=Paper=
{{Paper
|id=Vol-185/paper-11
|storemode=property
|title=Knowledge extraction from WebPages
|pdfUrl=https://ceur-ws.org/Vol-185/semAnnot05-11.pdf
|volume=Vol-185
|dblpUrl=https://dblp.org/rec/conf/semweb/TenierNPT05
}}
==Knowledge extraction from WebPages==
https://ceur-ws.org/Vol-185/semAnnot05-11.pdf
Knowledge extraction from webpages
Sylvain Tenier12 , Amedeo Napoli2 , Xavier Polanco1, and Yannick Toussaint2
1
Institut National de l’Information Scientifique et Technique
54514 Vandoeuvre-ls-Nancy, France
{polanco,tenier}@inist.fr,
http://www.inist.fr/uri/accueil.htm
2
Laboratoire Lorrain de Recherche en Informatique et ses Applications
BP 239, 54506 Vandoeuvre ls Nancy Cedex, France
napoli,toussaint,tenier}@loria.fr
http://www.loria.fr/equipes/orpailleur
Abstract. This article presents a system to extract Knowledge from
webpages by producing semantic annotations. taking into account seman-
tic information from the domain to annotate an element in a webpage
implies solving two problems : (1) identifying the syntactic structure of
this element in the webpage and (2) identifying the most specific concept
(in terms of subsumption) of the ontology that will be used to annotate
this element. Our approach relies on a wrapper-based machine learning
algorithm combined with reasoning making use of the formal structure
of the ontology.
1 Context of the research
Our system aims at using information provided by research teams on their web-
site to generate knowledge about the European Research Community. In order
to make this information machine-processable, a formal representation of the
content of the webpages is needed, encoded with a well-defined syntax and se-
mantics. This is the purpose of semantic annotation [1]. The system is provided
with :
– an ontology which represents the concepts of a domain and their relation-
ships. The ontology, implemented in the Web Ontology Language (OWL),
is based on Description Logics (DL) and thus reasoning mechanisms, like
classification and subsumption, are provided [2],
– webpages from which data are extracted according to the ontology.
For each data in the document, the systems generates an individual with the
concept and roles it instantiates. Each individual is added to a Knowledge Base
(KB). Two main tasks are dealt with: the first is about locating each data in
the provided documents and extracting it to generate a “raw” individual which
may not be specific enough. It is followed by a reasoning task which infers the
most specific concept the individual is an instance of.
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Fig. 1. overview of the approach
2 Extracting knowledge
2.1 Manual annotation
The system is based on machine learning techniques (fig. 1). It learns from some
examples the data to be extracted. Here, the examples are webpages annotated
by hand according to an ontology. This task is performed in a dedicated envi-
ronment in which a user is presented a page to annotate and the concepts of the
ontology in a browser-like interface. The user then annotates a few occurences
of the concepts of the ontology he wants the system to identify. The number of
annotations needed depends on the regularity of the page. If the data is strongly
structured, like tables, only two or three examples are needed. For example, to
extract knowledge about research teams, the SW RC ontology (fig. 2) is loaded
along with a page presenting the persons working in a team and the projects
they are involved in. The user then annotates some data that are instances of
the concept of Person and some instantiating the concept of Project. The output
is a marked document in which the annotations are embedded.
2.2 Wrapper induction using the tree structure of the page
The learning algorithm is derived from Kushmerick’s work on wrapper induction,
which identifies classes of wrappers than can be learnt using a deterministic
algorithm with low complexity [3]. A wrapper is a procedure using the syntactic
regularities of a document to extract data. This is particularly suited to semi-
structured documents like webpages. We have adapted Kushmerick’s work to
make use of the tree structure of a webpage provided by the w3c’s Document
Object Model (DOM). This model defines a path leading to each data in the
document. The marked page is taken as input and the similarities between paths
leading to data which are instances of the same concept are learnt. The output
is a wrapper which is applied to pages in which the tree structure is similar to
the example page in order to extract the data and their relationships.
102
Fig. 2. concepts and properties of the SWRC ontology
2.3 Adding knowledge to the KB
In step 3, the relevant wrappers are applied to the documents to extract data.
For each extracted data, an instance of the concept it belongs to is added to the
KB together with its relationships with other data. The resulting KB is a graph
implemented using the Resource Description Framework (RDF) which connects
each individual to the ontology and the individuals it is related to. At that point,
one problem is that the extracted knowledge is not specific enough. The reason is
that a wrapper must be as generic as possible in order to extract all the relevant
data in a document. Therefore, it is induced using the most general concept
available. For example, to extract people from a research team according to the
SW RC ontology, the wrapper will be designed to recognize any instance of the
concept Person instead of more specific concepts like AcademicStaff or Student.
The second limitation is that the semantics of the relationships is unknown, since
they are extracted using syntactic properties (for instance, two data are related
if they have a common parent node).
3 Refining the knowledge
With an ontology implemented in the Knowledge Representation language OWL,
reasoning mechanisms are provided. One of them is instantiation, which given
an individual and a set of concepts finds the most specific concept the individ-
ual belongs to, with respect to the subsumption ordering. For example, in the
SW RC ontology, two concepts are subsumed by the Person concept and have
a role whose filler must be an instance of the Project concept. Therefore, the
individuals in the KB that are instances of a Person and have a relationship
to a Project must be either instances of AcademicStaff or PhdStudent and the
relationship is identified as being the Project role. The new knowledge is inserted
into the KB.
103
4 Conclusion
We have presented a system that integrates semantics in the annotation genera-
tion process by making use of the reasoning mechanisms provided by the ontol-
ogy according to which webpages are annotated. This requires not only concept
instances but also role instances to be extracted. Initial works on webpage anno-
tation such as Annotea [4] aimed at enabling collaborative work between people.
The need for annotations machines could understand and reason with led to
systems, such as S-CREAM [5], producing semantic annotations according to
Description Logics based ontologies. Since manual annotation is a tedious and
error-prone task, machine learning system have been proposed; S-CREAM and
MnM [6] implement Amilcare [7], a semi-automatic tool that produces extrac-
tion rules from a corpus to generate concept instances; however, role instances
are not dealt with. Recently, fully automatic systems like Amardillo [8] or C-
Pankow [9] have been presented that make use of the redundancy of data on the
Web. Our system relies on the hypothesis of a of mapping between the syntax
and the semantics of a webpage. Since its efficiency depends on the presence of
regularities in the structure, pages from research team websites are well suited
for this task.
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