Sample Searches

1
Sample Searches

• Query smalheiser
• Response DBLP Neil Smalheiser
• PNAS Abstract Smalheiser
  et
• et al 97
• MBC abstract Smalheiser
• UIC Dept of Psychiatry Neil
• Smalheiser

2
Query computer science genetics

• Campus program university, campus, college and
  employment resources
• SpringerLink (On-line journals and books in
  science, technology and medicine)
• Course ( Advanced topics in computer science and
  computational genetics)
• Annual Review of Computer Science

3
Comments for Slides 1-2

• The first two searches are intended to show
  Google is reasonably good
• In retrieving Web pages when given a few
  keywords. In the first query,
• The name Smalheiser is submitted. Ideally, the
  home page of Smalheiser
• should be retrieved first. Instead, his
  publications in computer science are
• retrieved in the first document. This is followed
  by some of his publications
• In the medical area. Finally, his home page in
  Pyschiatry is retrieved.
• The second query asks for important documents in
  the intersection of the two
• areas computer science and genetics. The
  first retrieved document seems
• to be unrelated to the query. The second
  retrieved document seems ok.
• The third document is a course in both areas.
• The examples show that Google is still far from
  perfect.

4
Information Retrieval

• Document RepresentationRemove stop
  wordsEg. In Automatically IdentifyingGene
  terms in MEDLINE AbstractsRemove
  inStemming Automatically becomes
  automaticIdentifying becomes
  identifyAbstracts becomes abstract

5
Comments on last slide
The first two steps in constructing a document
representation consist of eliminating non-content
words and mapping variations of the same word to
the same stem via a process called stemming.
6
Document representation

• Document is a set of content words or terms
  automatic, identify, gene, term, medline,
  abstractSometimes, keep locations of terms.
  Eg. automatic first word in title

7
Comments on last slide

• Location information can be of importance in
  differentiating the ordering of contents words in
  a query from other orderings of the same words.
  It is also useful in determining phrases.

8
Assign weights to terms

• Term frequency no. of times the term occurs in
  the document
• Document frequency no. of documents having
  the term
• The weight of a term in a document
  proportional to term frequency, inversely
  proportional to document frequencyEg term
  frequency log ( N/document frequency)N no.
  of documents in collection

9
Comments on the last slide

• The well-known tf-idf weighting scheme to assign
  a weight to a term is given. The weight is
  proportional to the term frequency and inversely
  proportional to its document frequency. There are
  numerous variations of this formula, but all of
  them have the property that higher weights are
  given to terms with higher term frequencies and
  lower document frequencies.

10
Other factors in assigning weights

• Terms in title
• Terms in abstractTerms in big fonts etc.
• get heavier weights

11
Comments to last slide

• If a term occurs in the title, it usually gets a
  higher weight than the same term occurring in the
  main text. This may apply to the term appearing
  in the abstract. If the term occurs in big fonts
  or a way that attracts readers attention, it
  should also gets a higher weight.
• All these situations can be implemented by
  assuming that each occurrence of such a term is
  equivalent to k occurrences of the same term in
  the main text with k gt1.

12
Query representation

• Two common modelsVector space model query
  as a set of terms, possibly orderedBoolean
  Model Terms connected by AND, OR and NOT

13
Comments to the last slide

• In the information retrieval literature, it has
  been shown that the vector pace model is usually
  better than the Boolean Model, because if a query
  contains quite a few terms which are connected by
  ANDs, then there may not be a document
  satisfying the query. If the terms are connected
  by ORs, then there may be too many unordered
  documents satisfying the query and the user has
  no efficient way to identify the useful documents
  from the irrelevant ones.
• In practice, it is likely that a hybrid model
  having features of both models is used for
  effective retrieval.

14
Vector space model

• Each dimension of a vector represents a distinct
  termdimensions all terms in the collection,
  including proper namesEg. Automatic identify
  gene ( 1, 1, 1, .)

15
Compute the similarity between a query and a
document

• Q (q1, , qn) D (d1, , dn)
• Dot Q, D terms in common, favors long
  documentsNorm( D) Cosine( Q, D) DotQ, D
  /( Norm(D)Norm(Q))

16
Comments on last slide

• When the documents are binary vectors, the Dot
  product similarity function obtains the number of
  terms in common between the two vectors. When the
  terms are weighted, the weights are incorporated
  into the similarity function. Clearly, this
  favors a long document such as an encyclopedia.
• To compensate it, the norm ( length) of a
  document is included in the denominator of the
  similarity function so that a longer document
  gets a larger denominator. The query norm is used
  to ensure that the Cosine function returns a
  value between 0 and 1, if all terms have
  non-negative values. When the two vectors differ
  from each other by a positive multiplicative
  constant, their angular distance is 0 and the
  Cosine value is 1.

17
Boolean model

• gene AND abstract( sometimes, uses to
  ensure the term needs to be present)
• gene OR abstractgene AND NOT abstract( uses
  - to indicate undesiredterms Eg. gene
  abstract)

18
Other features

• Phrase search information retrievalProximity
  search information NEAR retrievalDate
  search 2003Field search Eg in the field
  Author, look for NealWildcard search
  smaler

19
Comments on last slide

• Some systems require a query phrase such
• as information retrieval to be placed in
• quotes. This may require a retrieved document to
  have exactly such a phrase.
• If a document containing the words retrieval of
  information is desired, the query can be
  reformulated as Information near retrieval.
• Filtering operations can be specified by filling
  in additional information in specific fields such
  as the author field. Wildcard entries such as
  smaler, where denotes zero or more
  characters are allowed, provided that does
  not occur in the first few characters ( say 3),
  otherwise the space for searching matching
  strings will be too large.

20
Additional features

• Case sensitive java gets java, Java,
  JAVAJava gets Java and possibly JAVA ( first
  capital letter implies a proper name )
• ordered query terms eg. stray dog
• spelling error if no such word,some search
  engines suggests similar words

21
Comments on last slide

• Location information in documents and the query
  can be used to differentiate stray dog from dog
  stray.
• If a word does not exist in the index of all
  words in the documents, then some search engines
  may suggest some neighboring words which differ
  from the misspelled word by 1 or 2 characters.
  Note that proper names are included in the index.

22
Directory search

• Specify subtree computer finance medicinehard
  ware software .. .
• .
• query memory under computer means computer
  memory vs human memory in medicine

23
Comments in last slide

• Directory search may reduce ambiguities.
• In the given directory, documents or pages are
  classified under each node. For example, there is
  a set of documents which are classified under
  computer and another class under medicine. The
  former class contains documents about computer
  memory while the latter class contains documents
  about human memory. If the query is restricted to
  the class computer, then only documents in the
  former class relating to computer memory will be
  retrieved.

24
Feedback

• identify relevant documents and possibly
  irrelevant documentsre-formulate query using
  terms from relevant documents and from irrelevant
  documents
• Query apple Rel Doc computer Irrel fruit
• Modified query apple, computer, - fruit

25
Comments to last slide

• The user needs to identify relevant documents and
  possibly irrelevant documents. Terms from the
  relevant documents may be added to the query,
  while terms from the irrelevant documents may be
  used to exclude documents having such terms to be
  retrieved in the next round. In the example, the
  term computer is found in the relevant
  documents and is added to the query, while the
  term fruit is found in the irrelevant documents
  and it is used to exclude documents having such a
  word.

26
Web

• Surface Web linked togetherDeep Web Not
  linked documents can be generated dynamically by
  programsQuite a few medical databases and
  bio-medical databases are in the Deep Web

27
Comments on the last slide

• The Web is roughly classified into the
• Surface web and the Deep Web. The pages in the
  former are hyperlinked, while pages in the latter
  are accessible only by submitting queries to
  query interfaces.
• Web crawlers which extract content information
  from Surface Web pages are unable to get into
  Deep Web pages for lack of hyperlinks.

28
Retrieval from the surface Web

• Anchor text belong to the document pointed
  to.
• lta href"http//tigger.uic.edu/htbin/cgiwrap/bin/n
  ewsbureau/cgi-bin/index.cgi"gtMore Newslt/agt
• Page rank importance of a Web pageRank( P)
  
• for every Qi pointing to P iterative Web
  surfing interpretation

29
Comments on last slide

• There are some differences between retrieval from
  the Web and from non-Web sources. In the former
  case, words known as anchor texts which appear
  together with the link from a page A to another
  page B should be utilized for retrieval.
  Specifically, the anchor words should be used as
  content words for page B, as they describe the
  contents of B as observed by the user who creates
  A.

30
Example to illustrate page rank

• Rank(P) ½ Rank(A) 1/3 Rank(B)
• A lot of pages pointing to the IBM home page,
  implying that it has a very high page rank.

A
P
B
31
Comments on last slide

• The example illustrates how the page rank of a
  page can be computed. In practice, all pages are
  initialized with the same rank and the page rank
  formula is applied to compute the page ranks of
  all pages. This process is repeated until
  convergence is reached. Under some reasonable
  assumptions, convergence is guaranteed. The page
  rank information is utilized to rank pages for
  any user query.

32
Query IBM

• Thousands of pages have that word, but among
  those pages having that word, IBM home page has
  largest rank.Google utilizes page rank

33
Comments on last slide

• There are a number of ways to utilize page ranks
  to rank pages for a given query. One way is to
  first retrieve pages which have reasonable
  similarities with the query. Then the retrieved
  pages are re-ranked in descending order of page
  rank. Another way is to compute the relevance of
  a page based on a function of the similarity of
  the page with the query and its page rank. Then
  pages are re-ranked in descending order of
  relevance.

34
Authority and Hub

• Query retrieves documents based on similarities
• Expand this set by adding their parents and their
  children
• Compute A(p) sum H(q) for each edge (q,p)
• Compute H(p) sum A(q) for each edge (p,q)

35
Authority and Hub continued

• Normalize A(p) and H(p)
• Repeat until A() and H() converge
• Output pages with top authority scores
• ( It has been shown that convergence is
  guaranteed.)
• www.teoma.com
• ( This company claims to have an advanced search
  capability which is more accurate than the
  standard authority and hub technique.)

36

• Various features of different search engines,
  including Google, AltaVista, Hotbot etc
• Search Engines for the World Wide Web
• By Alfred and Emily Glossbrenner, 3rd
  edition,Peachpit Press, 2001.

37
Metasearch engine

• Connects to numerous search engines.Given
  query Q, finds suitable search engines to process
  the query, invokes the selected search engines to
  search and merges their results.

38
Comments on last slide

• Instead of using a search engine such as Google,
  a metasearch engine which connects to numerous
  search engines can be utilized. Upon receiving a
  user query, a metasearch engine sends the query (
  with possibly some modifications) to appropriate
  search engines and merges and re-ranks the
  retrieved documents returned from the invoked
  search engines.

39
Advantages of Metasearch Engines over Search
Engines

• Do not need substantial hardware relative to
  large search engines
• Large coverage
• up-to-date information.

40
Comments to last slide

• There is no need for substantial hardware,
  because the searches are done by the underlying
  search engines. The coverage of a metasearch
  engine is the union of the coverages of the
  individual search engines. That it may have more
  up-to-date information than a large search engine
  will be explained by the next few slides.

41
Up-to-date information

• Search engine crawler gets data
• Builds large index database
• Time consuming to update large index database
• Metasearch engine connects to numerous small
  search engines

42
Comments to last slide

• A search engine utilizes a crawler to extract
  contents from Surface Web pages and then builds
  an index database. Upon receiving a query, the
  search engine searches the index database to
  determine the pages to return to the user. Since
  the contents of Web pages keep on changing, the
  index database needs to be updated. However, the
  index database is large and refreshing it may
  take a long time, say weeks. In contrast, if a
  metasearch engine is connected to numerous small
  search engines and each of these search engines
  keeps its database up-to-date, the metasearch
  engine may be able to provide current information.

43
Utilizes dictionary/ontology

• Wordnet ordinary dictionary terms
• MeSH hierarchy medical terms
• May want to include synonyms and hyponyms of
  query terms into query

Person --- (Synonyms human, people)
Hyponyms man woman
44
Comments to last slide

• Dictionaries or ontologies may be utilized to
  achieve high retrieval effectiveness. A common
  dictionary in a general domain is Wordnet which
  provides synonyms, hyponyms as well as other
  relationships to each ordinary word. As an
  example, if a query contains the word person,
  its synonyms and hyponyms may be added to the
  query. Note that a word may have multiple senses
  (meanings) and selections of suitable synonyms
  and hyponyms are essential. It is worthwhile to
  explore the use of the MeSH hierarchy for
  effective retrieval in the medical domain.

45
Difficulty

• A word sometimes has many sensesEg Query
  drugs for mental patients
• senses for drugs prescription drugs illegal
  drugsuseful to include antidepressantwill
  retrieve a lot of irrelevant documents if include
  heroin

46
Comments to the last slide

• The example shows that a correct addition of a
  hyponym (antidepressant is a hyponym of drug)
  will lead to high retrieval effectiveness while
  an incorrect addition (heroin is also a hyponym
  of drug) leads to poor retrieval results.

47
Natural language Processingfor Information
Retrieval

• finds part-of-speech of each word
• identify noun phrases
• identify proper names
• recognizes acronymseg. CHF congestive heart
  failures
• Word sense disambiguation
• eg. Apple CPU

48
Comments to last slide

• Natural language processing plays a role in
  information retrieval. However, so far, it is
  used to identify parts of speech of words, named
  entities and phrases only.
• Recognition of acronyms is also useful for
  information retrieval.

49
Word Sense Disambiguation

• Pine 1 kinds of evergreen tree with needle-shaped
  leaves
• 2 waste away through sorrow or illness
• Cone 1 solid body which narrows to a point
• 2 fruit of certain evergreen trees
• Find the combination of descriptions which have
  the largest number of words in common.

50
Comments to last slide

• If the query is pine cone and each of the two
  words has multiple senses, the correct sense may
  be identified by finding the combination of
  senses whose descriptions have the largest number
  of words in common. In this example, sense 1 of
  pine and sense 2 of cone have the words
  evergreen tree in common in their
  descriptions. These common words may be added to
  the query to improve retrieval effectiveness.

51
Information extraction

• Information retrieval obtains whole documents
  often users want small parts of retrieved
  documents.Examples
• From certain papers on heart disease, extract
  names of authorsfrom experimental sections of
  papers, extract tables of interest.

52
Techniques

• (1) Construct rules involving patterns or
  keywords of identify parts of interest utilizes
  a grammar to extract required informationEg. To
  identify terrorist eventsuseful keywords kill,
  bomb etc. use a grammar to identify the subjects
  (terrorists) and the objects (victims)

53
Comments

• Traditionally, information extraction is achieved
  by manually constructed rules for the extraction,
  after examining numerous instances of what are
  desired. In order to save labor cost, machine
  learning techniques are introduced. Rules are
  automatically constructed and based on positive
  and negative examples, promising rules are kept
  for future extraction activities.

54

• (2) Use machine learning techniques to
• construct rules
• Positive and negatives examples can
• be given to guide the construction
• Aim Reduce manual construction of rules

55
Machine learning

• Example Pavilion a230 Minitower
• AMD Athlon XP .. GHz
• .
• Pavilion a210n Minitower
• Intel Celeron GHz
• Rule (var1) ( var2) GHz

56
Comments

• In this example, the user supplies a few positive
  instances to be extracted. Then, the system
  automatically constructs the rule with R and GHz
  as landmarks. The words before the landmarks are
  captured by variables.
• In the Web environment, HTML or XML documents
  have tags and they may be used to construct
  rules. However, rules involving tags may be site
  dependent, implying that new rules may need to be
  generated when there is a site change.

57
Rules involving tags

• ltbgt Martino Motor Sales lt/bgt
• ltbgt Currie Motors Lincoln Memory lt/bgt
• Rule ltbgt Var lt/bgt
• Extracted data may not be that structured
• Layout of document can be site dependent,
  implying that new correct rules need to be
  constructed for new sites

58
Summary

• Information retrieval
• users point of view
• eg. phrase, case sensitive
• system point of view
• eg. Feedback query construction
• Web retrieval vs non-web retrieval
• search engine vs metasearch engine

59
Summary continued

• Natural language processing
• Eg. acronym recognition
• Information extraction
• rules manual, machine learning
• Can be site dependent