DIADEM: domain-centric, intelligent, automated data extraction

WWW 2012 – European Projects Track
April 16–20, 2012, Lyon, France
DIADEM: Domain-centric, Intelligent, Automated Data
Extraction Methodology∗
Tim Furche, Georg Gottlob, Giovanni Grasso, Omer Gunes, Xiaonan Guo,
Andrey Kravchenko, Giorgio Orsi, Christian Schallhart, Andrew Sellers, Cheng Wang
Department of Computer Science, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD
[email protected]
ABSTRACT
Search engines are the sinews of the web. These sinews have become strained, however: Where the web’s function once was a mix
of library and yellow pages, it has become the central marketplace
for information of almost any kind. We search more and more for
objects with specific characteristics, a car with a certain milage, an
affordable apartment close to a good school, or the latest accessory
for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers—never to be sure a better offer isn’t just around the
corner. What search engines are missing is understanding of the
objects and their attributes published on websites.
Automatically identifying and extracting these objects is akin to
alchemy: transforming unstructured web information into highly
structured data with near perfect accuracy. With DIADEM we
present a formula for this transformation, but at a price: DIADEM
identifies and extracts data from a website with high accuracy. The
price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the
domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language
of a website of this domain. In this demonstration, we demonstrate
with a first prototype of DIADEM that, in contrast to alchemists,
DIADEM has developed a viable formula.
Figure 1: Exploration: Form
extension businesses, articles, or information about products wherever they are published. Visibility on search engines has become
mission critical for most companies and persons. However, for
searches where the answer is not just a single web page, search engines are failing: What is the best apartment for my needs? Who offers the best price for this product in my area? Such object queries
have become an exercise in frustration: we users need to manually
sift through, compare, and rank the offers from dozens of websites returned by a search engine. At the same time, businesses
have to turn to stopgap measures, such as aggregators, that provide customers with search facilities in a specific domain (vertical
search). This endangers the just emerging universal information
market, “the great equalizer” of the Internet economy: visibility depends on deals with aggregators, the barrier to entry is raised, and
the market fragments. Rather than further stopgap measures, the
“publish on your website” model that has made the web such a success and so resilient against control by a few (government agents or
companies) must be extended to object search: Without additional
technological burdens to publishers, users should be able to search
and query objects such as properties or laptops based on attributes
such as price, location, or brand. Increasing the burden on publishers is not an option, as it further widens the digital divide between
those that can afford the necessary expertise and those that can not.
DIADEM, an ERC advanced investigator grant, is developing a
system that aims to provide this bridge: Without human supervision it finds, navigates, and analyses websites of a specific domain
and extracts all contained objects using highly efficient, scalable,
automatically generated wrappers. The analysis is parameterized
with domain knowledge that DIADEM uses to replace human annotators in traditional wrapper induction systems and to refine and
verify the generated wrappers. This domain knowledge describes
the ontology as well as the phenomenology of the domain: what are
the entities and their relations as well as how do they occur on websites. The latter describes that, e.g., real estate properties include a
location and a price and that these are displayed prominently.
Figures 1 and 2 show examples (screenshots from the current
prototype) of the kind of analysis required by DIADEM for fully
automated data extraction: Web sites needs to be explored to locate
relevant data, here real estate properties. This includes in particular
forms as in Figure 1 which DIADEM automatically classifies such
Categories and Subject Descriptors
H.3.5 [Information Storage and Retrieval]: On-line Information
Services—Web-based services
General Terms
Languages, Experimentation
Keywords
data extraction, deep web, knowledge
1.
INTRODUCTION
Search is failing. In our society, search engines play a crucial
role as information brokers. They allow us to find web pages and by
∗The research leading to these results has received funding from the
European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013) / ERC grant agreement DIADEM, no. 246858, http://diadem-project.info/.
Copyright is held by the International World Wide Web Conference Committee (IW3C2). Distribution of these papers is limited to classroom use,
and personal use by others.
WWW 2012 Companion, April 16–20, 2012, Lyon, France.
ACM 978-1-4503-1230-1/12/04.
267
WWW 2012 – European Projects Track
April 16–20, 2012, Lyon, France
DIADEM
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Figure 3: DIADEM Overview
level fact finders, whose output facts may be revised in case more
accurate information arises from other sources.
DIADEM focuses on a dual approach: at the low level of web
pattern recognition, we use machine-learning complemented by linguistic analysis and basic ontological annotation. Several of these
approaches, in addition to newly developed methods, are implemented as lower level building blocks (fact finders) to extract as
much knowledge as possible and integrate it into a common knowledge base. At a higher level, we use goal-directed domain-specific
rules for reasoning on top of all generated lower-level knowledge,
e.g. in order to identify the main input mask and the main extraction data structure, and to finalize the navigation process.
DIADEM approaches data extraction as a two stage process: In
a first stage, we analyse a small fraction of a web site to generate a
wrapper that is then executed in the second stage to extract all the
relevant data on the site at high speed and low cost. Figure 3 gives
an overview of the high-level architecture of DIADEM. On the left,
we show the analysis, on the right the execution stage.
(1) Sampling analysis: In the first stage, a sample of the web
pages of a site are used to fully automatically generate wrappers
(i.e., extraction program). The analysis is based on domain knowledge that describes the objects of interest (the “ontology”) and how
they appear on the web (the “phenomenology”). The result of the
analysis is a wrapper program, i.e., a specification how to extract
all the data from the website without further analysis.
Conceputally, it is divided into two major phases, though these are
closely interwoven in the actual analysis:
(i) Exploration: DIADEM automatically explores a site to locate
relevant objects. The major challenge here are web forms: DIADEM needs to understand such forms enough to fill them for
sampling, but also to generate exhaustive queries for the extraction stage such that all the relevant data is extracted (see
[1]). DIADEM’s form understanding engine OPAL [9, 10] is
uses an phenomenology of forms in the domain to classify the
form fields.
The exploration phase is supported by the page and block classification in MALACHITE where we identify, e.g., next links
in paginate results, navigation menus, and irrelevant data such
as advertisements. We further cluster pages by structural and
visual similarity to guide the exploration strategy and to avoid
analysing many similar pages. Since such pages follow a common template, the analysis of one or two pages from a cluster
usually suffices to generate a high confidence wrapper.
(ii) Identification: The exploration unearths those web pages that
contain actual objects. But DIADEM still needs to identify the
precise boundaries of these objects as well as their attributes.
To that end, DIADEM’s result page analysis AMBER [11]
analyses the repeated structure within and among pages. It
exploits the domain knowledge to distinguish noise from relevant data and is thus far more robust than existing data extraction approaches. AMBER is complemented by Oxtractor, that
analysis the free text descriptions. It benefits in this task from
the contextual knowledge in form of attributes already iden-
Figure 2: Identification: Result Page
that each form field is associated with a type from the domain ontology such as “minimum price” field in a “price range” segment.
These form models are then used for filling the form for further
analysis, but also to generate exhaustive queries for latter extraction of all relevant data. Figure 2 illustrates a (partial) result of the
analysis performed on a result page, i.e., a page containing relevant
objects of the domain: DIADEM identifies the objects, their boundaries on the page, as well as their attributes. Objects and attributes
are typed according to the domain ontology and verified against the
domain constraints. In Figure 2, e.g., we identify real estate properties (for sale) together with price, location, legal status, number
of bed and bathrooms, pictures, etc. The identified objects are generalised into a wrapper that can extract such objects from any page
following the same template without further analysis.
In the first year and a half, DIADEM has progressed beyond
our expectations: The current prototype is already able to generate wrappers for most UK real estate and used car websites with
higher accuracy than existing wrapper induction systems. In this
demonstration, we outline the DIADEM approach, first results, and
describe the live demonstration of the current prototype.
2.
DIADEM—THE METHODOLOGY
DIADEM is fundamentally different from previous approaches.
The integration of state-of-the-art technology with reasoning using
high-level expert knowledge at the scale envisaged by this project
has not yet been attempted and has a chance to become the cornerstone of next generation web data extraction technology.
Standard wrapping approaches [17, 6] are limited by their dependence on templates for web pages or sites and their lack of understanding of the extracted information. They cannot be generalised
and their dependence on human interaction prevents scaling to the
size of the Web. Several approaches that move towards fully automatic or generic wrapping of the existing World Wide Web have
been proposed and are currently under active development. Those
approaches often try to iteratively learn new instances from known
patterns and new patterns from known instances [5, 19] or to find
generalised patterns on web pages such as record boundaries. Although current web harvesting and automated information extraction systems show significant progress, they still lack the combined
recall and precision necessary to allow for very robust queries. We
intend to build upon those approaches, but add deep knowledge
into the extraction process and combine them in a new manner into
our knowledge-based approach; we propose to use them as low-
268
WWW 2012 – European Projects Track
April 16–20, 2012, Lyon, France
1
100
0
0.93
0
20
40
60
80
100 120 140
UK Real Estate (100) UK Used Car (100)
#pagesICQ (98)
Tel-8 (436)
50
40
400
30
300
600
20
200
400
10
100
0
0
1000
Web Harvest
Chickenfoot
1600
200
800
150
50
0
0 150 20
40300 350
60 40080
0 50 100
200 250
450 100 120 140
time [sec]
#pages
140
120
Chickenfoot
1000
100
200
160
OXPath
1400
memory
extracted matches Lixto
1200visited
Web
Harvest
pages
100
800
80
600
60
400
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200
1200
memory [MB]
300
OXPath
Web Content Extractor
memory
Lixto
visited
Visual Web Ripper
500 pages
pages
time [sec]
400
600
time w/o page loading [sec]
0.94
60
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0.95
500
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700
600
0.96
F-score
OXPath
Web Content Extractor
Lixto
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Web Harvest
Chickenfoot
700
0.98
0.97
Recall
800
#pages [1000] / #results [100,000]
Precision
0.99
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30
20
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100 200
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Table 2 shows the incremental improvements made in each
round. For each round, we report the number of unannotated instances, the number of instances recognized through attribute alignment, and the number of correctly identified instances. For each
round we also show the corresponding precision and recall met-
time [sec]
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Figure 4: Exploration and Execution
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unannotated instances
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to
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7.
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a
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pages.
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that
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and
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of DIADEM
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150
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30(4):929–959,
2005.
year,
summarise
on three
components
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ysis,
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4
[9]
we
start
the
demonstration
with
the
execution.
needed.
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do
not
consider tools su
1999.
simulating
Google
Scholar
pages
on
our
web
server
(to
avoid
overly
the four rounds. Using the incomplete gazetteer, we initially fail to
shows that OPAL is able to identify about 99% taxing
of all Google’s
form fields
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demonstration
starts
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only an
[15]
N. Sawa,
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Sugimoto,
andthey
Kitagawa.
servers). The
number
in brackets
indicate
we OXPath,
annotate 328 out
of
1484
attribute
instances.
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the
first
round,
the
6. real
CONCLUSION
AND correctly.
FUTURE
WORK
in the UK
estate and used car domain
We
also
show
OXPath,
shown
inWrapping
Figure
6bof
and
illustrated
thefor
screenas
required
extraction tasks. We a
Wraplet:
your
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with
a in
lightweight
scale
the
axes.
OXPath
extracts
over
7
million
pieces
data
from
gazetteer
step
identifies
226
unannotated
instances.
197
language.with
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387–394,
2007. as
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results
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where
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weor XWRAP [10]
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OXPath 140,
is the000
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(194
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instances
are
correctly
identified,
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yields
a
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[16] W. Shen,
Doan,
J. F.links)
Naughton,
R. Ramakrishnan.
> system
96% accuracy
(in memory
contrast recent
approaches
achievestrongly
at
demonstrate
how
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human
construct
wrapper
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the abilitybyto traverse to new
tion
with strict
guarantees,
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reflect
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similar
tests
(repeatedly
clicking
on would
all
for aand
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extraction
using generator
datalog
with
and recall
87.2%
and
60.1%
ofevaluation.
the unannotated
instances,
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in our
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an a visual interface
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[8]).
The
latter
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is without
use
of
domain
knowlandinformation
how
supervised
cantogentasks
with
one on very
largeapages
(Wiki-wrapper
embedded
extraction
predicates.
In VLDB, 1033–1044, 2007.
and 81.3%
all instances.
The
increase
in precision
is stable
in the
scripted
websites
easily. Thus, we
part of
the toolset
of developers
interacting
edge.ofimportant
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domain
knowledge
we
could
easily
achieve
close
to
a wrapper
from
just a few
examples.
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provide
a number
pedia)
andwith
one
onweb.
pageseralize
with[17]
many
results
(product
listings
on
J.-Y. Su, D.-J. Sun, I.-C. Wu, and L.-P. Chen. On design of
all the learning
rounds
sointhat,
the
end
ofa strong
the
fourth
task
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Scholar, which does
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arealso
committed
building
set
ofiteration,
tools
around
OX- web
99% accuracy
theseatto
cases.
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5 [11]
shows
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results
of standard
examples,
butand
are 12
also
suggestions
for
the JMST,
audiGoogle)
from
different
shops.
Figures 5(c)
(inopen
Ap-tosystem
browser-oriented
data
extraction
andon
plug-ins.
scripted
AMBERfor
achieves
a precision
of
and
a recall
80.6%
ofagain
the
Path.
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provide
a97.8%
visual
generator
forofOXPath
expressions
and
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the wrapper
istocreated
we submit itOn
andheavily
let it run
in thepages, the perfo
data
area,
record,
and
attribute
identification
on result
pages
18(2):189–200,
pendix
E)
show
thatence.
memory
is constant
w.r.t.2010.
pages visited
even
more pronounced. With each
unannotated
instances,
an overall
precision
and recall
offor95.1%
for AMBER
in and
the UK
real estate
domain.
We
report
each
at- largerbackground
while weand
continue
withquite
the rest of isthe
demonstration.
even
the
much
pages on Wikipedia
the often
and 93.8%, respectively.
visually-involved pages reached by the Google product search.
(a) Evaluation
time, <150 p.
(a) OPAL
WWW 2012 – European Projects Track
April 16–20, 2012, Lyon, France
1
2
4
5
6
3
(a) OPAL
(b) OXPath
Figure 6: Demonstration UIs
In the second part of the demonstration, we focus on the DIADEM prototype. A screencast of the demo is available from
[7] O. de Moor, G. Gottlob, T. Furche, and A. Sellers, eds.
Datalog Reloaded, Revised Selected Papers, LNCS. 2011.
[8] E. C. Dragut, T. Kabisch, C. Yu, and U. Leser. A hierarchical
approach to model web query interfaces for web source
integration. In VLDB, 2009.
[9] T. Furche, G. Gottlob, G. Grasso, X. Guo, G. Orsi, and
C. Schallhart. Real understanding of real estate forms. In
WIMS, 2011.
[10] T. Furche, G. Gottlob, G. Grasso, X. Guo, G. Orsi, and
C. Schallhart. OPAL: Automated Form Understanding for
the Deep Web. In WWW, 2012.
[11] T. Furche, G. Gottlob, G. Grasso, G. Orsi, C. Schallhart, and
C. Wang. Little knowledge rules the web: Domain-centric
result page extraction. In RR, 2011.
[12] T. Furche, G. Gottlob, X. Guo, C. Schallhart, A. Sellers, and
C. Wang. How the minotaur turned into Ariadne: Ontologies
in web data extraction. ICWE, (keynote), 2011.
[13] T. Furche, G. Gottlob, G. Grasso, C. Schallhart, and
A. Sellers. OXPath: A language for scalable,
memory-efficient data extraction from web applications. In
VLDB, 2011.
[14] G. Gottlob, T. Lukasiewicz, and G. I. Simari. Answering
threshold queries in probabilistic datalog+/- ontologies. In
SUM, 2011.
[15] G. Gottlob, T. Lukasiewicz, and G. I. Simari. Conjunctive
query answering in probabilistic datalog+/- ontologies. In
RR, 2011.
[16] G. Gottlob, G. Orsi, and A. Pieris. Ontological queries:
Rewriting and optimization. In ICDE, 2011.
[17] N. Kushmerick. Wrapper induction: efficiency and
expressiveness. AI, 118, 2000.
[18] G. Orsi and A. Pieris. Optimizing query answering under
ontological constraints. In VLDB, 2011.
[19] A. Yates, M. Cafarella, M. Banko, O. Etzioni, M. Broadhead,
and S. Soderland. Textrunner: open information extraction
on the web. In NAACL 2007.
diadem-project.info/screencast/diadem-01-april-2011.mp4.
In the demonstration, we let the prototype run on several pages
from the UK real estate and used car domain. The prototype runs
in interactive mode, i.e., it visualizes its deliberations and conclusions and prompts the user before continuing to a new page, so that
the user can inspect the results on the current page. With this, we
can demonstrate first how DIADEM analyses forms and explores
web sites based on these analyses. In particular, we show how DIADEM deals with form constraints such as mandatory fields, multi
stage forms and other advanced navigation structures. The demo
fully automatically explores the web site until it finds result pages
for which it performs a selective analysis sufficient to generate the
intended wrapper. As before, the demonstration discusses a number
of such result pages and shows in the interactive prototype how DIADEM extracts objects from these result pages as well as identifies
further exploration steps.
We conclude the demonstration with a brief outlook into applications of DIADEM technology beyond data extraction: The
screencast at diadem-project.info/opal shows how we use DIADEM’s form understanding to provide advanced assistive form
filling: where current assistive technologies are limited to simple
keyword matching or filling of already encountered forms, DIADEM’s technology allows us to fill any form of a domain with values from a master form with high precision.
5.
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