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|pdfUrl=https://ceur-ws.org/Vol-1267/LD4IE2014_IEchallenge_Overview.pdf
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https://ceur-ws.org/Vol-1267/LD4IE2014_IEchallenge_Overview.pdf
Linked Data for Information Extraction Challenge 2014
Tasks and Results
Robert Meusel and Heiko Paulheim
University of Mannheim, Germany
Data and Web Science Group
{robert,heiko}@informatik.uni-mannheim.de
Abstract. For making the web of linked data grow, information extraction meth-
ods are a good alternative to manual dataset curation, since there is an abundance
of semi-structured and unstructured information which can be harvested that way.
At the same time, existing Linked Data sets can be used for training and evalu-
ating such information extraction systems. In this paper, we introduce the Linked
Data for Information Extraction Challenge 2014. Using the example of person
data in Microformats, we show how training and testing data can be curated at
large scale. Furthermore, we discuss results achieved in the challenge, as well as
open problems and future directions for the challenge.
Keywords: Information Extraction, Linked Data, Benchmarking, Web Data Commons,
Microformats, Bootstrapping the Web of Data
1 Introduction
The web of linked data is constantly growing, from a small number of hand-curated
datasets to around 1, 000 datasets [1, 7], many of them created using heuristics and/or
crowdsourcing. Since manual creation of datasets has its inherent scalability limitations,
methods that automatically populate the web of linked data are a suitable means for its
future growth.
Different methods for automatic population have been proposed. Open information
extraction methods are unconstrained in the data they try to create, i.e., they do not use
any predefined schema [3]. In contrast, supervised methods have been proposed that
are trained using existing LOD datasets and applied to extract new facts, either by using
the dataset as a training set for the extraction [2, 9], or by performing open information
extraction first, and mapping the extracted facts to a given schema or ontology [4, 8].
In this paper, we discuss the creation of large-scale training and evaluation data sets for
such supervised information extraction methods.
2 Task and Dataset
In the last years, more and more websites started making use of markup languages as
Microdata, RDFa or Microformats to annotate information on their pages. In 2013 over
3
13.8% of all websites made use of at least one of those three markup languages, where
the most used markup format is Microformats hCard [5]. Tools like Any231 are capable
of extracting such annotated information from those web pages and return them as RDF
triples.
On of the largest, publicly available collections of such extracted triples from HTML
pages is provided by the Web Data Commons project.2 The triples were extracted by the
project using Any23 and web crawls curated by the Common Crawl Foundation,3 which
maintains one of the largest, publicly available web crawl corpora. So far, the project
offers three different datasets, gathered from crawls from 2010, 2012 and 2013 includ-
ing all together over 30 billion triples. The latest dataset, including 17 billion triples,
which were extracted from over half a billion HTML pages, contain large quantities of
product, review address, blog post, people, organization, event, and cooking recipe data
[5].
Since both the original web page and the extracted RDF triples are publicly avail-
able, those pairs (a web page plus its corresponding triples) can serve as training data
for a supervised information extraction system. In the 2014 edition of the challenge,
we focus on one class of information only, i.e., Microformats data about persons using
the hCard vocabulary.4 For the challenge, we provide a training dataset both with and
without markup, as well as a test set of web pages without the corresponding triples,
which are kept as a non-public hold out set for evaluation. The training dataset consists
of 9, 877 web pages and 373, 501 extracted triples, while the test dataset consists of
2, 379 web pages, with 85, 248 extracted triples (where the triples are not known to the
challenge participants).5
Fig. 1 shows the distribution of predicates for both the training and the test set.
It can be observed that the most frequent predicate is rdf#type (assigning the type
vcard#person), followed by name attributes. There are no predicates which are ex-
clusively contained in one of the two datasets.
As the ultimate goal of an information extraction system would be to extract such
data from web pages without markup, the test set should consist of non-markup pages.
However, for such pages, it would be very time-consuming to curate a reasonably sized
gold standard. As an alternative, we use the original pages from the Common Crawl and
remove the markup. This removal is done by substituting the Microformats classes with
random strings, which are uniquely created for each web page. This is done to allow
extraction systems to discover and exploit style information bound to those elements
(e.g., person names displayed in bold).
In order to evaluate information extraction systems, participants were asked to send
the extracted triples for the test set with the corresponding URL where the information
was extracted from. We compared those to the original triples and computed recall,
precision, and F-measure. Two triples from the URL are counted as identical if their
subject, predicate, and object are all three identical URIs or literals, where blank nodes
1
https://code.google.com/p/any23/
2
http://webdatacommons.org/structureddata
3
http://commoncrawl.org/
4
http://microformats.org/wiki/hcard
5
The datasets, except for the triples of the test set, are available online at http://data.dws.informatik.uni-
mannheim.de/LD4IE/
4
1000000
100000
10000
1000
100
10
1
0.1 Training
Evaluation
rdf#type
vcard#given-name
vcard#title
vcard#uid
vcard#n
vcard#fn
vcard#family-name
vcard#photo
vcard#org
vcard#organization-name
vcard#nickname
vcard#geo
vcard#note
vcard#logo
vcard#role
vcard#additional-name
vcard#adr
vcard#url
vcard#tel
vcard#email
vcard#workTel
vcard#honorific-prefix
vcard#organization-unit
vcard#category
vcard#fax
vcard#bday
vcard#class
Fig. 1: Distribution of predicates in the training and test set
are always counted as identical.6 Figure 2 summarizes the creation of the data sets and
the evaluation process.
3 Results
One effect of the process for creating the 2014 datasets is that all web pages in the evalu-
ation dataset can be expected to contain data about people, companies, or organizations.
This allows for implementing a trivial baseline, i.e., creating a triple
:1 rdf:type hcard:VCard .
for each web page.
We received one submission to the challenge, i.e., the µRaptor system [6]. In the fol-
lowing, we compare the baseline against that system, and provide some further insights
in the results.
Table 1 shows the overall performance in terms of recall, precision, and F-measure.
First of all, it is interesting that the baseline does not reach a precision of 1. This hints
at pages for which the gold standard is not perfect: for example, names and other
attributes of a person or organization are given, without explicitly stating the type
hcard#VCard. In cases like these, a perfect information extraction system will not
reach a precision of 1, based on the gold standard. One possible solution here is to use
RDFS entailment7 to materialize all axioms of both the gold standard and the solutions
using RDFS inference on the vCard vocabulary.8
6
The drawback of that convention is that for a web page containing n different hcard#VCard instances, a perfect
solution correctly attributing all properties to n blank nodes cannot be distinguished from a solution attributing them all
to one single blank node, although the latter is clearly inferior. Resolving that issue, however, requires graph matching
with blank nodes, which is not a trivial problem, and such a solution might be prone to introducing other biases.
7
http://www.w3.org/TR/rdf11-mt/#rdfs-entailment
8
http://www.w3.org/Submission/vcard-rdf/
5
extraction
Extracted
Training Plain HTML
Statements
Dataset (Training)
(Training)
Web pages split training
with
microformats
execution
Extracted
Test Plain HTML Extraction Statements
Dataset (Test) System (Test)
extraction
Input (Web Data Commons)
Provided for the challenge
Extracted
Evaluation (by challenge organizers) Statements Evaluation
(Test)
Developed by challenge participants
Submitted by challenge participants
Fig. 2: Dataset creation and evaluation process
Table 1: Overall performance of the submitted system and the baseline.
Approach # Triples Recall Precision F-measure
µRaptor 61, 909 0.665 0.916 0.771
Baseline 2, 379 0.027 0.966 0.052
Another observation for the baseline is that, even for type statements, the recall of
the baseline is low below 10%, since many pages contain data about more than one
entity.
For the submitted solution µRaptor, we can observe a significant amount of informa-
tion at a suitable precision. Fig. 3 depicts the performance of that system by predicate.
A first observation is that more frequent predicates are more easily extracted (Pearson’s
correlation between frequency and F-measure is 0.687). While pictures are particularly
well extracted (as they are rather easy to detect in HTML), organizations and addresses
seem more difficult. The latter may be explained by our evaluation using string equiv-
alence, which may not always be appropriate for complex organization names and ad-
dresses. On the other hand, it is interesting to see that the recall for telephone numbers
is unusually low, compared to the other predicates.
4 Conclusion
This year, we initiated the first Linked Data for Information Extraction Challenge,
showing that it is possible to create large-size training and evaluation data sets, which
allows for benchmarking supervised information extraction systems. The task this year
used hCard data, i.e., data about people, companies, and organizations.
The submitted results show that systems can be developed which, trained on a set of
web pages, extract meaningful information. On the other hand, the results also show that
6
100.00%
80.00%
60.00%
40.00%
20.00%
Recall
0.00% Precision
rdf#type
vcard#n
vcard#geo
vcard#title
vcard#fn
vcard#given-name
vcard#family-name
vcard#photo
vcard#org
vcard#organization-name
vcard#nickname
vcard#adr
vcard#url
vcard#tel
vcard#email
F-measure
Fig. 3: Performance of µRaptor by Predicate
the proposed evaluation has some limitations by nature, compared by, e.g., a manually
curated gold standard and supervised evaluation: not all the data in the gold standard
may be complete and correct, and the strict comparison of extracted values may be too
strict in some cases (e.g., when comparing addresses for string equality). Nevertheless,
a manual creation and evaluation would not be scalable enough – which holds for the
method proposed in this paper, which can be used to create training and test sets of
nearly arbitrary sizes.
There are two main directions in which we want to extend this evaluation in the
future. The first (and obvious) one is to include other classes as well, possibly also from
different markup techniques (i.e., Microdata and RDFa).
The second direction is to make the task more realistic by mixing relevant and irrel-
evant pages. This year, the evaluation dataset was compiled from web pages that contain
markup about persons. Thus, an extraction system could assume that some sort of per-
son data could be found on that web page. In a more realistic setting, the extraction
system would get a set of web pages which may or may not contain data of the de-
sired type. Thus, future editions will foster two evaluation datasets per class: one with
relevant pages (like this year), and one with a mix of relevant and irrelevant pages.
Since it is not reasonable to simply use a set of random, not marked-up web pages
as irrelevant pages (as they may contain information of the desired type, but just no
markup), one idea is to use marked-up web pages from which no data of the type at hand
has been extracted. The rationale is that since the web page creator has used markup,
it is likely that s/he would have also included markup for all other applicable types as
well. Therefore, it is likely that the web page is irrelevant for the type at hand.
Another interesting question is how representative the data in the training sets is,
which is relevant for the general applicability of systems trained based on that data.
Since some wide-spread content management systems (CMS) create markup in web
pages, it may be that the dataset shows a bias towards web pages created using those
7
CMS. In future editions of the challenge, we aim at a closer examination of such biases
and blind spots.
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