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									           Web search engines
Rooted in Information Retrieval (IR) systems
  •Prepare a keyword index for corpus
  •Respond to keyword queries with a ranked list of
  documents.
ARCHIE
  •Earliest application of rudimentary IR systems to
  the Internet
  •Title search across sites serving files over FTP
            Boolean queries: Examples
 Simple queries involving relationships
  between terms and documents
   • Documents containing the word Java
   • Documents containing the word Java but not
          the word coffee
 Proximity queries
  • Documents containing the phrase Java beans
          or the term API
      •   Documents where Java and island occur in
          the same sentence

Mining the Web         Chakrabarti and Ramakrishnan   2
                 Document preprocessing
 Tokenization
  • Filtering away tags
  • Tokens regarded as nonempty sequence of
          characters excluding spaces and
          punctuations.
      •   Token represented by a suitable integer, tid,
          typically 32 bits
      •   Optional: stemming/conflation of words
      •   Result: document (did) transformed into a
          sequence of integers (tid, pos)

Mining the Web          Chakrabarti and Ramakrishnan      3
                    Storing tokens
 Straight-forward implementation using a
  relational database
   • Example figure
   • Space scales to almost 10 times
 Accesses to table show common pattern
   • reduce the storage by mapping tids to a
          lexicographically sorted buffer of (did, pos)
          tuples.
      •   Indexing = transposing document-term matrix


Mining the Web          Chakrabarti and Ramakrishnan      4
Two variants of the inverted index data structure, usually stored on disk. The simpler
version in the middle does not store term offset information; the version to the right stores
term
offsets. The mapping from terms to documents and positions (written as
“document/position”) may
be implemented using a B-tree or a hash-table.
Mining the Web                   Chakrabarti and Ramakrishnan                              5
                        Storage
 For dynamic corpora
  • Berkeley DB2 storage manager
  • Can frequently add, modify and delete
         documents
 For static collections
  • Index compression techniques (to be
         discussed)




Mining the Web        Chakrabarti and Ramakrishnan   6
                               Stopwords
 Function words and connectives
 Appear in large number of documents and little
  use in pinpointing documents
 Indexing stopwords
   • Stopwords not indexed
                For reducing index space and improving performance
      • Replace stopwords with a placeholder (to remember
         the offset)
 Issues
   • Queries containing only stopwords ruled out
   • Polysemous words that are stopwords in one sense
         but not in others
                E.g.; can as a verb vs. can as a noun
Mining the Web                   Chakrabarti and Ramakrishnan         7
                           Stemming
 Conflating words to help match a query term with a
  morphological variant in the corpus.
 Remove inflections that convey parts of speech, tense
  and number
 E.g.: university and universal both stem to universe.
 Techniques
   • morphological analysis (e.g., Porter's algorithm)
   • dictionary lookup (e.g., WordNet).
 Stemming may increase recall but at the price of
  precision
   • Abbreviations, polysemy and names coined in the technical and
        commercial sectors
      • E.g.: Stemming “ides” to “IDE”, “SOCKS” to “sock”, “gated” to
        “gate”, may be bad !
Mining the Web              Chakrabarti and Ramakrishnan                8
      Batch indexing and updates
 Incremental indexing
   • Time-consuming due to random disk IO
   • High level of disk block fragmentation
 Simple sort-merges.
   • To replace the indexed update of variable-
     length postings
 For a dynamic collection
  • single document-level change may need to
           update hundreds to thousands of records.
      • Solution : create an additional “stop-press”
           index.
Mining the Web          Chakrabarti and Ramakrishnan   9
                 Maintaining indices over dynamic collections.

Mining the Web                  Chakrabarti and Ramakrishnan     10
                          Stop-press index
 Collection of document in flux
      • Model document modification as deletion followed by insertion
      • Documents in flux represented by a signed record (d,t,s)
      • “s” specifies if “d” has been deleted or inserted.
 Getting the final answer to a query
      • Main index returns a document set D0.
      • Stop-press index returns two document sets
                D+ : documents not yet indexed in D0 matching the query
                D- : documents matching the query removed from the collection
                 since D0 was constructed.
 Stop-press index getting too large
      • Rebuild the main index
           signed (d, t, s) records are sorted in (t, d, s) order and merge-
            
           purged into the master (t, d) records
      • Stop-press index can be emptied out.
Mining the Web                    Chakrabarti and Ramakrishnan                   11
       Index compression techniques
 Compressing the index so that much of it
  can be held in memory
   • Required for high-performance IR installations
         (as with Web search engines),
 Redundancy in index storage
  • Storage of document IDs.
 Delta encoding
  • Sort Doc IDs in increasing order
  • Store the first ID in full
  • Subsequently store only difference (gap) from
         previous ID
Mining the Web         Chakrabarti and Ramakrishnan   12
                   Encoding gaps
 Small gap must cost far fewer bits than a
  document ID.
 Binary encoding
   • Optimal when all symbols are equally likely
 Unary code
   • optimal if probability of large gaps decays
         exponentially




Mining the Web           Chakrabarti and Ramakrishnan   13
                         Encoding gaps
 Gamma code
  • Represent gap x as
             Unary       code for 1  logx  followed by
                                                 logx
                 x - 2logx represented in binary (      bits)
 Golomb codes
  • Further enhancement




Mining the Web                Chakrabarti and Ramakrishnan         14
    Lossy compression mechanisms
 Trading off space for time
 collect documents into buckets
   • Construct inverted index from terms to bucket
         IDs
  •      Document' IDs shrink to half their size.
 Cost: time overheads
  • For each query, all documents in that bucket
         need to be scanned
 Solution: index documents in each bucket
  separately
   • E.g.: Glimpse (http://webglimpse.org/)
Mining the Web         Chakrabarti and Ramakrishnan   15
                 General dilemmas
 Messy updates vs. High compression rate
 Storage allocation vs. Random I/Os
 Random I/O vs. large scale
  implementation




Mining the Web      Chakrabarti and Ramakrishnan   16
                        Relevance ranking
 Keyword queries
  • In natural language
  • Not precise, unlike SQL
                Boolean decision for response unacceptable
      • Solution
                Rate each document for how likely it is to satisfy the user's
                 information need
                Sort in decreasing order of the score
                Present results in a ranked list.
 No algorithmic way of ensuring that the ranking
  strategy always favors the information need
   • Query: only a part of the user's information need

Mining the Web                   Chakrabarti and Ramakrishnan                    17
                   Responding to queries
 Set-valued response
  • Response set may be very large
             (E.g.,  by recent estimates, over 12 million Web
                 pages contain the word java.)
 Demanding selective query from user
 Guessing user's information need and
  ranking responses
 Evaluating rankings


Mining the Web                Chakrabarti and Ramakrishnan       18
                 Evaluating procedure
 Given benchmark
  • Corpus of n documents D
  • A set of queries Q
  • For each query,q  Q an exhaustive set of
          relevant documentsq  D
                          D                             identified
          manually
 Query submitted system 1, d2 ,, dn )
                           (d
  • Ranked list of documents (r1, r2 , .., rn )
          retrieved d  D
              ri  1  i   q
      •   compute a 0/1 relevance list
              ri  0
                  iff
Mining the Web           Chakrabarti and Ramakrishnan                19
                  otherwise.
                    Recall and precision
 Recall at rank
  • Fraction of all relevant documents included in
          . (d1, d 2 ,, d n )
                            1
  •                   kri
          . recall(k) 
              | Dq | 1i
 Precision at rank  1 k
  • Fraction of the top k responses that are
          actually relevant.
                         1
      •   precision( k)   ri
          .
                             k   1i  k




Mining the Web                    Chakrabarti and Ramakrishnan   20
                          Other measures
 Average precision
  • Sum of precision at each relevant hit position in the
     response list, divided by the total number of relevant
     documents
   • . avg.precis ion  1  rk * precision(k )
     .                 | Dq | 1k |D|
   • avg.precision =1 iff engine retrieves all relevant
     documents and ranks them ahead of any irrelevant
     document
 Interpolated precision
   • To combine precision values from multiple queries
   • Gives precision-vs.-recall curve for the benchmark.
                                                    
                 For each query, take the maximum precision obtained for the
                 query for any recall greater than or equal to
                average them together for all queries
Mining the Web                  Chakrabarti and Ramakrishnan               21
 Others like measures of authority, prestige etc
           Precision-Recall tradeoff
 Interpolated precision cannot increase with
  recall
   • Interpolated precision at recall level 0 may be less
     than 1
 At level k = 0
   • Precision (by convention) = 1, Recall = 0
 Inspecting more documents
   • Can increase recall
   • Precision may decrease
         we will start encountering more and more irrelevant
          documents
 Search engine with a good ranking function will
     generally show a negative relation between
     recall and precision. and Ramakrishnan
Mining the Web        Chakrabarti                               22
ecision and interpolated precision plotted against recall for the given relevance vec
                               Missing rkare zeroes.

      Mining the Web             Chakrabarti and Ramakrishnan                 23
          The vector space model
 Documents represented as vectors in a
  multi-dimensional Euclidean space
   • Each axis = a term (token)
 Coordinate of document d in direction of
  term t determined by:
   • Term frequency TF(d,t)
        number   of times term t occurs in document d,
         scaled in a variety of ways to normalize document
         length
   • Inverse document frequency IDF(t)
        to       scale down the coordinates of terms that occur
Mining the Web in many documents and Ramakrishnan
                            Chakrabarti                            24
                         Term frequency
 . TF(d, t)  n(d, t)  TF(d, t) 
                                      n(d, t)

  .            n(d, )
               
                                   max (n(d, ))
                                    


 Cornell SMART system uses a smoothed
  version
        TF (d , t )  0                      n( d , t )  0
        TF (d , t )  1  log(1  n(d , t )) otherwise




Mining the Web                   Chakrabarti and Ramakrishnan   25
          Inverse document frequency
 Given
  • D is the document collection and
                                  Dt                                   is the set
         of documents containing t
 Formulae
                                 D
  • mostly dampened functions ofD |
                               |                                   t

  • SMART
            .                     1 | D |
                 IDF (t )  log(            )
                                    | Dt |




Mining the Web                      Chakrabarti and Ramakrishnan                    26
                 Vector space model
 Coordinate of document d in axis t
  • . dt  TF (d , t ) IDF (t )
                              
  • Transformed tod in the TFIDF-space
 Query q
  • Interpreted as a document
                             
  • Transformed toq in the same TFIDF-space
         as d




Mining the Web       Chakrabarti and Ramakrishnan   27
                   Measures of proximity
 Distance measure
  • Magnitude of the vector difference
          
     .
        |d q |
  • Document vectors must be normalized to unit
                                            L1
            or
         ( L2           ) length
             Else    shorter documents dominate (since queries
                 are short)
 Cosine similarity
                                                            
  • cosine of the angle between
                              d                             and
                                                             q
             Shorter   documents are penalized

Mining the Web               Chakrabarti and Ramakrishnan         28
                      Relevance feedback
 Users learning how to modify queries
  • Response list must have least some relevant
          documents
      •   Relevance feedback
                `correcting' the ranks to the user's taste
                automates the query refinement process
 Rocchio's method
                    
  • Folding-in user feedback
                    q
  • To query vector
             Add a weighted sum of vectors for relevant documents D+
                            
                        
             q   weighted    
          q' Subtract a d -  d sum of the irrelevant documents D-
      • .                D      D-



Mining the Web                   Chakrabarti and Ramakrishnan           29
          Relevance feedback (contd.)
 Pseudo-relevance feedback
  • D+ and D- generated automatically
             E.g.:Cornell SMART system
             top 10 documents reported by the first round of
              query execution are included in D+
  •  typically set to 0; D- not used
 Not a commonly available feature
  • Web users want instant gratification
  • System complexity
             Executing    the second round query slower and
                 expensive for major search engines
Mining the Web               Chakrabarti and Ramakrishnan       30
                 Ranking by odds ratio
 R : Boolean random variable which
  represents the relevance of document d
  w.r.t. query q.
 Ranking documents by their odds ratio for
                             
       Pr(R | q, d ) Pr(R, q, d ) / Pr(q, d ) Pr(R | q) / Pr(d | R , q)
  relevancePr(R , q, d) / Pr(q, d )  Pr(R | q) / Pr(d | R, q)
                   
      Pr(R | q, d )
   •.
 Approximating probability of d by product
        
  of theR,probabilities of individual terms in d
     Pr(d | q)
                  
                      Pr(x | R, q)
                       t
                                                    
     Pr(d | R , q)    Pr(x | R , q)
                                                             a (1  b )
   •.
                  t    t
                                          Pr(R | q, d )
                                                         t ,q   t ,q

                                          Pr(R | q, d )
                                                  tq  d    b (1  a )
                                                            t ,q   t ,q

   • Approximately…
Mining the Web             Chakrabarti and Ramakrishnan                   31
                   Bayesian Inferencing




Bayesian inference network for relevance ranking. A              Manual specification of
document is relevant to the extent that setting its              mappings between terms
corresponding belief node to true lets us assign a high          to approximate concepts.
degree of belief in the node corresponding to the query.




Mining the Web                    Chakrabarti and Ramakrishnan                      32
         Bayesian Inferencing (contd.)
 Four layers
   1.Document layer
   2.Representation layer
   3.Query concept layer
   4.Query
 Each node is associated with a random
  Boolean variable, reflecting belief
 Directed arcs signify that the belief of a
  node is a function of the belief of its
  immediate parents (and so on..)
Mining the Web    Chakrabarti and Ramakrishnan   33
        Bayesian Inferencing systems
 2 & 3 same for basic vector-space IR
  systems
 Verity's Search97
   • Allows administrators and users to define
         hierarchies of concepts in files
 Estimation of relevance of a document d
  w.r.t. the query q
  • Set the belief of the corresponding node to 1
  • Set all other document beliefs to 0
  • Compute the belief of the query
  • Rank documents in decreasing order of belief
         that they induce in the query
Mining the Web          Chakrabarti and Ramakrishnan   34
                              Other issues
 Spamming
  • Adding popular query terms to a page unrelated to
          those terms
      •   E.g.: Adding “Hawaii vacation rental” to a page about
          “Internet gambling”
   •      Little setback due to hyperlink-based ranking
 Titles, headings, meta tags and anchor-text
   • TFIDF framework treats all terms the same
   • Meta search engines:
                Assign weight age to text occurring in tags, meta-tags
      • Using anchor-text on pages u which link to v
                Anchor-text on u offers valuable editorial judgment about v as
                 well.
Mining the Web                   Chakrabarti and Ramakrishnan                35
                 Other issues (contd..)
 Including phrases to rank complex queries
   • Operators to specify word inclusions and
          exclusions
      •   With operators and phrases
          queries/documents can no longer be treated
          as ordinary points in vector space
 Dictionary of phrases
  • Could be cataloged manually
  • Could be derived from the corpus itself using
          statistical techniques
      •   Two separate indices:
             one   for single terms and another for phrases
Mining the Web               Chakrabarti and Ramakrishnan      36
   Corpus derived phrase dictionary
                             2
                         t
 Two terms t1 and                                                  2
                                                                t
 Null hypothesis = occurrences of t1 and are
  independent
 To the extent the pair violates the null hypothesis, it is
  likely to be a phrase
      • Measuring violation with likelihood ratio of the
          hypothesis
      •   Pick phrases that violate the null hypothesis
          with large confidence
 Contingency table built from statistics
                 ) 2 t , 1t( k  1 1k ) 2 t , 1t( k  0 1k
                 ) 2 t , 1t( k  1 0k ) 2 t , 1t( k  0 0k

Mining the Web                   Chakrabarti and Ramakrishnan           37
      Corpus derived phrase dictionary
   Hypotheses
    • Null hypothesis
 H ( p00 , p01, p10 , p11; k00 , k01, k10 , k11)  p00 p01 p10 p11
                                                    k00 k01 k10 k11



        • Alternative hypothesis
H ( p1 , p2 ; k00 , k01, k10 , k11)  ((1  p1 )(1  p2 ))k00 ((1  p1 ) p2 )k01 ( p1 (1  p2 ))k10 ( p1 p2 )k11

        • Likelihood ratio
        max H ( p; k )
        p 0
 
        max H ( p; k )
         p




  Mining the Web                          Chakrabarti and Ramakrishnan                                       38
          Approximate string matching
       Non-uniformity of word spellings
  • dialects of English
  • transliteration from other languages
 Two ways to reduce this problem.
  1. Aggressive conflation mechanism to
             collapse variant spellings into the same
             token
      2.     Decompose terms into a sequence of q-
             grams or sequences of q characters


Mining the Web           Chakrabarti and Ramakrishnan   39
          Approximate string matching
1. Aggressive conflation mechanism to collapse
   variant spellings into the same token
            •    E.g.: Soundex : takes phonetics and pronunciation details
                 into account
            •    used with great success in indexing and searching last
                 names in census and telephone directory data.
2. Decompose terms into a sequence of q-grams
   or sequences of q characters
            •    Check for similarity in the q(2  q  4) grams
            •    Looking up the inverted index : a two-stage affair:
                 •   Smaller index of q-grams consulted to expand each query
                     term into a set of slightly distorted query terms
                 •   These terms are submitted to the regular index
            •    Used by Google for spelling correction
            •    Idea also adopted for eliminating near-duplicate pages

Mining the Web                  Chakrabarti and Ramakrishnan                   40
                     Meta-search systems
• Take the search engine to the document
   • Forward queries to many geographically distributed
         repositories
            •    Each has its own search service
  • Consolidate their responses.
• Advantages
  • Perform non-trivial query rewriting
            •    Suit a single user query to many search engines with
                 different query syntax
  • Surprisingly small overlap between crawls
• Consolidating responses
  • Function goes beyond just eliminating duplicates
  • Search services do not provide standard ranks which
         can be combined meaningfully
Mining the Web                   Chakrabarti and Ramakrishnan           41
                  Similarity search
• Cluster hypothesis
  • Documents similar to relevant documents are
         also likely to be relevant
• Handling “find similar” queries
  • Replication or duplication of pages
  • Mirroring of sites




Mining the Web          Chakrabarti and Ramakrishnan   42
                         Document similarity
• Jaccard coefficient of similarity between
  documentd1 and 2                       d
• T(d) = set of tokens in document d
                     | T (d )  T (d ) |
   •. r ' (d , d ) 
                 1
                           1    2
                     | T (d )  T (d ) |
                     2
                           1    2

   • Symmetric, reflexive, not a metric
   • Forgives any number of occurrences and any
         permutations of the terms.
• 1  r ' (d1, d2 )            is a metric


Mining the Web                  Chakrabarti and Ramakrishnan   43
 Estimating Jaccard coefficient with
       random permutations
1. Generate a set of m random                   
   permutations     
2. for each dod1 )    (       (d2 )
3.                          and
         computemin T (d1 )  min T (d2 )
4.       check if
5. end for
6. if ' equality was observed in k cases,
    r (d1 , d 2 ) 
                    k
   estimate.        m



Mining the Web   Chakrabarti and Ramakrishnan       44
  Fast similarity search with random
                   permutations
                            
1. for each random permutation do
2.         create a file 
                        f
3.        for each document d do
4.            write out  s  min (T (d )), d      f
                                                    to
5.        end for
6.        sort f  using key s--this results in contiguous blocks with fixed
                                 ds
       s containing all associated
7.        create a fileg 
8.                      (
          for each paird1 , d2 )  within a run off    having a given s do
9.       write out a document-pair record 1 , d2 )
                                        (d            to g
10.   end for
11.   sort g  on keyd1 , d2 )
                      (
12. end for
13. merge g  for all in(d1 , d2 )                         (d , d
                                    order, counting the number 1of 2 )
    entries
Mining the Web               Chakrabarti and Ramakrishnan                  45
     Eliminating near-duplicates via shingling
• “Find-similar” algorithm reports all duplicate/near-
  duplicate pages
• Eliminating duplicates
   • Maintain a checksum with every page in the corpus
• Eliminating near-duplicates
   • Represent each document as a set T(d) of q-grams (shingles)
   • Find Jaccard similarityr (d1, d2 )    between d 2
                                                d1     and
   • Eliminate the pair from step 9 if it has similarity above a
         threshold




Mining the Web          Chakrabarti and Ramakrishnan               46
    Detecting locally similar sub-graphs of the
                        Web
•         Similarity search and duplicate elimination on the
          graph structure of the web
      •      To improve quality of hyperlink-assisted ranking
•         Detecting mirrored sites
      •      Approach 1 [Bottom-up Approach]
            1.   Start process with textual duplicate detection
                     •   cleaned URLs are listed and sorted to find duplicates/near-
                         duplicates
                     •   each set of equivalent URLs is assigned a unique token ID
                     •   each page is stripped of all text, and represented as a sequence
                         of outlink IDs
            2.     Continue using link sequence representation
            3.   Until no further collapse of multiple URLs are possible
      •      Approach 2 [Bottom-up Approach]
            1.   identify single nodes which are near duplicates (using text-
                 shingling)
            2.   extend single-node mirrors to two-node mirrors
            3.   continue on to larger and larger graphs which are likely mirrors of
Mining the Web   one another      Chakrabarti and Ramakrishnan                     47
                 Detecting mirrored sites (contd.)
      • Approach 3 [Step before fetching all pages]
            • Uses regularity in URL strings to identify host-pairs which are
              mirrors
            • Preprocessing
                   • Host are represented as sets of positional bigrams
                       • Convert host and path to all lowercase characters
                       • Let any punctuation or digit sequence be a token separator
                       • Tokenize the URL into a sequence of tokens, (e.g.,
                          www6.infoseek.com gives www, infoseek, com)
                       • Eliminate stop terms such as htm, html, txt, main, index, home,
                          bin, cgi
                       • Form positional bigrams from the token sequence
            •    Two hosts are said to be mirrors if
                   • A large fraction of paths are valid on both web sites
                   • These common paths link to pages that are near-duplicates.




Mining the Web                      Chakrabarti and Ramakrishnan                           48

								
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