CS511 Design of Database Management Systems

1
CS511Design of Database Management Systems

• Lecture 13
• Information Retrieval Overview
• Kevin C. Chang

2
Announcements

• MT format
• Wednesday 200-315pm
• open notes, papers, books. Calc. OK (wont need).
  PDA no.
• 75 points (for 75 minutes)
• 4 problems
• Prob. 1 True/False problems
• Prob. 2-4 longer problems
• Preparation
• study lecture notes, HW, SGP use them to review
  papers
• ask why ask that...
• discussion with peers
• think more (beyond stated) and try to relate
  issues

3
Some History

• Early Days--
• 1945 V. Bushs article As we may think
• 1957 H. P. Luhns idea of word counting and
  matching
• Indexing Evaluation Methodology (1960s)
• Smart system (G. Saltons group)
• Cranfield test collection (C. Cleverdons group)
• Indexing automatic can be as good as manual
• IR Models (1970s 1980s)
• Large-scale Evaluation Applications (1990s)
• TREC (D. Harman E. Voorhees, NIST)
• Large scale Web search

4
?? Text Search vs. Database Queries

• Two related areas
• information retrieval (IR)
• databases
• traditionally separate-- brought together by the
  Web
• ?? Any differences in
• data models?
• query semantics?
• desirable functionalities?

5
Text vs. Rel. DB Art vs. Algebra

• Data models
• unstructured text vs. well-structured data
• Query semantics
• fuzzy vs. well-defined
• text search to satisfy information need lt--
  art
• DB queries to perform data computation lt--
  algebra
• relevant vs. correct answers
• ranked vs. Boolean answers
• Functionalities
• read-mostly vs. read-write/transactions/cc ...

6
Recall Measuring False-Negatives

• Recall x / relevant
• e.g. relevant D1, D2, retrieved D1, D3,
  D4
• recall R 1 / 2 0.5
• there is 1 false negative D2
• ? How to fool recall?

x
relevant
retrieved
collection
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Precision Measuring False-Positives

• Precision x / retrieved
• e.g. relevant D1, D2, retrieved D1, D3,
  D4
• precision P 1 / 3 0.33
• there are 2 false positives D3 and D4

x
relevant
retrieved
collection
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Models

• Boolean criteria-based
• Vector space similarity-based
• Probabilistic probability-based

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Boolean Model

• Query
• Q1 data AND web
• Q2 (knowledge OR information) AND base
• Q3 data NOT info
• Documents
• D1 web data and web queries
• D2 digital data index
• D3 data base for dummies

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Boolean Model

• View Satisfaction by criteria
• Query a Boolean expression
• Q1 data AND web
• Document a Boolean conjunction
• D1 web data and web queries
• web AND data AND queries
• Query results
• D D implies Q, i.e., all docs that satisfy Q

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Boolean Queries Problems

• Query matching is exact and not flexible
• exact matching can result in too few/many matches
• Hard to formulate a right query
• what is query for documents about color
  printer?
• Results are not ranked/ordered for exploration
• Boolean is binary yes or no
• In short relevance not captured
• traditional DB queries are similarly bad at
  fuzzy concepts
• new research work in top-k queries

12
Vector Space Model

• View Similarity of content
• Intuitions
• docs consist of words --gt put docs in the word
  space
• space n-dimension for n words
• similarity becomes geometric comparison
• document-query similarity vector-vector
  similarity

D
Q
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Probabilistic Models

• View Probability of relevance
• the probabilistic ranking principle
• Estimate and rank by P(R Q, D)
• or by log-odds

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Probabilistic Models

• To rank by
• I.e., (see next page)
• Assume pi the same for all query terms
• Assume qi ni/N
• N is the collection size i.e., all docs are
  irrelevant
• Similar to using IDF
• intuition e.g., apple computer in a computer DB

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Probabilistic Models

• To rank by

16
System Architecture
docs
INDEXING
query
Query Rep
Doc Rep
User
Ranking
results
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Technique Term Selection/Weighting

• Basis for matching query with document
• Query and document should be represented using
  the same units/terms
• Controlled vocabulary vs. full text indexing

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What is a good indexing term?

• Specific (phrases) or general (single word)?
• Luhn found that words with middle frequency are
  most useful
• Not too specific
• Not too general
• All words or a (controlled) subset?
• When term weighting is used, it is a matter of
  weighting not selecting of indexing terms
• more later

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Technique Stemming

• Words with similar meanings should be mapped to
  the same indexing term
• Stemming Mapping all inflectional forms of words
  to the same root form, e.g.
• computer -gt compute
• computation -gt compute
• computing -gt compute
• Porters Stemmer is popular for English
• In general clustering of synonym words

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Technique Stopwords

• A common word that bears little semantic
  content
• preposition for, on,
• article a, an, the
• non-informative words (collection specific)
• e.g., database in this class
• e.g., PC in a computer collection
• You can search the Web for stopwords list

21
Technique Relevance Feedback(or Query
Modification)

• Motivation easier to judge results than to
  formulate queries right

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Pseudo Feedback

• Motivation top results are often relevant

Results d1 3.5 d2 2.4 dk 0.5 ...
Retrieval Engine
Query
Updated query
Document collection
Judgments d1 d2 d3 dk - ...
top 10
Feedback
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Technique Inverted List

• ti ? ltd1, gt, .., ltdn, gt
• E.g.,
• color ? ltd1, gt, ltd2, gt, ltd5, gt
• printer ? ltd2, gt, ltd5, gt, ltd8, gt
• How to evaluate Q color AND printer?
• How to evaluate Q color printer?
• what info to maintain in each entry?
• More later

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DB Meets IR

• Multimedia databases
• relational data text, images, audio, video
• Fuzzy retrieval for relational data
• similarity, preference-based queries
• e.g., product search in e-commerce
• XML represents text-based data
• IR type search will be helpful
• how can we extend it to retrieve XML documents?

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?? Web Search

• Text IR as natural starting point
• Web as a collection of HTML documents
• find pages satisfy information need
• Web search as killer-app of IR!
• Web search vs. traditional document search
• ?? how are they related?
• ?? any differences or new issues?
• why search engines give lousy results?

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Web Search New Issues and Challenges

• Highly topic-heterogeneous documents
• notion of collection lost
• stopwords, idf scheme for term selection/weighting
  challenged
• Structured/semi-structured documents
• Highly-linked pages collection no longer flat
• how to use links cleverly link analysis (more
  in TW2)
• ideas from social networks for standing or
  importance
• Extremely large scale Billions docs and counting
• Many documents/data hidden behind databases
• Multi-lingual documents
• Spamming

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Whats Next

• Vector space model

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End of Talk