structured querying of web text a technical challenge

Structured Querying of Web Structured Querying of Web TextText

A Technical ChallengeA Technical Challenge

Kulsawasd JitkajornwanichKulsawasd Jitkajornwanich

University of Texas at ArlingtonUniversity of Texas at Arlington

[email protected]@hotmail.com

CSE6339 Web Mining | April 16, 2009 | 9:30 amCSE6339 Web Mining | April 16, 2009 | 9:30 am

by Cafarella, Re’, Suciu, Etzioni & Banko

IntroductionIntroduction

What is What is structured-querystructured-query?? 2 types of query: Structured-query & 2 types of query: Structured-query &

Unstructured-queryUnstructured-query 1. Structured-query1. Structured-query

Has “condition” in the query Has “condition” in the query Can make a complicated queryCan make a complicated query ex. ex. ““SQL query”SQL query”

List employee whose name start with ‘David’ and salary List employee whose name start with ‘David’ and salary > 5000> 5000

SELECTSELECT E.name, E.salary E.name, E.salary FROM FROM Employee EEmployee E WHERE WHERE E.name LIKE ‘David’, E. salary > 5000E.name LIKE ‘David’, E. salary > 5000

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What is What is structured-querystructured-query?? 2. Unstructured-query2. Unstructured-query

ex. ex. ““Keyword Search”Keyword Search” no “condition” in the queryno “condition” in the query simply do “string matching”simply do “string matching”

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--> we just talked about type of query --> we just talked about type of query <--<--

What about type of data?What about type of data? 2 types of data:2 types of data:

1. Structured-data1. Structured-data ex. Relational tablesex. Relational tables

2. Unstructured-data2. Unstructured-data ex. Web documentsex. Web documents

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IntroductionIntroduction Objective of the paper:Objective of the paper:

To propose a tool called To propose a tool called ExDBExDB to make a to make a structured-structured-queryquery on web documents on web documents (unstructured-data)(unstructured-data)

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RelationalDatabase

Web Text

SQL Query

SQL Query

ExDB

Unstructured-query(Keyword Search)

Structured-query(Complicated query

like SQL-query)

Search Engine

Structured-data

Unstructured-data

How it works: How it works: Big Picture ofBig Picture of ExDBExDB

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Collection of web documents

ExDB Extractor

Fact Table

Type Table

Constraint TableUser

ExDB Complier

q(?x,?y):- invented(?x,?y)

RDBMS Database

Resulting Table


77


ExDB Extractor

Fact Table

Type Table

Constraint TableUser

ExDB Complier

q(?x,?y):- invented(?x,?y)

RDBMS Database

Resulting Table

OutlineOutline

11stst Component Component: : ExDB ExtractorExDB Extractor What/How does it do in more detail?What/How does it do in more detail?

22ndnd Component: Component: ExDB CompilerExDB Compiler What/How does it do in more detail?What/How does it do in more detail?

Test your understanding!!Test your understanding!! Working on tasksWorking on tasks Compare result Compare result ExDBExDB & & GoogleGoogle ConclusionConclusion

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How How ExDBExDB WorksWorks

11stst Component: Component: ExDB ExtractorExDB Extractor What does it do?What does it do?

To To extract dataextract data from the from the web documentsweb documents & & put itput it into the into the tablestables

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How How ExDBExDB WorksWorks

22ndnd Component: Component: ExDB CompilerExDB Compiler What does it do?What does it do?

To To processprocess the user’s the user’s structured-query structured-query on on the tables from 1the tables from 1stst component ( component (ExDB ExDB ExtractorExtractor) and give the ) and give the resulting tableresulting table back to userback to user

ex. ex. q(?x, ?y):- invented(?x, ?y)q(?x, ?y):- invented(?x, ?y) <we will study this query syntax later on><we will study this query syntax later on>

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1111

RDBMS Database


…was surprising. In

1877, Edison

invented the light bulb. Although he

…

ExDB Extractor

Fact Table

Type Table

Constraint Table User: Make a Make a query query using using ExDBExDB syntaxsyntax

ExDB Complier

11stst Component: Component:

ExDB ExDB ExtractorExtractor

22ndnd Component: Component:

ExDB ExDB CompilerCompiler

11stst Component: Component: ExDB ExtractorExDB Extractor

What does it do?What does it do? To To extract dataextract data from the from the web documentsweb documents & & put put

itit into the into the tablestables There are 3 tables:There are 3 tables:

1. Fact Table1. Fact Table 2. Type Table2. Type Table 3. Constraint Table3. Constraint Table

Additional column: stores tuple probabilityAdditional column: stores tuple probability Discussion:Discussion: Why do need this column?Why do need this column?

0<p<1, 0<p<1, p pi i = 1= 1 One way to assign probability: Counting occurrence One way to assign probability: Counting occurrence

frequencyfrequency Assume Assume IndependenceIndependence among tuples among tuples

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1.1 Fact Table1.1 Fact Table Stores Stores fact informationfact information

ex. “Edison invented light bulb” ex. “Edison invented light bulb” Uses Uses TextRunnerTextRunner to extract to extract How is it look like?How is it look like?

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PredicatePredicate Object 1Object 1 Object 2Object 2 ProbabilityProbability

inventedinvented EdisonEdison Light bulbLight bulb 0.750.75

died-indied-in EdisonEdison 18771877 0.550.55

…… …… …… ……

Fact TableFact Table

Probability= no of occurrence / no of predicate occurrences


Example1: shows how to get Example1: shows how to get Fact tableFact table

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InventedInvented EdisonEdison Light bulbLight bulb 0.750.75

InventedInvented EdisonEdison PhonographPhonograph 0.250.25

…… …… …… ……



1877, Edison


…

It was a big news when

Edison invented the light bulb.

…

…We all know that Edison invented light bulb.

…not only that Edison also invented the phonograph

.

Probability = no of occurrencesno of predicate

occurrences

Object

Predicate

TextRunnerTextRunnerTextRunnerTextRunner

Discussion:Discussion: What do you think might be a problem with this What do you think might be a problem with this

design of fact table?design of fact table? Cannot support Ternary-predicate --> ex. David donates books to Child Organization.

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…… …… …… ……



1.2 Type Table1.2 Type Table Stores Stores object type informationobject type information

ex. Edison is a scientist.ex. Edison is a scientist. Uses Uses KnowItAllKnowItAll to extract to extract How is it look like?How is it look like?

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TypeType ObjectObject ProbabilityProbability

ScientistScientist EdisonEdison 0.730.73

CityCity BostonBoston 0.360.36

…… …… ……

Type TableType Table

Probability= no of occurrence / no of type occurences


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TypeType ObjectObject ProbabilityProbability

scientistscientist EdisonEdison 0.750.75

ScientistScientist BenjaminBenjamin 0.250.25

…… …… ……


…As we know,

Edison is a scientist.

Although he …

… there are many world-

famous scientists

such as Edison,

…

…However, someone claim that

Benjamin is also an

scientist.

…scientists

such as Edison, …

Probability = no of occurrencesno of type

occurrences

Object Type

KnowItAllKnowItAll

Example2: shows how to get Example2: shows how to get Type tableType table

1.3 Constraint Table1.3 Constraint Table Stores Stores constraint information constraint information of objects or of objects or

predicatespredicates There are 2 types of constraints discussed in There are 2 types of constraints discussed in

this paper: Synonym and Inclusion Dependencythis paper: Synonym and Inclusion Dependency Uses Uses DIRTDIRT to extract to extract

1. Synonym1. Synonym example for predicate: did-invented = example for predicate: did-invented = inventedinvented example for object: Edison T. =example for object: Edison T. = Edison Edison

2. Inclusion Dependency2. Inclusion Dependency example for predicate: be-guardian example for predicate: be-guardian be-parentbe-parent example for object: relative example for object: relative sistersister

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example shows howexample shows how DIRT DIRT worksworks

for for SynonymSynonym constraint constraint

1111


1877, Edison


…Collection of web documents

DIRT

Thomas E.

Edison T.Thomas Edison

Thomas Edison

example shows howexample shows how DIRT DIRT worksworks

for for Inclusion DependencyInclusion Dependency constraintconstraint

1111


1877, Edison


…Collection of web documents

DIRT

Be-parent

Be-guardian Be-babysitter

1.3 Constraint Table1.3 Constraint Table How is it look like?How is it look like?

2020

ConstraintConstraint Object 1Object 1 Object 2Object 2 ProbabilityProbability

SynonymSynonym EdisonEdison T. EdisonT. Edison 0.750.75

Inclusion Inclusion DependencyDependency

Be-parentBe-parent Be-guardianBe-guardian 0.550.55

…… …… …… ……

Constraints TableConstraints Table


Superset

Subset

Key point summary of 1Key point summary of 1stst component: component: (ExDB Extractor)(ExDB Extractor) 1. ExDB Extractor uses different kinds 1. ExDB Extractor uses different kinds of existing extractor: of existing extractor: TextRunnerTextRunner, , KnowItAllKnowItAll and and DIRTDIRT..

2. 2. Probabilistic columnProbabilistic column is used to is used to indicate the indicate the degree of correctnessdegree of correctness and and deal with deal with uncertainty problemuncertainty problem..

3. Drawback of fact table, only 3. Drawback of fact table, only Binary Binary PredicatePredicate is allowed. is allowed.

2222



2323

RDBMS Database



1877, Edison


…

ExDB Extractor

Fact Table

Type Table

Constraint Table User: Make a Make a query query using using ExDBExDB syntaxsyntax

ExDB Complier

11stst Component: Component:

ExDB ExDB ExtractorExtractor

22ndnd Component: Component:

ExDB ExDB CompilerCompiler

What does it do?What does it do? To To processprocess the user’s the user’s structured-query structured-query

on the tables from 1on the tables from 1stst component ( component (ExDB ExDB ExtractorExtractor))

Result will be in Result will be in tabletable format and format and ranked ranked by highest probability valueby highest probability value..

ex. ex. q(?x, ?y):- invented(?x, ?y)q(?x, ?y):- invented(?x, ?y) However, users are not expected to However, users are not expected to

know the table schema.know the table schema.

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22ndnd Component: Component: ExDB CompilerExDB Compiler

ExDBExDB syntax: syntax: ?x ?x = variable = variable xx w w = constant value = constant value ww q(?x,?y):- q(?x,?y):- = define resulting table = define resulting table qq consisting consisting of column of column xx and and yy

invented(?x,?y) invented(?x,?y) = return list of object x and y = return list of object x and y regarding predicate “invented”regarding predicate “invented”

invented(<scientists> ?x,?y)invented(<scientists> ?x,?y) = return list of = return list of object object xx whose type is whose type is <scientists><scientists> and and yy regarding predicate regarding predicate “invented”“invented”

This syntax is calledThis syntax is called “Datalog-like notation” “Datalog-like notation” Let’s try some examples!Let’s try some examples!

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q(?x, ?y):-invented(?x, ?y)q(?x, ?y):-invented(?x, ?y)

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Make a QueryMake a Query example:example:



inventedinvented EdisonEdison TelescopeTelescope 0.140.14

inventedinvented EdisonEdison PhonographPhonograph 0.140.14

inventedinvented JasonJason Cell phoneCell phone 0.140.14

died-indied-in Mary T.Mary T. 18771877 0.050.05


example4:example4: list all inventions invented by Edisonlist all inventions invented by Edison

answeranswer:: q(?i):- invented(Edison, ?i)q(?i):- invented(Edison, ?i)

ii ProbabilityProbability

Light bulbLight bulb 0.550.55

TelescopeTelescope 0.140.14

PhonographPhonograph 0.140.14

q Tableq Table

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PredicatePredicate Object 1Object 1 Object 2Object 2 ProbProb



inventedinvented David A.David A. GuitarGuitar 0.300.30

died-indied-in PeterPeter 19551955 0.200.20



example5:example5: list all scientist died in 1955list all scientist died in 1955

TypeType ObjectObject ProbProb


scientistscientist PeterPeter 0.150.15

scientistscientist Mary T.Mary T. 0.150.15

scientistscientist David A.David A. 0.100.10

citycity BostonBoston 0.050.05


answer:answer: q(?i):- died-in(<scientist> ?i, 1955) q(?i):- died-in(<scientist> ?i, 1955)

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example5:example5: list all scientist died in 1955list all scientist died in 1955








TypeType ObjectObject PredicatePredicate ObjectObject ProbProb

scientistscientist EdisonEdison died-indied-in 19551955 0.200.20

scientistscientist PeterPeter died-indied-in 19551955 0.030.03

Joining TableJoining Table 0.20 = 0.50 x 0.40 because we

assume independence among tuples;

i.e,P(t1, t2)=P(t1) *

P(t2)

?i?i ProbProb

EdisonEdison 0.200.20

PeterPeter 0.030.03

q Tableq Table

answer:answer: q(?i):- died-in(<scientist> ?i, 1955) q(?i):- died-in(<scientist> ?i, 1955)

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example6:example6: list all scientist who died after 1900, their list all scientist who died after 1900, their

inventions and year they diedinventions and year they died








answeranswer:: q(?x, ?y, ?z):- invented(?x, ?y),q(?x, ?y, ?z):- invented(?x, ?y),

died-in(<scientist> ?x, ?z),died-in(<scientist> ?x, ?z), (z > 1900)(z > 1900)

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example6:example6: list all scientist who died after 1900, their list all scientist who died after 1900, their

inventions and year they diedinventions and year they died








TypeType PredicatePredicate PredicatePredicate ObjectObject ObjectObject ObjectObject ProbProb

scientistscientist died-indied-in inventedinvented EdisonEdison 19551955 light light bulbbulb

0.140.14

Joining TableJoining Table

0.14 = 0.50 x 0.40 x 0.70

?x?x ?y?y ?z?z ProbProb

EdisonEdison Light Light bulbbulb

19551955 0.140.14

q Tableq Table

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Test Your Understanding!Test Your Understanding!



playplay JohnJohn GuitarGuitar 0.400.40


PlayPlay JacksonJackson PianoPiano 0.200.20

playplay JacksonJackson GuitarGuitar 0.050.05

Born-inBorn-in JohnJohn 19901990 0.050.05

Born-inBorn-in JacksonJackson 19801980 0.050.05

Born-inBorn-in BobbyBobby 19801980 0.050.05


Problem1:Problem1: list all singer who born in 1980, their instrumentslist all singer who born in 1980, their instruments


SingerSinger JohnJohn 0.500.50

instrumentinstrument GuitarGuitar 0.150.15

instrumentinstrument pianopiano 0.150.15

SingerSinger JacksonJackson 0.100.10

SingerSinger BobbyBobby 0.050.05


answeranswer:: q(?x, ?y):- q(?x, ?y):- play(<singer> ?x,<instrument> ?y),play(<singer> ?x,<instrument> ?y),

born-in(<singer> ?x, 1980)born-in(<singer> ?x, 1980)

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Test Your Understanding!Test Your Understanding!


Being-producerBeing-producer MattMatt BobbyBobby 0.700.70

Being-producerBeing-producer MattMatt JacksonJackson 0.400.40

Has-incomeHas-income BobbyBobby 25002500 0.300.30

Has-incomeHas-income JacksonJackson 30003000 0.200.20

Has-incomeHas-income MattMatt 20002000 0.050.05

Being-producerBeing-producer MattMatt JohnJohn 0.050.05

Has-incomeHas-income JohnJohn 10001000 0.050.05


Problem2:Problem2: list all singer who has income more than their list all singer who has income more than their

producerproducer


SingerSinger JohnJohn 0.500.50

ProducerProducer MattMatt 0.150.15

ProducerProducer DavidDavid 0.150.15

SingerSinger JacksonJackson 0.100.10

SingerSinger BobbyBobby 0.050.05


answeranswer:: q(?x):- has-income(<singer> ?x, ?y),q(?x):- has-income(<singer> ?x, ?y),

has-income(<producer> ?m, ?n),has-income(<producer> ?m, ?n), being-producer(?m, ?x),being-producer(?m, ?x),

(?y > ?n)(?y > ?n)

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PredicatePredicate Obj.1Obj.1 Obj.2Obj.2 ProbProb


inventedinvented EdisonEdison TelescopeTelescope 0.140.14

inventedinvented EdisonEdison PhonographPhonograph 0.140.14

inventedinvented JasonJason Cell phoneCell phone 0.140.14

died-indied-in MaryMary 18771877 0.050.05


example7:example7: list all inventions discovered by Edisonlist all inventions discovered by Edison

answeranswer:: q(?i):- discovered(Edison, ?i)q(?i):- discovered(Edison, ?i)

ii ProbabilityProbability

Light bulbLight bulb 0.55 x 0.50.55 x 0.5

TelescopeTelescope 0.14 x 0.50.14 x 0.5

PhonographPhonograph 0.14 x 0.50.14 x 0.5

q Tableq Table

ConstConst Obj.1Obj.1 Obj.2Obj.2 ProbProb

IDID InventedInvented discovereddiscovered 0.500.50

SynSyn EdisonEdison Edison TEdison T 0.150.15

SynSyn EdisonEdison Thomas EThomas E 0.100.10

Constraint TableConstraint Table

DiscussionDiscussion:: In this case, What can we do In this case, What can we do

to answer this query?to answer this query?

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Make a QueryMake a Query Problem ScenarioProblem Scenario

3333

example8: (this example involves PROJECTION)example8: (this example involves PROJECTION) list all name who invented somethinglist all name who invented something

?x?x ?y?y ProbProb

EdisonEdison light bulblight bulb 0.340.34

EdisonEdison telescopetelescope 0.130.13

EdisonEdison PhonographPhonograph 0.130.13

TreeTree TableTable 0.090.09

Tree Tree Pen Pen 0.090.09

TreeTree PaperPaper 0.090.09

TreeTree FruitFruit 0.090.09

TreeTree ForestForest 0.090.09

TreeTree EraserEraser 0.090.09

TreeTree rulerruler 0.090.09


answeranswer:: q(?x):- invented(?x, ?y)q(?x):- invented(?x, ?y)

?x?x ProbProb

TreeTree 0.630.63


q Tableq Table 0.63 = 0.09 x 7

Discussion:

•Can you see something wrong in the resulting table?

Problem scenario caused by projection Problem scenario caused by projection operation.operation.

Conventional Way: Conventional Way: newProb = newProb = duplicateProb duplicateProbii

New Way: New Way: usingusing “Panel of Expert” “Panel of Expert” techniquetechnique principle:principle:

1.define number n of duplicate output 1.define number n of duplicate output ex. n=5 (meaning that if in total, there are 10 ex. n=5 (meaning that if in total, there are 10 duplicate output, we will consider only 5 and duplicate output, we will consider only 5 and eliminate other 5) to eliminate low quality eliminate other 5) to eliminate low quality output.output.

2.newProb = calculate by selecting the max value 2.newProb = calculate by selecting the max value among those n duplicate output.among those n duplicate output.

newProb = max {duplicateProbnewProb = max {duplicateProbii}; i}; in n

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Solving Problem Scenario by usingSolving Problem Scenario by using ‘‘Panel of ExpertPanel of Expert’’

techniquetechnique

22

Make a QueryMake a Query Problem Scenario:Problem Scenario:

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example8: (problem caused by projection operation)example8: (problem caused by projection operation) list all name who invented somethinglist all name who invented something

?x?x ?y?y ProbProb

EdisonEdison light bulblight bulb 0.340.34

EdisonEdison telescopetelescope 0.130.13

EdisonEdison PhonographPhonograph 0.130.13

TreeTree WrongInfo1WrongInfo1 0.090.09








answeranswer:: q(?x):- invented(?x, ?y),q(?x):- invented(?x, ?y),

?x?x ProbProb


TreeTree 0.090.09

q Tableq Table

0.63 = 0.09 x 7

Solved by“Panel of Expert”

technique

?x?x ProbProb

TreeTree 0.630.63


q Tableq Table

Key points summary of 2Key points summary of 2ndnd Component: Component: (ExDB Compiler)(ExDB Compiler)1.1. ExDBExDB has its own syntax. has its own syntax.2.2. Result will be in table format.Result will be in table format.3.3. Last column is probability value Last column is probability value ranked by ranked by

decreasingdecreasing order of probability value. The order of probability value. The assumption is that the assumption is that the higher probabilityhigher probability, the , the more more accurateaccurate..

4.4. Can implement top K to reduce time complexity Can implement top K to reduce time complexity (increase performance).(increase performance).

5.5. In case of In case of JOIN JOIN table, the resulting probability table, the resulting probability the product of 2 joining tablethe product of 2 joining table

6.6. In case of In case of PROJECTIONPROJECTION, use , use Panel of ExpertPanel of Expert to solve to solve the problem.the problem.

7.7. In case that user’s query contains In case that user’s query contains relationrelation which which does not exist in the Fact Tabledoes not exist in the Fact Table, we can use , we can use Constraint TableConstraint Table to answer such a query. to answer such a query.

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Working On Task#1Working On Task#1

Synthetic TableSynthetic Table an additional feature to combine the an additional feature to combine the

result query q togetherresult query q together example:example:

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Synthetic Table generated by MERGING answers fromdied-in(?x,?y),invented(?x,?y),published(?x,?y),taught(?x,?y)

Working On Task#2Working On Task#2

Implementing with Google Search EngineImplementing with Google Search Engine

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list all scientist, their inventions,who died before 1955

Search Textbox

GO

q(?x, ?y):- invented(<scientists>?x, ?y), died-in(?x, ?z), (?z < 1955)

Compare result Compare result ExDBExDB && GoogleGoogle Test query:Test query: list all scientists who create list all scientists who create somethingsomething

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Output from ExDB

Output from GoogleComments:Comments:

ExDB performs much better than Google.ExDB performs much better than Google. For Google result, after investigating all the link, only For Google result, after investigating all the link, only

1 document comes close to the answer.1 document comes close to the answer. For ExDB, although they have some redundancy, answer is For ExDB, although they have some redundancy, answer is

still better.still better.

ConclusionConclusion

Only Binary Predicate is allowed.Only Binary Predicate is allowed.

Result will be in table format (different from Google search Result will be in table format (different from Google search engine).engine).

How How ExDBExDB get answer makes more sense since they get answer makes more sense since they integrate integrate all data together before we make a query on themall data together before we make a query on them..

Extractor has to run beforehand before allowing user to make Extractor has to run beforehand before allowing user to make a query.a query.

IE involved in this paper are TextRunner, KnowItAll, DIRT.IE involved in this paper are TextRunner, KnowItAll, DIRT.

User is not expected to know the schema of the table, instead, User is not expected to know the schema of the table, instead, system itself will try to match as much as they can to answer system itself will try to match as much as they can to answer the query (using synonym, inclusion independency). the query (using synonym, inclusion independency).

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Question?Question?

4242

??

ReferencesReferences

N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.

D. V. K. Reynold Cheng and S. Prabhakar. Evaluating proba bilistic queries over imprecise data. In SIGMOD, pages 551

–562, 2003.

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structured querying of web text a technical challenge

Documents

exdb extractorpredicateobject

component exdb extractor

exdb extractorwhathow

exdb compilerwhathow

types of query

fact table1st component

web mining

sql query list employee