Summit2013 georg gottlob and tim furche - diadem

DIADEM data extraction methodologydomain-centric intelligent automated

DIADEMDomains to Databases

Georg Gottlob and Tim Furche (Vienna University of Technology and Oxford University)

July 2013 @ STI Summitjoint work with Giovanni Grasso, Omer Gunes, Xiaonan Guo, Andrey Kravchenko, Thomas Lukasiewicz,

Giorgio Orsi, Andreas Pieris, Christian Schallhart, Andrew Sellers, Gerardo Simari, Cheng Wang

About us …

DIADEM lab at Oxford University

2

2010 2011 2012 2013 2014 2015

DIADEM

About us …

DIADEM lab at Oxford University

2

2010 2011 2012 2013 2014 2015

DIADEM

3

3

DIADEM

4

5

DIADEM ›❯ The State of Search

Search engines don’t cut it any more …

6

20121995 2000 2004 2008Jahr

Web

pag

es

Search Results

Overall Content

DIADEM ›❯ The State of Search

Search engines don’t cut it any more …

6

20121995 2000 2004 2008Jahr

Web

pag

es

Search Results

Overall Content

What humans can process

DIADEM ›❯ The State of the Game7

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favors “big” aggregators & news sites

with poor quality results

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11Microsoft Bing:

“Model Every Object on the Planet”

11Microsoft Bing:

“Model Every Object on the Planet”Google:

“Knowledge Graph: things, not strings”

http://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html




11Microsoft Bing:







11Microsoft Bing:



common sense, static facts

wikipedia-like

requires high degree of redundancy

same information on many sites

not for dynamic, product data





DIADEM ›❯ The State of the Game

Web Data Extraction

ref-code postcode bedrooms bathrooms available price

33453 OX2 6AR 3 2 15/10/2013 £1280 pcm

33433 OX4 7DG 2 1 18/04/2013 £995 pcm

12


: Supervised Data Extraction

Navigation Steps

Mozilla Web Browser

Extraction Configuration

13


Need for Automatic Extraction Technology

14

Example: Real Estate UK > 15000 sites

many not covered by aggregators

list of all agencies easy to get (source discovery)

but: manual or semi-automatic wrapping too expensive

wrapper construction

testing

tracking changes

No existing tool or methodology can do it fully automatically



15

All search engine providers need it! Many work on it.

vertical search

object search

semantic search

no one really has done this successfully at scale yetRaghu Ramakrishnan, Yahoo!, March 2009

current technologies are not good enough yet to provide what search engines really need. […] any successful approach would

probably need a combination of knowledge and learning Alon Halevy, Google, Feb. 2009

DIADEM ›❯ What?16


This study shows: significant long-tail effect for many attributes

>1000 sites to get above 80% coverage required

Examples of these attributes:

phone numbers and home pages of companies

restaurants, car sellers, hotels, banks, …

ISBN of books

reviews of hotels and restaurants

An analysis of structured data on the web, Dalvi et al. (Yahoo) VLDB 2012

for many kinds of information one may have to extract from thousands of sites in order to build a comprehensive database, even

when we restrict to a given domain with known popular top sites

DIADEM ›❯ What?

Domain-Centric Data Extraction

17

1 <?xml version ="1.0" encoding="UTF-8"?> 2 <results> 3 <tyre> 4 <brand>Star Performer</brand> 5 <profile>HP</profile> 6 <price>42.60</price> 7 </tyre> 8 <tyre> 9 <brand>High Performer</brand> 10 <profile>HS-3</profile> 11 <price>39.40</price> 12 </tyre> 13 ... 14 </results>

Blackbox that

turns any of the thousands of websites of a given domain

into structured data

DIADEM ›❯ What?

Domain-Centric Data Extraction

17

1 <?xml version ="1.0" encoding="UTF-8"?> 2 <results> 3 <tyre> 4 <brand>Star Performer</brand> 5 <profile>HP</profile> 6 <price>42.60</price> 7 </tyre> 8 <tyre> 9 <brand>High Performer</brand> 10 <profile>HS-3</profile> 11 <price>39.40</price> 12 </tyre> 13 ... 14 </results>

Blackbox that

turns any of the thousands of websites of a given domain

into structured data

DIADEM

Web Data Extraction

Scenario ➀: Electronics retailer

electronics retailer: online market intelligence

comprehensive overview of the market

daily information on price, shipping costs, trends, product mix

by product, geographical region, or competitor

thousands of products

hundreds of competitors

nowadays: specialized companies

mostly manual, sampling

large cost

18

Web Data Extraction › Scenarios

Scenario ➂: Hotel Agency

online travel agency

best price guarantee

prices of competing agencies

average market price

19

taken and report history


Scenario ➃: Hedge Fund

house price index

published in regular intervals by national statistics agency

affects share values of various industries

hedge fund:

online market intelligence to predict the house price index

20


tenders from all over the world

existing aggregators

expensive, often incomplete

yet need to be published (online) by law in most countries

Scenario ➄: Construction

21


… and the Semantic Web

22



22


33453 OX2 6AR 3 2 15/10/2013 £1280 pcm

33433 OX4 7DG 2 1 18/04/2013 £995 pcm



22


33453 OX2 6AR 3 2 15/10/2013 £1280 pcm

33433 OX4 7DG 2 1 18/04/2013 £995 pcm



22


33453 OX2 6AR 3 2 15/10/2013 £1280 pcm

33433 OX4 7DG 2 1 18/04/2013 £995 pcm



22


33453 OX2 6AR 3 2 15/10/2013 £1280 pcm

33433 OX4 7DG 2 1 18/04/2013 £995 pcm

23

Domain database

Whole DomainSingle schemaRich attributes

Goal:

24

Product provider Single agency

Few attributes

24

Product provider Single agency

Few attributes

>15000 in the UK alone

25

Product provider

Semantic API (RDF)

Structured API (XML/JSON)

HTML interface

1template

reverse engineering the DB

25

Product provider

Semantic API (RDF)


HTML interface

1template

reverse engineering the DB

26

Product provider

Semantic API (RDF)


HTML interface

1template

27

Semantic API (RDF)


HTML interface

1template

2

Form filling

28

Semantic API (RDF)


HTML interface

1template

2

Form filling

29

2

Form filling

3

Object identification

30

2

Form filling

3


Energy Performance Chart

Maps

Tables

Flat Text

31

Product provider

Semantic API (RDF)


HTML interface

1template

2

Form filling

3



Maps

Tables

Flat Text

Domain database

Cleaning & integration

4

31

Product provider

Semantic API (RDF)


HTML interface

1template

2

Form filling

3



Maps

Tables

Flat Text

Domain database

Cleaning & integration

4

Other Provider Other

Provider

Other Provider

Other Provider

Oth

er p

rovi

ders

32

DIADEM data extraction methodologydomain-centric intelligent automated

DIADEM ›❯ How

DIADEM: Methods and Examples

ROSeAnn: World-best entity extraction from text (VLDB’13+14)

over 350 entity types disambiguated through knowledge/ontology

33

DIADEM ›❯ How




BERyL: Unique block classification (ICWE’12)

rich feature model; methodology for easy addition of new features

34

ascending_visual_siblings(X) :- numeric(X, ValueX) direct_visual_sibling(X,Y,left), direct_visual_sibling(X,Z,right), numeric(Y, ValueY), numeric(Z, ValueZ), ValueY < ValueX < ValueZ.

Website n n1 n2 P R Screenshot

Rea

lest

ate FindAProperty 370 1 1 1 1

Zoopla 332 1 1 1 1Savills 234 2 2 1 1

Car

s Autotrader 262 2 2 1 1Motors 472 2 2 1 1Autoweb 103 2 2 1 1

Ret

ail Amazon 448 1 1 1 1

Ikea 290 2 0 1 1

Lands’ End 527 2 2 1 1

Foru

ms TechCrunch 279 0 1 1 1

TMZ 200 2 2 1 1Ars Technica 341 2 2 1 1

Table 1: Sample pages

recall). n is the number of links on the result page, n1 (n2) the number of immediatenumeric (non-numeric) pagination links on the page, and P, R are precision and recallfor our approach.1 For each website we also present a screenshot of either its pagina-tion links or a potential false positive. Even in this small sample of webpages, we canobserve the diversity of pagination links: Only six of the twelve websites have a typ-ical pagination link layout (non-numeric link containing a NEXT keyword and a list ofnumeric links with the current page represented as a non-link). Some of the challengesevident from this table are:1. For FindAProperty and IKEA the index of the current page is a link and thus we

need to consider, e.g., its style to distinguish it from the other links.2. For Zoopla the “50” for the results per page can be easily mistaken for an immediate

numeric pagination link.3. For Savills, numeric links come as intervals. However, our NUMBER annotations also

cover numeric ranges (as well as “2k” or “two”).4. For Amazon the result page contains a confusing scrollbar for navigation through

the related products (right screenshot).5. For Lands’ End the non-numeric pagination link is an image. However, our ap-

proach classifies it correctly, based on the context and attribute values.6. TechCrunch contains a single isolated non-numeric pagination link, that we are able

to identify due to the keyword present in its text and the proximity to “Page 1”.7. TMZ has a pagination link that carries both a NEXT and a NUMBER annotation. From

the context, we nevertheless identify it correctly as non-numeric.

1 Precision is the percentage of true positives among the nodes identified as pagination links,recall the percentage of identified pagination links among all pagination links (and thus lowerrecall means more false negatives).

DIADEM ›❯ How






OPAL: World-best form understanding (WWW’12,VLDBJ‘13a)

rich feature model with ontology-based classification

35

labels of the parent of 3 and thus there are two A labels. 4 is notmatched as both A labels are values.

OPAL-TL templates. OPAL-TL extends Datalog¬ (Datalog withstratified negation) by templates to define reusable patterns for do-main concepts. Examples of such patterns are basic classificationpatterns that derive a domain type from a conjunction of annota-tion types or min-max range patterns where we look for multiplefields with related annotations in a group and some clue that theyrepresent a range. There are two types of template patterns, one forclassification constraints, one for structural constraints. The formerspecify patterns for relationships between domain and annotationtypes, the latter the abstract structure of domain concepts,

DEFINITION 12. A OPAL-TL template is an expression of theform TEMPLATE name <D1, . . . ,Dk> { p ( expr } where name is thename of the template, D1, . . . ,Dk are formal template parameters,p a template atom, and expr a conjunction of template atoms andannotation queries. A template atom is an expression of the formp<C1, . . . ,Ck>(X1, . . . ,Xn) where p is a first-order predicate name,X1, . . . ,Xn first-order variables and C1, . . . ,Ck template variables.First-order variables and template variables are disjoint. A tem-plate atom is template ground if all its template variables are val-ued to a constant. A template atom is ground if it is template groundand all its first-order variables are valued to a constant.

Multiple rules with the same head express union as usual. For con-venience, we use _ and ¬ over conjunctions, which are translatedto pure Datalog¬ rules as usual (and with no effect on data com-plexity).

As an example, the following template defines a family of con-straints that associate the domain type D to a node N whenever Nis labeled by an exclusive direct and proper annotation of type A.

TEMPLATE basic_concept <D,A> { concept<D>(N) ( N@A{e,d,l} }

A template tpl is instantiated to produce a family of rules wherethe formal template variables D1, . . . ,Dk are instantiated using val-ues vi

1, . . . ,vik from a template instantiation expression of the form

INSTANTIATE tpl <D1, . . . ,Dk> using { <v11, . . . ,v

1k> . . . <vn

1, . . . ,vnk> }

For example, the following template instantiation expression in-stantiates basic_concept replacing D with type RADIUS and A withannotation type radius:

INSTANTIATE basic_concept <D,A> using {<RADIUS, radius>}

It thus produces the following template ground rule:

concept<radius_node>(N) ( N@RADIUS{e,d,l}

PROPOSITION 1. OPAL-TL has the same data complexity asDatalog¬.

PROOF. After instantiation OPAL-TL rules are translated to Dat-alog with stratified negation and inequality by producing uniquenames for concept<S> predicate names, and expanding _ into mul-tiple rules. Though instantiation can yield a Datalog program ex-ponential in the size of the OPAL-TL specification, data complexityremains unaffected.

5.2 ClassificationClassification is based on the classification constraints of the do-

main schema. In OPAL these constraints are specified using OPAL-TL to enable reuse of domain concept and concept patterns. In the

TEMPLATE basic_concept<C,A> { concept<C>(N) ( N@A{d,e,p} }2

TEMPLATE concept_by_segment<C,A> {4 concept<C>(N) ( N@A{e,p} }

6 TEMPLATE concept_minmax<C,CM,A> {concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

8 N1@A{e,d},(concept<C>(N2) _ N2@A{e,d})concept<CM>(N1)(child(N1,G),child(N2,G),follows(N2,N1),

10 concept<C>(N1),N2@range_connector{e,d},¬(A1 � A, N2@A1{d})concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

12 N1@A{e,p},N2@A{e,p},�(N1@min{e,p},N2@max{e,p})

_ (N1@max{e,p},N2@min{e,p})�

Figure 8: OPAL-TL classification templates

real estate and used car domain, we identify three patterns that suf-fice to describe nearly all classification constraints. These patternseffectively capture very common semantic entities in forms and,in principle, can be parametrized using domain knowledge. Thebuilding blocks are a domain type (or concept) C and an annotationtype A that is used to define a classification constraint for C. Noneof these patterns uses more than one annotation type as template pa-rameter, though many query additional (but fixed) annotation typesin their bodies.

Table 8 shows the OPAL-TL templates for classification constraintsin the real-estate and used car domain

(1) Basic concept. The first template captures direct classifica-tion of a node N with type C, if N matches X@A{d,e,p}, i.e., hasmore proper labels of type A than of any other type A0 with A0 � A.This template is by far the most used, primarily for concepts withunambiguous proper labels.

(2) Concept by segment. The second template relaxes the re-quirement by considering also indirect labels (i.e., labels of theparent segment). In the real estate and used car domains, thistemplate is used primarily for control fields such as ORDER_BY orDISPLAY_METHOD (grid, list, map) where the possible values of the fieldare often misleading (e.g., an ORDER_BY field may contain “price”,“location”, etc. as values).

(3) Min-max concept. Web forms often show pairs of fields rep-resenting min-max values for a feature (e.g., the number of bed-rooms of a property). We specify this pattern using three simplerules (line 6–13), that describe three configurations of groups withelements with only value labels (proper labels are captured by thefirst two templates). It is the only template with two concept tem-plate parameters, C and CM where CM <C is the “minmax” variantof C. The first locates, adjacent pairs of such nodes or a single suchnode and one that is already classified as C. The second rule locatesnodes where the second follows directly the first (already classifiedwith C), has a range_connector (e.g., “from” or “to”), and is not anno-tated with an annotation type with precedence over A. The last rulealso locates adjacent pairs of such nodes and classifies them withCM if they carry a combination of min and max annotations.

In addition to these templates, there is also a small number ofspecific patterns. In the real estate domain, e.g., we use the follow-ing rule to describe forms that use a links for submission (ratherthan submit fields or buttons). Identifying such a link (withoutprobing and analysis of Javascript event handlers) is performedbased on an annotation type for typical content, title (i.e., tooltip),or alt attribute of contained images. This is mostly, but not entirelydomain independent (e.g., in real-estate a “rent” link is a strongcandidate).

Range widget ⟸ two fields + connected by “to” or other range connector+ some clues in the annotations or classifications

DIADEM ›❯ How








OXPath: World-best extraction language (VLDB’11,VLDBJ‘13b)

minimal resource use for cloud extraction; easy to use language

36

Bitemporal Complex Event Processing of

Web Event Advertisements

?

Tim Furche1, Giovanni Grasso1, Michael Huemer2,Christian Schallhart1, and Michael Schrefl2

1 Department of Computer Science, Oxford University,Wolfson Building, Parks Road, Oxford OX1 3QD

[email protected] Department of Business Informatics – Data & Knowledge Engineering,

Johannes Kepler University, Altenberger Str. 69, Linz, [email protected]

doc(’http://www.scottfraser.co.uk/’)//select[@id=’search-type’]/{1 /}2 //input/{click /}/(//div[1]/table//td[4]/a/{click /})*{0,500}

//div[@class=’property-wrapper’]:<record>4 [? .:<ORIGIN_URL=current-url()>]

[? .//div[@class=’propertyPrice’]/text()[last()-1]:<PRICE=normalize-space(.)> ]6 [? .//li[@class=’rec’]/span[@class=’value’]/text():<RECEPTION_ROOM_NUMBER=string(.)> ]

[? .//div[@class=’propertyTitle’]//@href:<URL=string(.)> ]8 [? .//span[@class=’priceQualifier’]/text():<PERIOD_UNIT=string(.)> ]

[? .//div[@class=’propertyDescription’]/text()[1]:<DESCRIPTION=string(.)> ]10 [? .//li[@class=’bed’]/span[@class=’value’]/text():<BEDROOM_NUMBER=string(.)> ]

[? .//li[@class=’bath’]/span[@class=’value’]/text():<BATHROOM_NUMBER=string(.)> ]12 [? .//div[@class=’propertyThumbnail’]/a//@src:<IMAGE=string(.)> ]

[? .//div[@class=’propertyTitleWrapper’]//a/text():<LOCATION=string(.)> ]

doc(’http://www.timruss.co.uk/’)//input[@value=’cntrlListingType_Sales’]/{click /}2 //input[@name=’ctl00$ctl14$btnSearch$ctl00’]/{click /}/

(//div[5]//td/following-sibling::td[contains(string(.),’>’)]/a/{click /})*{0,500}4 //div[@id=’ctl00_cntrlCenterRegion_ctl01_pnlPagingFooter’]/preceding-sibling::div/div[1]/div:<record>

[? .:<ORIGIN_URL=current-url()>]6 [? .//div/following-sibling::h2//text():<PRICE=substring(normalize-space(.),string-length(substring-before(normalize-space(.)," "))+1)> ]

[? .//div[@class=’ListResultsRooms’]/div[last()]/span/text():<RECEPTION_ROOM_NUMBER=substring-after(normalize-space(.),"Receptions: ")> ]8 [? .//a[.=’Full Details >’]/@href:<URL=string(.)> ]

[? .//div[contains(@class,’SearchText’)]:<DESCRIPTION=string(.)> ]10 [? .//div[contains(string(.),’Bedrooms:’)]/span/text():<BEDROOM_NUMBER=substring-after(normalize-space(.),"Bedrooms: ")> ]

[? .//div[contains(string(.),’Bathrooms:’)]/span/text():<BATHROOM_NUMBER=substring-after(normalize-space(.),"Bathrooms: ")> ]12 [? .//a[@class=’propAdd’]/text():<TOWN=string(.)> ]

[? .//img[@class=’fulldetails-photo-item’]/@src:<IMAGE=string(.)> ]14 [? .//a[@class=’propAdd’]/text():<LOCATION=string(.)> ]

? The research leading to these results has received funding from the European Research Councilunder the European Community’s Seventh Framework Programme (FP7/2007–2013) / ERCgrant agreement DIADEM, no. 246858. Michael Huemer has been supported by a MariettaBlau Scholarship granted by the Austrian Federal Ministry of Science and Research (BMWF)for a research stay at Oxford University’s Department of Computer Science.

DIADEM ›❯ How








OXPath: World-best extraction language (VLDB’11,VLDBJ‘13b)

minimal resource use for cloud extraction; easy to use language

World-first fully automatic, full domain extraction system

over 5000 sites in UK real-estate

37

DIADEM ›❯ How

Core Insight: Phenomenology

Monochromatic Rectangle

Geographic

search facility

Postcode Active map ….

ISA ISA

Occurs in

Price

search facility ….

….

Occurs in

….

Geo-Price Searchbox

ISA

38

Web Object Ontology (domain-parameterized)

DIADEM ›❯ How

Property SearchFacility

Property List

Single Property Description

Featuredproperty

part-of

39


Monochromatic Rectangle

Geographicsearch facility

Postcode Active map ….

ISA ISA

Occurs in

Price search facility

….

….

Occurs in

….

Geo-Price Searchbox

ISA

DIADEM ›❯ How 40


implements Property SearchFacility

Property List

Single Property Description

Featuredproperty

part-of

DIADEM ›❯ How

Object creation in Datalog+

41

PRODUCTToshiba Protégé cxDell 25416 Dell 23233Acer 78987

PRICE480360 470390

table(T1) & table(T2) & sameColor(T1,T2) &isNeighbourRight(T1,T2) ⟹ "∃ X (tablebox(X) &

" " contains(X,T1) & " " contains(X,T2)).

DIADEM ›❯ How


42




PRICE480360 470390

T1 T2

DIADEM ›❯ How


43




PRICE480360 470390

T1 T2

DIADEM ›❯ How


44



Deduction in Datalog+ undecidable (TGDs)

DIADEM ›❯ How


45



Deduction in Datalog+ undecidable (TGDs)

Datalog± : require guardedness of rule bodies. Decidable, linear-time data complexity.


DIADEM Architecture

OPAL

Form filling & understanding

AMBER

Object identification & alignment

BERyL

Block analysis & object enrichment

OXPath

Efficient extraction in the cloud

GLUEExploration control and integration language

47

DEMO


DIADEM: Statistics

48

sites facts modules sequential time

avg. sequential

Rightmove.co.uk 1 < 1M 1098 12 mins —

Oxfordshire 172 98M 127k 1 day < 10 mins

UK RE (capped) 5000 almost 3B 4M 43 days 10 mins

49

per$Task$ per$Page$ per$Site$ TOTAL$Sec$ 3.19$ 50.40$ 336.30$ 60534.44$Min$ 0.05$ 0.84$ 5.61$ 1008.91$

1.00$

10.00$

100.00$

1000.00$

10000.00$

1.00$

10.00$

100.00$

1000.00$

10000.00$

100000.00$

Time%per%…%

50

1.00$ 0.98$ 0.98$

0.36$

1.00$

0.38$

0.20$

0.44$

0.26$

0.98$

0.46$0.42$

0.72$

0.20$0.16$

0.04$

0.30$

0.04$0.00$

0.10$

0.20$

0.30$

0.40$

0.50$

0.60$

0.70$

0.80$

0.90$

1.00$

price$

loca5on$

url$

postcode$

descrip5on$

street_address$

city$

town$

county$

image$

property_type$

property_status$

bedroom_number$

bathroom_number$

recep5on_room_number$

furnishing$

period_unit$

branch_loca5on$

Average'a(ributes'per'record'

51

Avg$#$Ac'ons$ Avg$#$Fillings$ Avg$#$Filled$Text$All$ 2.61$ 0.44$ 0.03$form$ 11.20$ 3.34$ 0.21$result$ 1.73$ 0.00$ 0.00$

0.00$

2.00$

4.00$

6.00$

8.00$

10.00$

12.00$

52



?




















53



?






















?





















DIADEM Architecture

OPAL


AMBER


BERyL


OXPath




DIADEM Architecture

OPAL


AMBER


BERyL


OXPath



DIADEM ›❯ OPAL

Navigation in DIADEM: OPAL

56

OPAL is DIADEM’s novel framework for

form and interface understanding and

form and interface navigation

previously navigation mostly

crawler-like: navigate all facets of an interface

probing-based: attempts many “blind” submissions

wide applicability beyond data extraction

meta search; automation; assisted/mobile interfaces

DIADEM ›❯ OPAL


56









Furche, Gottlob, Grasso, Guo, Orsi, Schallhart, OPAL: Automated form understanding for the deep web. WWW 2012

DIADEM ›❯ OPAL


56









DIADEM ›❯ OPAL


56








meta search; automation; assisted/mobile interfacesFurche, Grasso, Guo, Orsi, Schallhart, The Ontological Key: Automatically Understanding and Integrating Forms to Access the Deep Web. VLDB Journal 2013

DIADEM ›❯ OPAL


56









DIADEM ›❯ OPAL

Ontological: Constraints for real estate forms

Annotation schema: Λ=(A,<,≺,(isLabela, isValuea: a ∈ A))

set A of annotation types

a transitive, reflexive subclass relation <

a transitive, irreflexive, antisymmetric precedence relation ≺

and two characteristic functions isLabela and isValuea on text nodes for each a ∈ A.

Domain schema: Σ = (Λ,T,CT ,CΛ)

annotation schema Λset of domain types T

CT, CΛ: map domain types to classification & structural constraints

57

DIADEM ›❯ OPAL 58

Location Location Location

Location

Location

Geographic

Area/BranchBuy/Rent

Buy/Rent

Buy/Rent Type of Use

Local NationalLocation/…

RentingBuyingOfficeAll Residential Commercial

Min. BedroomsAny

Price Range (£)0

to700 Submit

Type of Use

Type of Use

Bedroom

Features

Price

Min-Price Max-Price Button

Buy/Rent Form

Real-Estate Form

OPAL Classification over Sample Form

59

labels of the parent of 3 and thus there are two A labels. 4 is notmatched as both A labels are values.

OPAL-TL templates. OPAL-TL extends Datalog¬ (Datalog withstratified negation) by templates to define reusable patterns for do-main concepts. Examples of such patterns are basic classificationpatterns that derive a domain type from a conjunction of annota-tion types or min-max range patterns where we look for multiplefields with related annotations in a group and some clue that theyrepresent a range. There are two types of template patterns, one forclassification constraints, one for structural constraints. The formerspecify patterns for relationships between domain and annotationtypes, the latter the abstract structure of domain concepts,

DEFINITION 12. A OPAL-TL template is an expression of theform TEMPLATE name <D1, . . . ,Dk> { p ( expr } where name is thename of the template, D1, . . . ,Dk are formal template parameters,p a template atom, and expr a conjunction of template atoms andannotation queries. A template atom is an expression of the formp<C1, . . . ,Ck>(X1, . . . ,Xn) where p is a first-order predicate name,X1, . . . ,Xn first-order variables and C1, . . . ,Ck template variables.First-order variables and template variables are disjoint. A tem-plate atom is template ground if all its template variables are val-ued to a constant. A template atom is ground if it is template groundand all its first-order variables are valued to a constant.

Multiple rules with the same head express union as usual. For con-venience, we use _ and ¬ over conjunctions, which are translatedto pure Datalog¬ rules as usual (and with no effect on data com-plexity).


TEMPLATE basic_concept <D,A> { concept<D>(N) ( N@A{e,d,l} }



INSTANTIATE tpl <D1, . . . ,Dk> using { <v11, . . . ,v

1k> . . . <vn

1, . . . ,vnk> }

For example, the following template instantiation expression in-stantiates basic_concept replacing D with type RADIUS and A withannotation type radius:

INSTANTIATE basic_concept <D,A> using {<RADIUS, radius>}

It thus produces the following template ground rule:

concept<radius_node>(N) ( N@RADIUS{e,d,l}

PROPOSITION 1. OPAL-TL has the same data complexity asDatalog¬.

PROOF. After instantiation OPAL-TL rules are translated to Dat-alog with stratified negation and inequality by producing uniquenames for concept<S> predicate names, and expanding _ into mul-tiple rules. Though instantiation can yield a Datalog program ex-ponential in the size of the OPAL-TL specification, data complexityremains unaffected.


main schema. In OPAL these constraints are specified using OPAL-TL to enable reuse of domain concept and concept patterns. In the

TEMPLATE basic_concept<C,A> { concept<C>(N) ( N@A{d,e,p} }2

TEMPLATE concept_by_segment<C,A> {4 concept<C>(N) ( N@A{e,p} }

6 TEMPLATE concept_minmax<C,CM,A> {concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

8 N1@A{e,d},(concept<C>(N2) _ N2@A{e,d})concept<CM>(N1)(child(N1,G),child(N2,G),follows(N2,N1),

10 concept<C>(N1),N2@range_connector{e,d},¬(A1 � A, N2@A1{d})concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

12 N1@A{e,p},N2@A{e,p},�(N1@min{e,p},N2@max{e,p})

_ (N1@max{e,p},N2@min{e,p})�


real estate and used car domain, we identify three patterns that suf-fice to describe nearly all classification constraints. These patternseffectively capture very common semantic entities in forms and,in principle, can be parametrized using domain knowledge. Thebuilding blocks are a domain type (or concept) C and an annotationtype A that is used to define a classification constraint for C. Noneof these patterns uses more than one annotation type as template pa-rameter, though many query additional (but fixed) annotation typesin their bodies.

Table 8 shows the OPAL-TL templates for classification constraintsin the real-estate and used car domain

(1) Basic concept. The first template captures direct classifica-tion of a node N with type C, if N matches X@A{d,e,p}, i.e., hasmore proper labels of type A than of any other type A0 with A0 � A.This template is by far the most used, primarily for concepts withunambiguous proper labels.

(2) Concept by segment. The second template relaxes the re-quirement by considering also indirect labels (i.e., labels of theparent segment). In the real estate and used car domains, thistemplate is used primarily for control fields such as ORDER_BY orDISPLAY_METHOD (grid, list, map) where the possible values of the fieldare often misleading (e.g., an ORDER_BY field may contain “price”,“location”, etc. as values).

(3) Min-max concept. Web forms often show pairs of fields rep-resenting min-max values for a feature (e.g., the number of bed-rooms of a property). We specify this pattern using three simplerules (line 6–13), that describe three configurations of groups withelements with only value labels (proper labels are captured by thefirst two templates). It is the only template with two concept tem-plate parameters, C and CM where CM <C is the “minmax” variantof C. The first locates, adjacent pairs of such nodes or a single suchnode and one that is already classified as C. The second rule locatesnodes where the second follows directly the first (already classifiedwith C), has a range_connector (e.g., “from” or “to”), and is not anno-tated with an annotation type with precedence over A. The last rulealso locates adjacent pairs of such nodes and classifies them withCM if they carry a combination of min and max annotations.

In addition to these templates, there is also a small number ofspecific patterns. In the real estate domain, e.g., we use the follow-ing rule to describe forms that use a links for submission (ratherthan submit fields or buttons). Identifying such a link (withoutprobing and analysis of Javascript event handlers) is performedbased on an annotation type for typical content, title (i.e., tooltip),or alt attribute of contained images. This is mostly, but not entirelydomain independent (e.g., in real-estate a “rent” link is a strongcandidate).

A A

AA

B

B

C

3

42

1

Figure 6: Example Form Labeling

are either provided by human domain experts or derived from ex-ternal sources such as DBPedia and Freebase. The current OPALversion contains a large set of such artefacts for common domaintypes such as price, location, or date.

DEFINITION 11. Given a form labeling F on a DOM P and anannotation schema L, an OPAL-TL annotation query is an expres-sion of the form: X@A{d, p,e} where X is a first-order variable,A 2 A, and d, p, and e are annotation modifiers. An annotationquery X@Aµ with µ ✓ {d, p,e} holds for all X 2 JAµ K with

J@Aµ K = {n 2 P : Allowµ (n)\Matchµ (A) 6= /0}\Blockµ (A)

with Allowµ (n) set to y(n) for d 2 µ , and y(n)[y(parent of n)otherwise. Matchµ (A) is to {l :

SA0<⇤A isLabelA0(l)} for p 2 µ , and

{l :S

A0<⇤A(isLabelA0(l)_ isValueA0(l))} otherwise. Blockµ (A) equals{n : 9A0 �A, |Matchµ (A)|< |Matchµ (A0)|} if e2 µ , and /0 otherwise.

Intuitively, an annotation query X@A returns all nodes labeledwith a label that is annotated with A. If the modifier d (direct) isnot present, we also consider the (direct) segment parents, other-wise only direct labels are considered. If the modifier p (proper) ispresent, only isLabelA is used, otherwise also isValueA. If the modi-fier e (exclusive) is present, a node that fullfils all other conditionsis still not returned, if there are more labels with annotations of atype that has precedence over A.

Consider the form labeling of Figure 6 under a schema withC < B and B � A. Labels are denoted with triangles, fields withdiamonds, segments with circles. Labels are further annotated withmatching annotation types (here always only one). If value labelsare drawn as outlines. Then, X@A{} matches 2,3,4; X@A{e,d}matches 2,4, but not 3 as 3 has more labels of B (or one of its sub-classes) than of A and the exclusive modifier e is present; X@A{e, p}matches 2,3, but not 4 as the proper modifier p prevents the valuelabels in white to be considered. The latter matches 3 despite thepresence of e, as we consider also the labels of the parent of 3 (sincethe direct modifier d is absent) and thus there are two A labels.

OPAL-TL templates. OPAL-TL extends Datalog¬ (Datalog withstratified negation) by templates to define reusable patterns for do-main concepts. Examples of such patterns are basic classificationpatterns that derive a domain type from a conjunction of annotationtypes or min-max range patterns where we look for multiple fieldswith related annotations in a group and some clue that they repre-sent a range. In general, there are two types of template patterns,one for classification constraints, one for structural constraints. Theformer specify patterns for relationships between domain and an-notation types, the latter the abstract structure of domain concepts.

DEFINITION 12. An OPAL-TL template is an expressionTEMPLATE N<D1, . . . ,Dk> { p ( expr } where N names the template,D1, . . . ,Dk are template parameters, p is a template atom, expra conjunction of template atoms and annotation queries. A tem-plate atom p<C1, . . . ,Ck>(X1, . . . ,Xn) consists of first-order predi-cate name p, template variables C1, . . . ,Ck, and first-order vari-ables X1, . . . ,Xn.

Multiple rules with the same head express union as usual. For con-venience, we use _ and ¬ over conjunctions, which are translatedto pure Datalog¬ rules as usual (not effecting data complexity).

TEMPLATE basic_concept<C,A> { concept<C>(N)(N@A{d,e,p} }2

TEMPLATE concept_by_segment<C,A> { concept<C>(N)(N@A{e,p} }4

TEMPLATE concept_minmax<C,CM,A> {6 concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

N1@A{e,d},(concept<C>(N2) _ N2@A{e,d})8 concept<CM>(N2)(child(N1,G),child(N2,G),follows(N2,N1),

concept<C>(N1),N2@range_connector{e,d},¬(A1 � A, N2@A1{d})10 concept<CM>(N1)(child(N1,G),child(N2,G),adjacent(N1,N2),

N1@A{e,p},N2@A{e,p},�(N1@min{e,p},N2@max{e,p})

12 _ (N1@max{e,p},N2@min{e,p})�



TEMPLATE basic_concept<D,A> { concept<D>(N) ( N@A{e,d,l} }



INSTANTIATE tpl<D1, . . . ,Dk> using { <v11, . . . ,v

1k> . . . <vn

1, . . . ,vnk> }

For example, the following expression instantiates basic_conceptreplacing D with type RADIUS and A with annotation type radius

INSTANTIATE basic_concept<D,A> using {<RADIUS, radius>}

and produces the following instantiated rule:

concept<RADIUS>(N)(N@radius{e,d,l}

PROP. 1. OPAL-TL has the same data complexity as Datalog¬.


main schema. In OPAL these constraints are specified using OPAL-TL to enable reuse of domain concepts and concept patterns. In thereal estate and used car domains, we identify three patterns that suf-fice to describe nearly all classification constraints. These patternseffectively capture very common semantic entities in forms and areparametrized using domain knowledge. The building blocks are adomain type (or concept) C and an annotation type A that is used todefine a classification constraint for C. None of these patterns usesmore than one annotation type as template parameter, though manyquery additional (but fixed) annotation types in their bodies.

Figure 7 shows the classification templates for real-estate andused car: (1) Basic concept. The first template captures direct clas-sification of a node N with type C, if N matches X@A{d,e,p}, i.e.,has more proper labels of type A than of any other type A0 withA0 � A. This template is used by far most frequently, primarily forconcepts with unambiguous proper labels. (2) Concept by segment.The second template relaxes the requirement by considering alsoindirect labels (i.e., labels of the parent segment). In the real estateand used car domains, this template is instantiated primarily forcontrol fields such as ORDER_BY or DISPLAY_METHOD (grid, list, map)where the possible values of the field are often misleading (e.g.,an ORDER_BY field may contain “price”, “location”, etc. as values).(3) Min-max concept. Web forms often show pairs of fields repre-senting min-max values for a feature (e.g., the number of bedroomsof a property). We specify this pattern with three simple rules (line5–12), that describe three configurations of segments with fields as-sociated with value labels only (proper labels are captured by the

Precision Recall F-score

0.94

0.955

0.97

0.985

1

UK Real Estate (100) UK Used Car (100) ICQ (98) Tel-8 (436)


0.94

0.955

0.97

0.985

1


Su et al., TWeb, 2012with training


0.94

0.955

0.97

0.985

1


0.9

0.92

0.94

0.96

0.98

1

Airfare Auto Book Job US R.E.



0.94

0.955

0.97

0.985

1


0.9

0.92

0.94

0.96

0.98

1

Airfare Auto Book Job US R.E.

Dragut et al., VLDB, 2009


DIADEM ›❯ Inside61

Real-estate

Used-car

0.6 0.7 0.8 0.9 1

field segment layout domain

Contribution of Scopes

DIADEM ›❯ Inside

Phenomenology: Datalog±

Infer a new form segment if

there is a group of fields (G) that is not yet classified

and has at least two children (N1, N2) of type C

Add all children of G of type C to the new segment

62

candidate-segment<C>(∃ X, G) :- ¬segment(G), child(N1, G), child(N2, G), concept<C>(N1), concept<C>(N2). child(X, N) :- candidate-segment<C>(X, G), child(N, G), concept<C>(N, G). segment<C>(X) :- candidate-segment<C>(X, _).


DIADEM Architecture

OPAL


AMBER


BERyL


OXPath



64

D1

M1,1

M1,2

D2

…

D3

…

M1,3 E

M1,4

Figure 3: Data area identification

its of order dominance: The pivot nodes in E are organized ratherregularly, whereas the pivot nodes in D1 vary quite notably. How-ever, there variation is small enough that M1,1 to M1,4 are depth anddistance consistent (for d = e = 3). The two lower pivot nodes inE however are neither depth (due to M1,1) nor distance consistent(due to M1,2 and M1,3) and therefore can not be added to this clus-ter. They form a separate cluster together with the rightmost pivotnode in E. This cluster, however, is not order dominant and there-fore dropped in lines 24� 28. Thus, y(D1), the support of D1, isonly {M1,1, . . . ,M1,4} and the three remaining pivot nodes in E arenot used further.

The latter shows that in some cases order dominance may notidentify the “best” data area. The primary reason is that depth anddistance consistence are defined using absolute thresholds for theentire page, rather than allowing data areas with different levelsof consistency on a page. Pages with such a structure occur veryinfrequently in practice (as demonstrated by the evaluation in Sec-tion 5) and could be addressed by a slight extension of the currentidentification algorithm (see Section 6).

4.2 Record SegmentationAMBER is tailored to result pages with multiple “records”, i.e.,

representations of domain entities. During the data area identifica-tion, we identify areas of a page with sufficient repeated structurein the relevant data that we can assume that records in such a dataarea are instantiations of the same template and thus have a similarstructure. Despite this assumption AMBER can deal with a largedegree of noise: (1) AMBER tolerates inter-record noise, such asadvertisements, by focusing on relevant data. (2) AMBER toler-ates most intra-record variances due to, e.g., optional attributes ormultiple entity types by segmenting records based only on manda-tory, usually highly regular attributes. (3) AMBER also addressesmulti-template pages, where records on the same page are gener-ated from different templates by considering each data area sepa-rately for record segmentation. AMBER approximates relevant dataand structural similarity of records through occurrences of manda-tory attribute types only, as in the data area case. This allows AM-BER to scale to large and complex pages at ease.

DEFINITION 7. A record is a set r of children of a data aread such that r is continuous for � and r contains at least one pivotnode from y(d). A record segmentation of d is a set of uniform,non-overlapping records R, i.e., all records in R have the samesize and no child of d occurs in more than one record.

For example generation, we are interested in record segmenta-tions that expose the regular structure of the page. We formalizethis as the following dual objective optimization problem:

(1) Maximize the length of an evenly segmented sequence of pivotnodes. A sequence of pivot nodes p1, . . . , pn is evenly seg-mented in a data area d, if the subtrees containing the pi oc-cur in distinct records and all have the same distance from eachother, i.e., if there is a k such that li �sibl li+1 = k for all i whereli is the child of the data area d that contains pi.

(2) Minimize the irregularity of the record segmentation. Theirregularity of a record segmentation R is the sumof the relative tree edit distances between all pairsof nodes in different records in R, irregularity(R) =Ân2r,n02r0with r 6=r02R editDist(n,n0) where editDist(n,n0) is thestandard tree edit distance normalized by the size of the sub-trees rooted at n and n0 (their “maximum” edit distance).

In AMBER we approximate such a record segmentation using Al-gorithm 2. It computes a record segmentation in two steps such thatthe record segmentation contains a large sequence of evenly seg-mented pivot nodes and has minimal irregularity among all recordsegmentations with those pivot nodes and same record size. Ina pre-processing step all children of the data area that contain notext or attributes (“empty” nodes) are collapsed and excluded fromthe further discussion under the assumption that these are separatenodes such as br.

First, we determine the sequence of pivot nodes underlying thesegmentation. We identify the pivot nodes by their “leading node”,i.e., the child of the data area that contains the pivot node (line 1, L).In lines 3� 4 we estimate the distance Len between leading nodesthat yields the largest evenly segmented sequence: The children ofthe data area are partitioned at each leading node and Len becomesthe minimum partition size that occurs with maximal frequency inthe resulting partition (line 4). In lines 5� 8 we drop all leadingnodes from L that are less than Len from their previous leadingnode, except for the start (line 5) and end (line 6) of the sequence,where we remove the outer leading nodes under the assumption thatthey are noise in the header or trailer of the data area.

Second, we use the remaining leading nodes to compute all seg-mentations with record size Len such that each record contains atleast one leading node from L. To that end, line 9 compute thestart points of these records by shifting to the left from the nodesin L. We then iterate over all the sequences of such start pointsin the loop of line 12� 18 and compute the actual segmentationsas the records of Len length from each starting point (line 14). Byconstruction these are records, as they are continuous and containat least one leading node (and thus at least one pivot node). Thewhole Segmentation is a record segmentation as its record are non-overlapping (due to lines 5� 8) and of uniform size Len (line 15).Among all these record segmentations we then return the one withthe lowest irregularity (lines 15�18).

PROPOSITION 1. Algorithm 2 runs in O(b ·n3) on a data aread where b is the degree of D and n the size of d.

PROOF. Lines 1� 8 are clearly in O(b2). Line 9 generates atmost b + 1 segmentations (as Len b) of at most b size. Theloop is executed once for each such segmentation and dominatedby the computation of irregularity() which is bounded by O(n3) us-ing a standard tree edit distance algorithm. Since b n, the overallbound is O(b ·n3).

In Figure 2, the record segmentation is fairly straightforwardsince both data areas are rather regular. We eliminate the sepa-rator nodes (the white diamonds) and then segment the children ofthe data areas. The first f of the e data area is omitted as it does notform a record of size 2 as all others in e.

consistent_cluster_members(C, N1, N2, N3) :- pivot(N1), pivot(N2), ... similar_depth(N1, N2), similar_depth(N2, N3), similar_depth(N1,N3), similar_tree_distance(N1, N2, N3).cluster(C,N) :- continuous, lca, contains at least one of all mandatories

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precision recall

Real Estate(100 sites)

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price postcode location bathroom bedroom reception legal type

precision recall

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precision recall

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Used Car(100 sites)


90

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price postcode location bathroom bedroom reception legal type

precision recall

66

18 Tim Furche et al.

0%

20%

40%

60%

80%

100%

price location

detailed page bedroom

legal status postcode

property type bathroom

reception

250 pages, manual 2215 pages, automatic

Fig. 21: Attribute Frequencies in Large Scale Extraction

Sheet1

Page 1

ε Data Areas Records

abc

98.2% 99.0%99.6% 99.6%98.2% 99.2%

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Data Areas Records

(0,0) (1,2) (2,4)

Fig. 22: Depth/Distance Thresholds (Q depth,Q dist)

Sheet1

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precision recall F1AMBER 99.4% 98.7% 99.2%RR (!) 48.3% 59.7% 53.4%RR (=) 36.7% 45.3% 40.5%MDR 56.5% 72.0% 63.3%AMBER 99.6% 98.9% 99.2%RR (!) 42.5% 65.1% 51.4%RR (=) 30.5% 46.7% 36.9%MDR 38.0% 48.0% 42.4%

Contains means that the attribute extracted by RR contains a groundtruth attributeContains means that the attribute extracted by RR contains a groundtruth attributeContains means that the attribute extracted by RR contains a groundtruth attributeContains means that the attribute extracted by RR contains a groundtruth attributeContains means that the attribute extracted by RR contains a groundtruth attributeContains means that the attribute extracted by RR contains a groundtruth attributeexactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.exactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.exactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.exactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.exactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.exactly the same means that the attribute extracted by RR is exactly the same with one groundtruth attributes.

25%

50%

75%

100%

AMBER RR (!) RR (=) MDR AMBER RR (!) RR (=) MDR

precision recall

Real-Estate Used Car

Fig. 23: Comparison with ROADRUNNER and MDR

repeated occurrences of variable data (“slots” of the un-derlying page template) and therefore extracts too manyattributes. For example, ROADRUNNER extracts on somepages more than 300 attributes, mostly URLs and elementsin menu structures, where our gold standard contains only90 actual attributes. To avoid biasing the evaluation againstROADRUNNER, we filter the output of ROADRUNNER, byremoving the description block, duplicate URLs, and at-tributes not contained in the gold standard, such as page ortelephone numbers.

Another issue in comparing AMBER with ROAD-RUNNER is that ROADRUNNER only extracts entire textnodes. For example, ROADRUNNER might extract “Price£114,995”, while AMBER would produce “£114,995”.Therefore we evaluate ROADRUNNER in two ways, once

counting an attribute as correctly extracted if the gold stan-dard value is contained in one of the attributes extractedby ROADRUNNER (RR ⇡ in Figure 23), and once count-ing an attribute only as correctly extracted if the strings ex-actly match (RR = in Figure 23). Finally, as ROADRUN-NER works better with more than one result page from thesame site, we exclude sites with a single result page fromthis comparison. The results are shown in Figure 23. AM-BER outperforms ROADRUNNER by a wide margin, whichreaches only 49% in precision and 66% in recall comparedto almost perfect scores for AMBER. As expected, recall ishigher than precision in ROADRUNNER.

Comparison with MDR. We further evaluate AMBER withMDR, an automatic system for mining data records in webpages. MDR is able to recognize data areas and records,but unlike AMBER, not attributes. Therefore in our com-parison we only consider precision and recall for data areasand records in both real estate and used cars domains. Alsofor the comparison with ROADRUNNER, we avoid biasingthe evaluation against MDR filtering out page portions e.g.,menu, footer, pagination links, whose regularity in structuremisleads MDR. Indeed, these are recognized by MDR asdata areas or records. Figure 23 illustrates the results. Inall cases, AMBER outperforms MDR which on used-carsreports 57% in precision and 72% in recall as best perfor-mance. MDR suffers the complex structure of data records,which may contain optional information as nested repeatedstructure. This, in turn, are often (wrongly) recognized byMDR as record (data area).

6.4 AMBER Learning

The evaluation of AMBER’s learning capabilities is donewith respect to the upfront learning mode discussed in Sec-tion 4. In particular, we want to evaluate AMBER’s abilityof constructing an accurate and complete gazetteer for anattribute type from an incomplete and noisy seed gazetteer.We show that at each learning iteration (see Algorithm 5 inSection 4) the accuracy of the gazetteer is significantly im-proved, and that the learning process converges to a stablegazetteer after few iterations, even in the case of attributetypes with large and/or irregular value distributions in theirdomains.

Setting. In the evaluation that follows we show AMBER’slearning behaviour on the LOCATION attribute type. In oursetting, the term location refers to formal geographical lo-cations such as towns, counties and regions, e.g., “Oxford”,“Hampshire”, and “Midlands”. Also, it is often the casethat the value for an attribute type consists of multiple andsomehow structured terms, e.g., “The Old Barn, St. ThomasStreet - Oxford”. The choice of LOCATION as target for the


DIADEM Architecture

OPAL


AMBER


BERyL


OXPath




Observational Knowledge

comes in three forms

GATE Gazetteer lists

JAPE rules (roughly EBNF + constraints)

domain-independent classifiers

to recognise blocks: advertisements, pagination links, etc.

for attribute and entity extraction

Datalog¬,Agg rules for feature extraction and cleaning

68

housetown housetownhouse

corner houseflat

apartmentmaisonette

cottageconverted barnbarn conversion

conversionmews house

mewsfarmhouse

farmpenthouseresidence

lodgeparking spacecoach house

bungalowdevelopment

villaresidence

former rectoryformer vicarage

chalet

Property type

<money> ::= <currency> <numeric_value><rental.price> ::= <money> <rental.period> | <money> where money.value < rental.price.max

Rental price










68


corner houseflat

apartmentmaisonette



mewsfarmhouse



bungalowdevelopment

villaresidence


chalet

Property type


Rental price

Aim: Nearly automatic acquisition of such knowledge










68


corner houseflat

apartmentmaisonette



mewsfarmhouse



bungalowdevelopment

villaresidence


chalet

Property type


Rental price


Furche, Grasso, Kravchenko and Schallhart. Turn the Page: Automated Traversal of Paginated Websites. In Intl Conf. on Web Engineering (ICWE). 2012










68


corner houseflat

apartmentmaisonette



mewsfarmhouse



bungalowdevelopment

villaresidence


chalet

Property type


Rental price



Observational Knowledge: Block

69

ascending_visual_siblings(X) :- numeric(X, ValueX) direct_visual_sibling(X,Y,left), direct_visual_sibling(X,Z,right), numeric(Y, ValueY), numeric(Z, ValueZ), ValueY < ValueX < ValueZ.

Siblings in ascending order

Fig. 1: Numeric (1, 3�14) and non-numeric (‹ and ›)

neighborhood of links just as well, but although relatively sophisticated, such fea-tures fail to contribute significantly towards high accuracy results, either alone orcombined with content or structural features, as discussed in Section 7. CS: can we

give an example where some seemingly good heuristics breaks down? In the best case, we would use a

heuristic which has been employed by the other approaches.

4. Page position features: Pagination links usually appear on top or below the pagi-nated information. Thus, a link’s relative position on a page or whether it occurs onthe first screen (at a typical resolution) might seem to constitute a promising fea-ture. Unfortunately, advertisement or navigation headers and footers easily affectthese features significantly (and reliably recognizing those is anything but easy).For simple features, Section 7 again shows that neither alone nor combined witheither content or structural features high accuracy is achieved. CS: can we give an ex-

ample where some seemingly good heuristics breaks down? Has this been used by other approaches? If

so, can we give an example from their heuristics and show it fail? If no, why not?

Rename: local visual -> page position, global visual -> neighborhood, (second global visual -> structural)

Fortunately, BERyL makes it very easy to extract a large set of features throughdeclarative (Datalog) extraction rules. On the extracted feature model, we employ stan-dard machine learning techniques for automated feature selection and classification.With this combination, we achieve near perfect accuracy for identifying paginationlinks, yet remain comparable in performance to other block classification methods thatincorporate visual features: All these approaches are dominated in performance by theunderlying page rendering, which is necessary to extract the visual features and whichbecomes unavoidable even for content and structural features, as scripted pages reshapethe web today. Nevertheless, we identify pagination links on most pages within onesecond. Furthermore, this is by far offset by the fact that a high-accuracy identificationof pagination links avoids following many irrelevant links without missing any relevantdata. Achieve and verify 1 sec

block classification:

trade-off between precision, recall, and speed

different block types require different trade-off

flexible framework for block classification: BERyL


BERyL: Navigation Blocks

70

Website n n1 n2 P R Screenshot

Rea

lest

ate FindAProperty 370 1 1 1 1

Zoopla 332 1 1 1 1Savills 234 2 2 1 1

Car

s Autotrader 262 2 2 1 1Motors 472 2 2 1 1Autoweb 103 2 2 1 1

Ret

ail Amazon 448 1 1 1 1

Ikea 290 2 0 1 1

Lands’ End 527 2 2 1 1

Foru

ms TechCrunch 279 0 1 1 1

TMZ 200 2 2 1 1Ars Technica 341 2 2 1 1

Table 1: Sample pages

recall). n is the number of links on the result page, n1 (n2) the number of immediatenumeric (non-numeric) pagination links on the page, and P, R are precision and recallfor our approach.1 For each website we also present a screenshot of either its pagina-tion links or a potential false positive. Even in this small sample of webpages, we canobserve the diversity of pagination links: Only six of the twelve websites have a typ-ical pagination link layout (non-numeric link containing a NEXT keyword and a list ofnumeric links with the current page represented as a non-link). Some of the challengesevident from this table are:1. For FindAProperty and IKEA the index of the current page is a link and thus we

need to consider, e.g., its style to distinguish it from the other links.2. For Zoopla the “50” for the results per page can be easily mistaken for an immediate

numeric pagination link.3. For Savills, numeric links come as intervals. However, our NUMBER annotations also

cover numeric ranges (as well as “2k” or “two”).4. For Amazon the result page contains a confusing scrollbar for navigation through

the related products (right screenshot).5. For Lands’ End the non-numeric pagination link is an image. However, our ap-

proach classifies it correctly, based on the context and attribute values.6. TechCrunch contains a single isolated non-numeric pagination link, that we are able

to identify due to the keyword present in its text and the proximity to “Page 1”.7. TMZ has a pagination link that carries both a NEXT and a NUMBER annotation. From

the context, we nevertheless identify it correctly as non-numeric.

1 Precision is the percentage of true positives among the nodes identified as pagination links,recall the percentage of identified pagination links among all pagination links (and thus lowerrecall means more false negatives).


Phenomenology: Datalog±

Infer a new rectangle if

there are two touching boxes (N1, N2) with

same color and same height (or same width)

no visible border (separator line) between them

no existing box contains only N1 and N2 (omitted here)

Set its dimensions to the MBR for the original boxes

71

box(Y, L, T, R, B) :- mon-rect(Y, L, T, R, B).

∃ X mon-rect(X, L, T, R, B) :- box(N1, L1, T1, R1, B1), box(N2, L2, T2, R2, B2), touches(N1, N2), same-height(N1, N2), same-color(N1, N2), ¬ visible-border-between(N1, N2), ...∃ X mon-rect(X, ... open geospatial consortium

geometric relations


BERyL: Navigation Blocks

feature model: derived from observed facts

through Datalog program with templates

less than two dozen lines of code

72 TEMPLATE annotated_by<Model,AType> {2 <Model>::annotated_by<AType>(X) ( node_of_interest(X),

gate::annotation(X, <AType>, _). }4 TEMPLATE in_proximity<Model,Property(Close)> {

<Model>::in_proximity<Property>(X) ( node_of_interest(X),6 std::proximity(Y,X), <Property(Close)>. }TEMPLATE num_in_proximity<Model,Property(Close)> {

8 <Model>::in_proximity<Property>(X,Num) ( node_of_interest(X),std::proximity(Close,X), Num = #count(N: <Property(Close)>). }

10 TEMPLATE relative_position<Model,Within(Height,Width)> {<Model>::relative_position<Within>(X, (PosH, PosV)) ( node_of_interest(X),

12 css::box(X, LeftX, TopX, _, _), <Within(Height,Width)>,

PosH = 100·LeftXWidth , PosV = 100·TopX

Height . }

14 TEMPLATE contained_in<Model,Container(Left,Top,Bottom,Right)> {<Model>::contained_in<Container>(X) ( node_of_interest(X),

16 css::box(X,LeftX,TopX,RightX,BottomX), <Container(Left,Top,Right,Bottom)>,Left < LeftX < RightX < Right, Top < TopX < BottomX < Bottom. }

18 TEMPLATE closest<Model,Relation(Closest,X),Property(Closest),Test(Closest)> {<Model>::closest<Relation>_with<Property>_is<Test>(X) ( node_of_interest(X),

20 <Relation(Closest,X)>, <Property(Closest)>, <Test(Closest)>,¬(<Relation(Y,X)>, <Property(Y)>, <Relation(Y,Closest)>). }

Fig. 4: BERyL feature templates

In a similar way, the second template defines a boolean feature that holds for nodesof interest, if there is another node in their proximity for which Property(Close) is true.To instantiate it to nodes that are annotated with PAGINATION, we write

INSTANTIATE in_proximity<Model,Property(Close)>2 USING <plm, plm::annotated_by<PAGINATION(Closest)>

Observe, that BERyL templates thus allow for two forms of template parameters: vari-ables and predicates. More formally,

Definition 3. A BERyL template is an expression TEMPLATE N<D1, . . . ,Dk>{p( expr} suchthat N is the template name, D1, . . . ,Dk are template parameters, p is a template atom,expr is a conjunction of template atoms and annotation queries. A template parameteris either a variable or an expression of the shape p(V1, . . . ,Vl) where p is a predicatevariable and V1, . . . , Vn are names of required first order variables in bindings of p.

A template atom p<C1, . . . ,Ck>(X1, . . . ,Xn) consists of a first-order predicate name orpredicate variable p, template variables C1, . . . ,Ck, and first-order variables X1, . . . ,Xn.If p(V1, . . . ,Vl) is a parameter for N, then {V1, . . .Vl}⇢ {X1, . . . ,Xn}.

An instantiation always has to provide bindings for all template parameters. Weextend the usual safety and stratification definitions in the obvious way to a BERyLtemplate program. Then it is easy to see that the rules derived by instantiating a safeand stratified template program are always a safe, stratified Datalog¬,Agg program.

0.95

0.97

0.98

1.00

Real Estate Cars Retail Forums Total

Precision Recall F1


DIADEM Architecture

OPAL


AMBER


BERyL


OXPath



OXPath » The Language

OXPath = XPath + 4

74

action

iteration

extractionstyle


OXPath = XPath + 4

74

action

iteration

extractionstyleFurche, Gottlob, Grasso, Schallhart and Sellers. OXPath: A

Language for Scalable, Memory-efficient Data Extraction from Web Applications. VLDB, 2011

Furche, Gottlob, Grasso, Schallhart, and Sellers. OXPATH: A Language for Scalable Data Extraction, Automation, and Crawling on the Deep Web. In VLDB J. (VLDB 2012 best paper issue) 2013.


OXPath = XPath + 4

74

action

iteration

extractionstyle


OXPath = XPath + 4

74

action

iteration

extractionstyle

Silver price @ “Open Source Software World Challenge 2012”


OXPath = XPath + 4

74

action

iteration

extractionstyle

75

75 Start at kayak.co.uk:

doc("kayak.co.uk")

http://www.rightmove.co.uk



doc("kayak.co.uk")To select an airport, type a few letters and select from completion list

//field().destination/{"Sea" /} //div#smartbox//li[1]/{click /}




doc("kayak.co.uk")To select an airport, type a few letters and select from completion list

//field().destination/{"Sea" /} //div#smartbox//li[1]/{click /}Submit the form



76

76

Refine the results by unchecking the “2+ stops”:

//*#stops2/{uncheck }

76


//*#stops2/{uncheck }On all result pages

/(//a[.=‘Next’]/{click /})*

76


//*#stops2/{uncheck }On all result pages

/(//a[.=‘Next’]/{click /})*and for each flight

//body.resultrow:<flight>

76

77

77

Extract the attributes

77


Mouseover the ! to extract flight quality warnings

//span.qualityWarningIcon/{mouseover /}

77


Mouseover the ! to extract flight quality warnings

//span.qualityWarningIcon/{mouseover /}Click on the details to extract layovers

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OXPathLixto

Web HarvestChickenfoot

(c) Norm. evaluation time, <850 p.

78

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78


DIADEM Architecture

OPAL


AMBER


BERyL


OXPath



DIADEM ›❯ Future

Summary

80

Examples of knowledge (and its representation) in DIADEM

observational: clues for price (“looks like a price”) and location

representation: Gazetteers, JAPE rules, WEKA classifiers & Datalog¬,Agg rules

phenomenological: a real estate record and its attributes

representation: Datalog¬,Agg,± rules

ontological: constraints for real estate form

representation: template language on top of Datalog¬,Agg,± rules

script: strategy for exploring post-form pages

representation: modularised Datalog¬,Agg rules

DIADEM ›❯ Partners

Who wants data from us?

81

Threat detection[Security analytics, London]

Entity extraction in biology[Oxford Martin institute, Oxford]

Financial data extraction[Oxford-Man institute, Oxford]

Forum and blog analysis[Salzburg research, Austria]

DIADEM ›❯ Partners

Collaborations

82

83

83

Lehmann, Furche, Grasso, et al. DEQA: Deep Web Extraction for Question Answering. ISWC 2012.

83

84

Kindergarden_B

White_Road

1,499,950 £

gr :Offering

rdf:type

dd:hasPrice

Kindergarden_Adbp:near

Domain Specific Triple Store

Question:House near a Kindergarden under 2,000,000 £?

OXPath

OXPath

TBSL

White_Road

Answer:

15

dd:bedrooms

1,499,950 £dd:hasPrice

dbp:near Kindergarden_A

Linking-MetricOXPath

Fig. 2: Implementation of deqa for the real-estate domain.

language query to SPARQL, yet can fall back to standard information retrieval,where this fails.

The domain-specific implementation of the conceptual framework, which weused for the real estate domain, is depicted in Figure 2. It covers the abovedescribed steps by employing state-of-the-art tools in the respective areas, OX-Path for data extraction to RDF, Limes for linking to the linked data cloud,and TBSL for translating natural language questions to Sparql queries. In thefollowing, we briefly discuss how each of these challenges are addressed in deqa.

2.1 OXPath for RDF extraction

OXPath is a recently introduced [9] modern wrapper language that combinesease-of-use (through a very small extension of standard XPath and a suite ofvisual tools [14]) with highly efficient data extraction. Here, we illustrate OXPaththrough a sample wrapper shown in Figure 3.

This wrapper directly produces RDF triples, for which we extended OXPathwith RDF extraction markers that generate both data and object propertiesincluding proper type information and object identities. For example the extrac-tion markers <:(gr:Offering> and <gr:includes(dd:House)> in Figure 3 produce –given a suitable page – a set of matches typed as gr:Offering, each with a set ofdd:House children. When this expression is evaluated for RDF output, each pairof such matches generates two RDF instances related by gr:includes and typedas above (i.e., three RDF triples).