Applying%20Evolutionary%20Computation%20Techniques%20to%20Web%20Information%20Retrieval

1
Applying Evolutionary Computation Techniques to
Web Information Retrieval

• Chih-Chin Lai, Ph.D.
• Dept. of Computer Science and Information
  Engineering
• National University of Tainan, Taiwan
• E-mail cclai_at_mail.nutn.edu.tw
• Nov. 28, 2007

2
Outlines

• Information Retrieval
• some related topics
• Evolutionary Computation (EC)
• Applying EC to Web Information Retrieval
• Conclusions

3
Introduction

• Definition of Information Retrieval
• Salton (1989) Information-retrieval systems
  process files of records and requests for
  information, and identify and retrieve from the
  files certain records in response to the
  information requests. The retrieval of particular
  records depends on the similarity between the
  records and the queries, which in turn is
  measured by comparing the values of certain
  attributes to records and information requests.
• Kowalski (1997) An Information Retrieval System
  is a system that is capable of storage,
  retrieval, and maintenance of information.
  Information in this context can be composed of
  text (including numeric and date data), images,
  audio, video, and other multi-media objects).

4
Introduction (cont.)

• Information Retrieval (IR)
• The indexing and retrieval of textual documents
• Searching newspaper articles
• Searching on the Web
• Concerned firstly with retrieving relevant
  documents to a query
• Concerned secondly with retrieving large sets of
  documents efficiently

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Typical IR Task

• Given
• User has information need
• A corpus of textual natural-language documents
• A user query in the form of a textual string
• Find
• A ranked set of documents that are relevant to
  the query

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Key Qualities

• Document and query representations
• Mechanisms for finding relevant documents and
  ranking the results
• Mechanisms for obtaining user feedback

7
Typical IR System
Documents
Documents
Query
User
Process
Process
Retrieved relevant(?) documents
Store
Retrieval Part
IR System
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Relevance

• Relevance is a subject judgment
• Being on the proper subject
• Being timely (recent information)
• Satisfying the goals of the user and his/her
  intended use of the information (information need)

9
IR System Components

• Text operations forms index words (tokens)
• Stopword removal
• Stemming
• Indexing maps each keyword to a set of documents
  that contains the keyword
• Searching retrieves documents that contain a
  given query token from the inverted index
• Ranking scores all retrieved documents according
  to a relevance metric

10
IR System Components (cont.)

• User interface manages interaction with the user
• Query input and document output
• Relevance feedback
• Visualization of results
• Query operations transform the query to improve
  retrieval

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Examples of IR System

• Conventional (library catalog) Search by
  keyword, title, author, etc.

12
Examples of IR System (cont.)

• Text-based (Google) Search by keywords. Limited
  search using queries in natural language

13
Examples of IR System (cont.)

• Multimedia (WebSeek) Search by visual appearance
  (shapes, colors,)

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Examples of IR System (cont.)

• Question answering systems (AnswerBus) Search in
  (restricted) natural language

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Searching the Web

• Application of IR to HTML documents on the World
  Wide Web
• Three forms
• Use search engines that index a portion of the
  Web documents as a full-text database
• Use Web directories, which classify selected Web
  documents by subject
• Search the Web exploiting its hyperlink structure

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Web Search System
Documents
Query
Spider
User
Process
Process
Retrieved relevant(?) documents
Store
World Wide Web
Retrieval Part
IR System
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Retrieval Models

• A retrieval model specifies the details of
• Document and Query representation
• Matching strategies for assessing the relevance
  of documents to a user query
• Methods for ranking query output
• Mechanisms for acquiring user-relevance feedback
• Notion of relevance can be binary or continuous
  (i.e. ranked retrieval)

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Types of IR Models

• Boolean model
• Simple Boolean queries regarding existence of
  terms within documents
• Easy to understand, but difficult to rank output
• Vector space model
• Documents are represented by n-dimensional
  vectors
• Typically one dimension per term

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Types of IR Models (cont.)

• Probabilistic model
• Start with some user-supplied relevance
  information about a training set of documents
• The training set is used to compute term weights
  by estimating
• Useful for theoretical analysis, but probably not
  in practice (?)

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Statistical Retrieval

• Retrieval based on similarity between query and
  documents
• Output documents are ranked according to
  similarity to query
• Similarity based on occurrence frequencies of
  keywords in query and document

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The Vector Space Model

• A document is typically represented by a bag of
  words (unordered words with frequencies)
• Assume a vocabulary of t distinct terms
• Each term, i, in a document or query, j, is given
  a real-valued weight, wij
• Both documents and queries are expressed as
  t-dimensional vectors
• dj (w1j, w2j, , wtj)

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Concept Representation

• Example
• Vdoc1 2T1 4T2 5T3
• Vdoc2 4T1 7T2 T3
• Vquery 0T1 0T2 2T3

T3
5
Vdoc1 2T1 4T2 5T3
Vquery 0T1 0T2 2T3
4
2
T1
Vdoc2 4T1 7T2 T3
7

• Is Vdoc1or Vdoc2 more similar to Vquery?
• How to measure the degree of similarity?

T2
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Term Weights TF-IDF

• More frequent terms in a document are more
  indicative to the topic
• fij frequency of term i in document j
• tfij fij / maxfij (normalization)
• Terms that appear in many different documents are
  less indicative of overall topic
• df i document frequency of term i
• number of documents containing term
  i
• idfi inverse document frequency of term i,
  
• log(N/ df i) ( where N total
  number of documents)

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TF-IDF Weighting

• A typical combined term importance indicator is
  tf-idf weighting
• wij tfij idfi tfij log (N/ dfi)
• A term occurring frequently in the document but
  rarely in the rest of the collection is given
  high weight
• Experimentally, tf-idf has been found to work well

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Similarity Measure

• A similarity measure is a function that computes
  the degree of similarity between two vectors
• Using a similarity measure between the query and
  each document
• to rank the retrieved documents
• to control the size of the retrieved set

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Similarity Measure (cont.)
t3

• Cosine similarity measures the cosine of the
  angle between two vectors inner product
  normalized by the vector lengths

?1
Vdoc1
Vquery
?2
t1
CosSim(dj, q)
Vdoc2
t2
Vdoc1 2T1 4T2 5T3 CosSim(Vdoc1 ,
Vquery) 10 / ?(41625)(004) 0.75 Vdoc2
4T1 7T2 1T3 CosSim(Vdoc2 , Vquery) 2 /
?(16491)(004) 0.12 Vquery 0T1 0T2 2T3
D1 is 6 times better than D2 using cosine
similarity but only 5 times better using inner
product.
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Accuracy Measures Precision and Recall
Relevant documents
From all the documents that are retrieved by the
IR system, how many are relevant? From all
the documents that are relevant out there, how
many did the IR system retrieve?
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Precision and Recall

• Precision
• The ability to retrieve top-ranked documents that
  are mostly relevant
• Recall
• The ability of the search to find all of the
  relevant items in the corpus

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Precision and Recall Variations
Narrow query formulation Returns relevant
documents but misses many useful ones
1
Broad query formulation Returns most
relevant documents but includes lots of junk
Precision
1
0
Recall
Figure taken from Raymond J. Mooney
(http//www.cs.utexas.edu/users/mooney/ir-course/)
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Evolutionary Computation

• Definition
• EC (GA, GP, ES) solve computational problems by
  simulating evolution with natural selection
• They are stochastic search algorithms which
  incrementally preserve and combine desirable
  features of individual potential solutions in a
  population over an extended period of time

Figure taken from www.genetic-programming.org
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Template of EC

• procedure EC
• begin
• t 0
• initializePopulation(P(0))
• evaluate(P(0))
• repeat
• t t 1
• P' selectForVariation((P(t))
• recombine(P')
• mutate(P')
• evaluate(P')
• until termination true
• end

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Applications of EC to IR

• EC has been applied to the following problems
• Automatic document indexing
• Document and term clustering
• Query definition
• Matching function learning
• Image retrieval
• Design of user profiles for IR on the Internet
• Web page classification
• Design of agents for Internet searching

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MGA for Web Search

• Genetic algorithm
• John Holland, 1975
• David E. Goldberg, 1989
• Metagenetic algorithm (MGA)
• Zacharis and Panayiotopoulos proposed (2001)
• A two-stage GA that controls and optimizes both
  populations simultaneously

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MGA for Web Search (cont.)

• Zacharis and Panayiotopoulos, IEEE Internet
  Computing, 2001

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Hierarchical Genetic Algorithm

• HGA
• Tang et al. (1998) proposed
• It is a variant of conventional genetic
  algorithm with hierarchical genetic structure
• In HGA, the chromosome consists of two types of
  genes
• the control genes and
• the parametric genes
• The relationship between parametric genes and
  control genes is that the activation of former is
  governed by the value of the latter

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HGA Representation
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HGA for Web Search
Chromosome
Randomly generated
W1 gt W2 select Keyword1
Dictionary
Control genes Control genes Control genes Control genes Control genes Parametric genes
1 1 0 0 1 news intelligence mit lab artificial
0 1 1 0 1 mit artificial ai lab intelligence
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HGA for Web Search (cont.)
Control genes Control genes Control genes Control genes Control genes
1 1 0 0 1
0 1 1 0 1
Cut point
Control genes Control genes Control genes Control genes Control genes Parametric genes
1 1 1 0 1 news intelligence mit lab artificial
0 1 0 0 1 mit artificial ai lab intelligence
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HGA for Web Search (cont.)
Control genes Control genes Control genes Control genes Control genes Parametric genes
1 1 1 0 1 news intelligence mit lab artificial
Dictionary
0 1 0 0 1 mit artificial ai lab intelligence
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HGA for Web Search (cont.)
User interface
Interesting
Relevance page
Update
PWIS
Vector DD
Recommendation component
Vector DR
Query
World Wide Web
Results by PageRank
Keywords
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HGA for Web Search (cont.)
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HGA for Web Search (cont.)
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Profile for Web Search
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Profile for Web Search
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Profile for Web Search
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Profile for Web Search (cont.)
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Profile for Web Search (cont.)
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Profile for Web Search (cont.)
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Conclusions

• The aim of a Web IR system is to estimate the
  relevance of web information items to a user
  information need expressed in a query
• This is a very hard and complex task
• It is pervaded with subjectivity, vagueness and
  impression
• The main characteristic of EC is that it is
  tolerant to impression, vagueness, partial truth,
  and approximation
• EC techniques have been used satisfactorily to
  improve IR process

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Conclusions (cont.)
Figure taken from M. Henzinger, The past,
presence, and future of Web Information Retrieval
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Web Intelligence

• Today's search engines are designed for human
  consumption
• (1) A user queries the SE and gets relevant pages
• (2) The user reads the pages and extracts
  manually the information
• (3) The information must be integrated to produce
  the desired knowledge

(1)
(2)
(1)
(3)
(3)
Figure taken from Prof. F. Ciravegna, University
of Sheffield, Web Intelligence
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Web Intelligence (cont.)

• The future web will have semantics associated to
  pages and SE will be able to provide
  semantically-based services

Figure taken from Prof. F. Ciravegna, University
of Sheffield, Web Intelligence
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References Journals

• Information Processing and Management
• Journal of the American Society of Information
  Science
• Transactions On Information Science
• Information Retrieval
• Journal of Documentation
• Information Retrieval

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Good books

• Van Rijsbergen
• Information Retrieval, ir.dcs.gla.ac.uk
• Sparck Jones and Willett
• Readings in Information Retrieval
• Baeza-Yates and Ribeiro-Neto
• Modern Information Retrieval
• Witten, Moffat and Bell
• Managing Gigabytes