Big Data from the Web · 2019-02-13 · ERC Advanced Grant DIADEM + ERC Proof of Concept Grant EXTRALYTICS Founded February 2015 operating initially in Oxford now in London 2 possibilities:

Big Data from the Web

Georg Gottlob, TU Wien & University of Oxford

Reports about joint work with C.Koch, A. Pieris, R. Pichler, R. Baumgartner, M. Herzog, T.Furche, et al.

The WWW is older than we may think...

3

This talk is about extracting data from the Web…

…and about our (ad)ventures in:

Theory

Technology

Commercialization

Business

(Kaplan Turbine)

The Web is the Largest Database

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The Web is the Largest Database

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The Web is not a Database

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• No common schema for data.

• No structured queries possible

• Only keyword search

The Web is not a Database!

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• No common schema for data.

• No structured queries possible

• Only keyword search

For example, the following queries cannot be answered:

- List all flats for sale in Vienna having a balcony that cost

less than €500,000 and that are in an area with

above-average many Italian restaurants.

- List all used cars for sale in or around Oxford that are not

older than 5 years and that cost no more than £6,000.

- Find translation offices in Europe that translate German to Basque,

charge no more than €20 per page, and accept VISA card payments

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

HTML Content Extractor

Function f: HTML Parse tree Subtrees

Leaves of subtrees are among leaves of orig. tree

f

html

body

table

tr

td

tr

td td td td td

Ch

risto

ph

Ko

ch

Geo

rg G

ottlo

b

go

ttlob

@d

ba

i.tuw

ien

.ac

.at

ko

ch

@d

ba

i.tuw

ien

.ac

.at

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0

h1

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">

<html> <body>

<h1>People @ DBAI</h1>

<table border="1" cellpadding="3" cellspacing="1">

<tr> <td>Georg Gottlob</td>

<td>[email protected]</td>

<td>18420</td>

</tr>

<tr> <td>Christoph Koch</td>


<td>18449</td>

</tr>

</table>

</body> </html>

A HTML page

Georg Gottlob gottlob@… 18420

Christoph Koch koch@… 18449

People @ DBAI

Predicate employeetable



People @ DBAI

html

body

table

tr

td

tr

td td td td td

Ch

risto

ph

Ko

ch

Geo

rg G

ottlo

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ttlob

@d

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h1


<html> <body>





<td>18420</td>

</tr>



<td>18449</td>

</tr>

</table>

</body> </html>

Predicate employee



People @ DBAI

html

body

table

tr

td

tr

td td td td td

Ch

risto

ph

Ko

ch

Ge

org

Go

ttlob

go

ttlob

@d

ba

i.tuw

ien

.ac

.at

ko

ch

@d

ba

i.tuw

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.at

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h1


<html> <body>





<td>18420</td>

</tr>



<td>18449</td>

</tr>

</table>

</body> </html>


<html> <body>





<td>18420</td>

</tr>



<td>18449</td>

</tr>

</table>

</body> </html>

Predicate phone



People @ DBAI

html

body

table

tr

td

tr

td td td td td

Ch

risto

ph

Ko

ch

Geo

rg G

ottlo

b

go

ttlob

@d

ba

i.tuw

ien

.ac

.at

ko

ch

@d

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Logic heaven

DB theory heaven

DB programming heaven

Application design heaven

MSO

Monadic Datalog

Elog

Lixto Visual Wrapper

Suite

http://images.google.de/imgres?imgurl=www.aeat.com/netcen/airqual/aqma/cloud.jpg&imgrefurl=http://www.aeat.com/netcen/airqual/aqma/&h=200&w=200&sz=4&tbnid=7pa5ey0NBzIJ:&tbnh=99&tbnw=99&start=5&prev=/images%3Fq%3Dcloud%26hl%3Dde%26lr%3D%26ie%3DUTF-8%26sa%3DG%26edition%3Dde




http://images.google.de/imgres?imgurl=www.wallpaperbase.com/wallpapers/landscape/mountains/mountain08.jpg&imgrefurl=http://www.wallpaperbase.com/landscape-mountain.shtml&h=768&w=1024&sz=163&tbnid=FwkyBW4kRZsJ:&tbnh=112&tbnw=149&start=3&prev=/images%3Fq%3Dlandscape%2Bmountains%26hl%3Dde%26lr%3D%26ie%3DUTF-8%26sa%3DG%26edition%3Dde

Monadic Datalog as a Wrapping Language

html

body

table

tr

td

tr

td td td td td

root

entry(X) :- root(R), firstchild(R,U), label[html](U),

firstchild(U,V), label[body](V),

firstchild(V,W),label[table](W),

firstchild(W,X), label[tr](X).

entry(X):- entry(Y), nextsibling(Y,X).

name(X) :- entry(E), firstchild(E, X), label[td](X).

email(X) :- name(N), nextsibling(N, X), label[td](X).

phone(X) :- email(M), nextsibling(M, X), label[td](X).










html

body

table

tr

td

tr

td td td td td

root










html

body

table

tr

td

tr

td td td td td

root


html

body

table

tr

td

tr

td td td td td

root










html

body

table

tr

td

tr

td td td td td

root


















html

body

table

tr

td

tr

td td td td td

root

How complex is Monadic Datalog?

Monadic Datalog over trees has

combined complexity:

O(|data tree|*|program|)

Theorem [G. & Koch 2002]:

ELOG [Baumgartner, Flesca, G. VLDB’01]

Examples of Special predicates:

subelem(S,X,Path,…)

before(X,Y,….d.)

after(X,Y,…)

property(X,Attribute, Op,Value…..)

Additional features: Stratified negation,

string processing

ontological concepts “phonenumber(X)”

ranges: H(S,X) :- body(……..)[1,5]

object hierarchies

distance tolerance,etc.

Xpath-like expression

document(URL,D)

getdocumentFromHref(X,D),

etc. url

ELOG Program for eBay pages

Lixto Visual Developer (VD)

Navigation

Steps

Mozilla

Web

Browser

Extraction

Configuratio

n

.

.

Comparison of Prices

I INA

A

ThyssenKrupp TKP

http://stat02.ltur.com/corporate/de/s?Homepage.Header.HO&ns_type=clickin&ns_url=http://www.ltur.com/de/index.html

http://images.google.at/imgres?imgurl=http://www.verclas-friends.de/logo_f_it_kl_2005.jpg&imgrefurl=http://www.verclas-friends.de/kunden/freudenberg_it.htm&h=53&w=106&sz=4&tbnid=Htg95rS0RCbQrM:&tbnh=39&tbnw=79&hl=de&start=15&prev=/images%3Fq%3DFreudenberg%2BIT%2BF-IT%26svnum%3D10%26hl%3Dde%26lr%3D

http://www.hotel.de/homepage.aspx?lng=EN

http://images.google.at/imgres?imgurl=http://ceoworld.biz/ceo/wp-content/uploads/2009/07/sap_logo.gif&imgrefurl=http://ceoworld.biz/ceo/tag/sap/&usg=__MRJMeiI6janB7gbYuY8Ve-Hirh4=&h=384&w=739&sz=5&hl=de&start=1&um=1&tbnid=d1zYaXleJAjzYM:&tbnh=73&tbnw=141&prev=/images%3Fq%3DSAP%26hl%3Dde%26sa%3DN%26um%3D1

http://images.google.co.uk/imgres?imgurl=http://galilei2d.altervista.org/wordpress/wp-content/uploads/2009/02/repubblica_italiana_emblema_logo.jpg&imgrefurl=http://galilei2d.altervista.org/wordpress/%3Fp%3D2638&usg=__U_NwtkV8xg55KGGh5qnFuOvcbyE=&h=471&w=420&sz=129&hl=en&start=1&tbnid=uZVjlf4An5a6lM:&tbnh=129&tbnw=115&prev=/images%3Fq%3DRepubblica%2Bitaliana%26hl%3Den

http://images.google.at/imgres?imgurl=http://www.vdmarkt.de/emarket_admin/upload/foerd/thyssenkrupp.gif&imgrefurl=http://www.vdma-e-market.com/en/news/innovation-guide/single-view/n/115/b/313/t/TKF&h=204&w=200&sz=29&tbnid=IoejZr5xLz8TrM:&tbnh=99&tbnw=97&hl=de&start=2&prev=/images%3Fq%3DThyssenkrupp%26svnum%3D10%26hl%3Dde%26lr%3D%26sa%3DG

August 2013:

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This [Lixto] is great technology, but …

… too many people needed for generating and

maintaining the wrappers!

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From the tourism, real estate, and retail domains,

we understood independently that automated

wrapper-generation would be useful.

But how can this´be achieved?

Need for Fully Automated Extraction Technology

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 could do it fully automatically

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This talk is about extracting data from the Web…

…and about our (ad)ventures in:

Theory

Technology

Commercialization

Business K

1

BLACKBOX

Application domain with thousands of websites

URL

Application-relevant Structured data (XML or RDF)

DIADEM a €2.5M project started in April '10

machine learning e.g. neural networks page classification visual clue recognition text classification small entity identification

rule-based reasoning knowledge-based page exploration strategy navigation planning global decision making plausibility checks

X & Y Z

Taught knowledge

(expert system)

Self-learned

knowledge

Rough Idea: Knowledge via Rules Use “expert” rules that analyze Web pages and interact with them

Ontological rules (how do entities relate to each other)

- a flat is a real-estate property

- a house is a real-estate property

- a real-estate property has a number of rooms

- a price consists of a number and a currency

Phemomenological rules (how do entities manifest themselves on the Web?)

- the text chunk closest to an input field is with high prob. its descriptor.

- each sales item is described in a “convex” (usual. rectangular) region.

Site exploration rules:

- before filling a field try to leave it empty

- rules for handling next-page links

Other types of rules

DIADEM PROJECT

DIADEM lab at Oxford University

2010 2011 2012 2013 2014 2015

spin-out start-up

Wrapidity

Funding so far > $7M

VADA

DIADEM Architecture

OPAL

Form filling &

understanding

AMBER

Object identification

& alignment

BERyL

Block analysis &

object enrichment

OXPath

Efficient extraction

in the cloud

DATALOG± (implemented in DLV) Rule, exploration, control and integration language

New knowledge-based technology combining formalised knowledge with machine learning

Domains considered so far (since 2014)

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• Real estate UK • Real estate US • Used cars • Consumer electronics • Restaurant chains • Restaurants in the ‘Open Web’ • Jobs (from company Web sites)

• News

Commercial Impact

ERC Advanced Grant DIADEM

+ ERC Proof of Concept Grant

EXTRALYTICS

Founded February 2015

operating initially in Oxford

now in London

2 possibilities:

• Build up company with large client portfolio

• Sell technology, IP & software to strategic partner

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Theory

Technology

Commercialization

Business

2

New Application: Personalised Ads


Full

street

addresses

Reverse Geocoding

Käppelestr. 5, 76131 Karlsruhe

Herrenstraße 21, 76133 Karlsruhe

.....

.....


Full

street

addresses

Reverse Geocoding



.....

.....

Web Data Extraction


Full

street

addresses

Reverse Geocoding



.....

.....

Web Data Extraction

User profile is created and later refined.

Ads related to animals and children will be sent...

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CRIME HEATMAPS

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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

Scenario ➁: Hotel Agency

online travel agency

best price guarantee

prices of competing agencies

average market price

Scenario ➂: 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

tenders from all over the world

existing aggregators

expensive, often incomplete

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

Scenario ➃: Tenders for

construction company

Documents

Big Data from the Web · 2019-02-13 · ERC Advanced Grant DIADEM + ERC Proof of Concept Grant EXTRALYTICS Founded February 2015 operating initially in Oxford now in London 2 possibilities: