (RDIL)

• If inverted lists are long (due to common
  keywords or large document collections) even the
  cost of a single scan of the inverted list can be
  expensive, especially if the users want only the
  top few results.

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RDIL -2

• One solution
• Order the inverted lists by the ElemRank instead
  of by the Dewey ID.
• Higher ranked results will appear first in the
  inverted list.
• Threshold Algorithm.

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RDIL Data Structure

• RDIL is similar to DIL except that
• Inverted lists are ordered by ElemRank,
• Each inverted list has a B-tree index of the
  Dewey ID field.

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RDIL Query Processing

• Consider an entry retrieved from the inverted
  list of keyword k i .
• The entry contains the Dewey ID d of a top-ranked
  element that directly contains the query keyword
  k i .
• To determine a query result the longest prefix of
  d that also contains the other query keywords
  needs to be determined.

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Hybrid Dewey Inverted List (HDIL)

• In many cases RDIL is likely to perform well.
• It may perform worse than DIL when there is a
  query where keywords are not correlated.

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HDIL -2

• The individual query keywords occur relatively
  frequently in the document collection but rarely
  occur together in the same document.
• Since the number of results is small
• RDIL has to scan most (or all) of the inverted
  lists to produce the output.
• Can we combine the benefits of DIL and RDIL
  without replicating the entire inverted list
  index?

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HDIL Query Processing

• An adaptive strategy
• Periodically monitor performance.
• Calculate
• Time spent t
• The number of results above the threshold r
• Estimated time remaining for RDIL (m-r)t/r
• m desired number of query results
• If estimated time is more than the expected time
  for DIL, then switch to DIL.

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Experimental Evaluation

• Experimental Setup
• Quality and Ranking Function
• Space requirements
• Query Performance
• (1) the number of query keywords
• (2) the correlation between the keywords
• (3) the desired number of query results
• (4) the selectivity of the keywords.