=Paper=
{{Paper
|id=None
|storemode=property
|title=User driven Information Extraction with LODIE
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_112.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/GentileM14
}}
==User driven Information Extraction with LODIE==
https://ceur-ws.org/Vol-1272/paper_112.pdf
User driven Information Extraction with LODIE
Anna Lisa Gentile and Suvodeep Mazumdar
Department of Computer Science, University of Sheffield, UK
{a.gentile, s.mazumdar}@sheffield.ac.uk
Abstract. Information Extraction (IE) is the technique for transform-
ing unstructured or semi-structured data into structured representation
that can be understood by machines. In this paper we use a user-driven
Information Extraction technique to wrap entity-centric Web pages. The
user can select concepts and properties of interest from available Linked
Data. Given a number of websites containing pages about the concepts of
interest, the method will exploit (i) recurrent structures in the Web pages
and (ii) available knowledge in Linked data to extract the information
of interest from the Web pages.
1 Introduction
Information Extraction transforms unstructured or semi-structured text into
structured data that can be understood by machines. It is a crucial technique
towards realizing the vision of the Semantic Web. Wrapper Induction (WI) is
the task of automatically learning wrappers (or extraction patterns) for a set
of homogeneous Web pages, i.e. pages from the same website, generated using
consistent templates1 . WI methods [1,2] learn a set of rules enabling the system-
atic extraction of specific data records from the homogeneous Web pages. In this
paper we adopt a user driven paradigm for IE and we perform on demand extrac-
tion on entity-centric webpages. We adopt our WI method [2,3] developed within
the LODIE (Linked Open Data for Information Extraction) framework [4]. The
main advantage of our method is that does not require manually annotated
pages. The training examples for the WI method are automatically generated
exploiting Linked Data.
2 State of the Art
Using WI to extract information from structured Web pages has been studied
extensively. Early studies focused on the DOM-tree representation of Web pages
and learn a template that wrap data records in HTML tags, such as [1,5,6]. Su-
pervised methods require manual annotation on example pages to learn wrappers
for similar pages [1,7,8]. The number of required annotations can be drastically
reduced by annotating pages from a specific website and then adapting the learnt
1
For example, a yellow page website will use the same template to display information
(e.g., name, address, cuisine) of different restaurants.
2 Gentile and Mazumdar
rules to previously unseen websites of the same domain [9,10]. Completely un-
supervised methods (e.g. RoadRunner [11] and EXALG [12]) do not require any
training data, nor an initial extraction template (indicating which concepts and
attributes to extract), and they only assume the homogeneity of the considered
pages. The drawback of unsupervised methods is that the semantic of produced
results is left as a post-process to the user. Hybrid methods [2] intend to find a
tradeoff with these two limitations by proposing a supervised strategy, where the
training data is automatically generated exploiting Linked Data. In this work
we perform IE using the method proposed in [2,3] and follow the general IE
paradigm from [4].
3 User-driven Information Extraction
In LODIE we adopt a user driven paradigm for IE. As first step, the user must
define her/his information need. This is done via a visual exploration of linked
data (Figure 1).
Fig. 1: Exploring linked data to define user need, by selecting concepts and attributes to extract.
Here the user selected the concept Book and the attributes title and author. As author is a datatype
attribute, of type P erson, the attribute name is chosen.
The user can explore underlying linked data using the Affective Graphs vi-
sualization tool [13] and select concepts and properties she/he is interested in
(a screenshot is shown in Figure 1). These concepts and properties get added
to the side panel. Once the selection is finished, she/he can start the IE pro-
cess. The IE starts with a dictionary generation phase. A dictionary di,k consists
of values for the attribute ai,k of instances of concept ci . Noisy entries in the
dictionaries are removed using a cleaning procedure detailed in [3]. As a run-
ning example we will assume the user wants to extract title and author for the
concept Book. We retrieve from the Web k websites containing entity-pages of
the concept types selected by the user, and save the pages Wci ,k . Following the
Book example, Barnes&Noble2 or AbeBooks3 websites can be used, and pages
collected in Wbook,barnesandnoble and Wbook,abebooks .
For each Wci ,k we generate a set of extraction patterns for every attribute.
In our example we will produce 4 sets of patterns, one per each website and
2
http://www.barnesandnoble.com/
3
http://www.abebooks.co.uk
User driven Information Extraction with LODIE 3
attribute. To produce the patterns we (i) use our dictionaries to generate brute-
force annotations on the pages in Wci ,k and then (ii) use statistical (occurrence
frequency) and structural (position of the annotations in the webpage) clues to
choose the final extraction patterns.
Briefly, a page is transformed to a simplified page representation Pci : a col-
lection of pairs 〈xpath4 , text value〉. Candidates are generated matching the dic-
tionaries di,k against possible text values in Pci (Figure 2).
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[1]/H2[1]/text()[1] breaking dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/DIV[1]/H2[1]/EM[1]/text()[1] breaking dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[10]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[1]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[2]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[3]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[6]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/TABLE[8]/TBODY[1]/TR[1]/TD[3]/B[1]/A[1]/text()[1] break-
ing dawn
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[3]/DIV[3]/UL[1]/LI[2]/A[1]/text()[1] the host
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[3]/DIV[3]/UL[1]/LI[5]/A[1]/text()[1] new moon
Fig. 2: Example of candidates for book title for a Web page on the book “Breaking Dawn”, from the
website AbeBooks.
Final patterns are chosen amongst the candidates exploiting frequency in-
formation and other heuristics. Details of the method can be found in [2,3].
In the running example, higher scoring patterns for extracting book title from
AbeBooks website are shown in Figure 3.
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[1]/H2[1]/text()[1] 329.0
/HTML[1]/BODY[1]/DIV[2]/DIV[2]/DIV[2]/DIV[4]/DIV[1]/H2[1]/EM[1]/text()[1] 329.0
Fig. 3: Extraction patterns for book titles from AbeBooks website.
All extraction patterns are then used to extract target values from all Wci ,k .
Results are produced as linked data, using the concept and properties initially
selected by the user for representation, and made accessible to the user via an
exploration interface (Figure 4), implemented using Simile Widgets5 .
A video showing the proposed system used with the running Book exam-
ple can be found at http://staffwww.dcs.shef.ac.uk/people/A.L.Gentile/
demo/iswc2014.html.
4 Conclusions and future work
In this paper we describe the LODIE approach to perform IE on user defined
extraction tasks. The user is prompted a visual tool to explore available linked
data and choose concepts for which she/he wants to mine additional material
from the Web. We learn extraction patterns to wrap relevant websites and return
structured results to the user.
4
http://www.w3.org/TR/xpath/
5
http://www.simile-widgets.org/
4 Gentile and Mazumdar
Fig. 4: Exploration of results produced by the IE method
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