Naychi Lai Lai Thein, Khin Haymar Saw Hla, Ni Lar Thein
University of Computer Studies (Yangon)
Keywords: Semantic Web, Shared Ontology, Information Extraction, Shared Terminology, Web Ontology Language,
Source Ontology, Local Ontology, Semantic Mapping.
Abstract: The Semantic Web is an extension of the current Web in which information is given well-defined meaning,
better enabling computers and people to work in cooperation. One of the basic problems in the development
of Semantic Web is information integration. Indeed, the web is composed of a variety of information
sources, and in order to integrate information from such sources, their semantic integration and reconciliation
is required. Also, web pages are formatted with HTML which is only a human readable format and the
agents cannot understand their meaning. In this paper, we present an approach to extract information from
unstructured documents (e.g. HTML) and are converted to standard format (XML) by using source ontology.
Then, we translate XML output to local ontology. This paper also describes a key technology for mapping
between ontologies to compute similarity measures to express complex relationships among concepts. In
order to address this problem, we apply machine learning approach for semantic interoperability in the real,
commercial and governmental world.
Most of today’s web content is easily understood by
humans but difficult to understand by computers. A
significant portion of the data on the web is in the
form of HTML pages. Since content, navigational
information and formatting have no clear separation
in HTML, the conventional information retrieval
systems have the additional task of dealing with
noisy data when providing full text search.
However, the number of different information
urces is growing significantly and therefore; the
problem of managing heterogeneity is increasing.
Heterogeneity can be classified into four categories:
structure, syntax, system and semantic. These
problems that have to be faced are due to the lack of
common ontology, causing semantic differences
between information sources.
Integrating information from web sources starts by
extracting the data from the web pages exported by
the data sources. Although XML is supposed to
reduce the need for this extraction, relatively few
sources are currently available in XML, and legacy
HTML sources will be around for years to come.
Because of the semantic heterogeneity among
rces, merely extracting the data from web pages
is often insufficient to support integration. The
problem is that information might be organized in
different ways with different vocabularies. A
solution to the problem of semantic heterogeneity is
to formally specify the meaning of the terminology
of each system and define a translation between each
system terminology and the shared terminology.
Once a system can extract in
formation from the
various sources and has a semantic description of
these sources, the next challenge is to relate the data
in the sources. So, to integrate data across sources,
an integration system must be able to accurately
determine which data in two different sources refer
to the same entities. This method uses the mapping
between the relations and attribute names among the
schemas of the individual data sources to help
determine the object mappings. In order to address
the problem of semantic information integration, we
apply the idea of ontologies as a tool for data
Lai Lai Thein N., Haymar Saw Hla K. and Lar Thein N. (2005).
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 457-460
DOI: 10.5220/0002555504570460
The integration method is based on a three-step
approach. As described in Figure 1, the first aspect
is tackled by wrappers that lift selected content of
individual information sources to a common data
model. The latter part is done by mediators that
provide the glue. Ontologies simplify the job of
mediators by defining an integrated semantic model
that gives an explicit representation of the semantics
of information components. So, ontology can be
used as a common model for our purpose. Our
intended use of ontology is to describe a data model
and to give semantics to data stored in web pages,
rather than knowledge (Staab, 2002).
Figure 1: Conceptual Flow of Information Integration
Our hypothesis is that many web sites do not
often change their organization of information. New
information is published with a rather steady
structure. Then, if we can recognize how
information is organized, a precise data extraction
can take place (
Maedche, 2002).
There is a tremendous amount of information
available on the web but much of this information is
not in a form that can be easily used by other
application. So, we have developed the technology
for rapidly building wrappers for accurately and
reliably extracting data from unstructured sources.
Documents in HTML do not allow for direct
querying. Therefore, we first convert the HTML
document into XML by following their hierarchical
structure. Possible errors are corrected using HTML
TIDY. Here, we present an approach to extract
information from unstructured documents based on a
source ontology that describes a domain of interest.
Starting with such ontology, we formulate rules to
extract constants and context keywords from
unstructured documents. Once a web page is
transformed into XML using source ontology,
portions of data can be easily used to create local
ontology. We use OWL (Web Ontology Language)
to build ontology (
Embley, 1998).
As case studies to test these ideas for this paper,
we consider the history of Myanmar Pagodas to
extract information. This case is data rich and
narrow in ontological breadth. This also includes
information about name, year, features, enshrined
things and location.
4.1 Data Extraction and Structuring
from Unstructured Documents
In this section, we present the framework to extract
and structure the data in an unstructured document.
As shown in Figure 2, there are three processes in
this framework: an ontology parser, a
constant/keyword recognizer and a structured text
generator. The input is source ontology and a set of
unstructured documents. The output is XML
structured format filtered with respect to the source
ontology and local ontology which is converted from
XML output. In this paper, the only step that
requires significant human intervention is the initial
creation of source ontology for pagoda. However,
once such an ontology is written, it can be applied to
unstructured documents from a wide variety of
sources, as long as these documents correspond to
the given source domain (
Embley, 1998).
In order to extract information from the HTML
code, we need to understand how the data is
presented within the HTML code and analyzes the
page’s tag structure. Given the inputs, parser
module (PM) starts by fetching HTML code of the
web page to be parsed. Then, it analyzes the tag
structure and tries to determine how the data is
presented in this code.
After determining the tag structure of the page,
PM finds the starting point of data and proceeds to
divide the data into fields and records using the pre-
defined record separator tags. After invoking the
parser, the constant/keyword recognizer uses the
source ontology to recognize each regular
expression. The structured text generator uses the
object/relationship/constraints lists to generate XML
format. The ontology generator uses this XML
output to transform local ontology using each
regular expression.
Figure 2: Framework for Ontology Based Information
Extraction and Structuring
<owl:Class rdf:ID="Pagoda">
<owl: onProperty
<owl: onProperty
Figure 3: OWL Source Ontology for Pagoda
While this approach provides a solution for the
problem of extracting information from weakly
structured resources, the problem of integrating
information from different sources remains largely
unsolved (
Cui, 2001).
<StartKing>Anawrahta </StartKing>
Figure 5: Resulting Sample XML File.
<owl:Class rdf:ID="Shwezigon">
<hasStartKing rdf:
<hasFinishKing rdf:
<hasCity rdf: resource="Bagan"/>
<hasState rdf: resource="Mandalay"/>
Figure 6: Sample Output Local Ontology
Information integration is concerned with unifying
data sharing some common semantics but is
originated from unrelated sources. There are a lot of
advantages in the use of ontologies for information
integration. In general, three different directions can
be identified: single ontology approach, multiple
ontologies approach and hybrid approach.
As our approach is based on the hybrid ontology, it
has two main advantages:
(1) New information sources can be added without
the need of modification.
(2) Shared vocabulary and the mappings among the
local ontologies make them be comparables.
In this system, shared ontologies provide a
vocabulary in order to specify the semantics of
information in different sources. Formally, we
define a shared terminology as a set of words and a
partial function over pairs of words [
5.1 Ontologies Alignment Using
Shared Terminology
It has been argued that semantic heterogeneity can
be resolved by transforming information from one
context into another.
A conceptual model of the context of each
information source builds a basis for integration on
the semantic level. In this process, we take the
information about the context of the source
providing a new context description for that entity
within the new information source. Here, we focus
on context transformation by classification [8].
5.2 Mapping between Ontologies
In this system, we apply machine learning
techniques to semi-automatically create semantic
mappings. Since taxonomies are central components
of ontologies, we focus on finding correspondences
among the taxonomies of two given ontologies: for
each concept node in one taxonomy, find the most
similar concept node in the other taxonomy. The
first issue we address is the meaning of similarity
between two concepts. In our approach similarity
measure is based on the joint probability
To match concepts between two taxonomies, we
need a measure of similarity. First, we would like
the similarity measures to be well-defined. Second,
we want the similarity measures to correspond to our
intuitive notions of similarity.
Many practical similarity measures can be
defined based on the joint distribution of the
concepts involved. A possible definition for the
exact similarity measure is
his similarity measure is known as the Jaccard
coefficient. It takes the lowest value 0 when A and B
are disjoint, and the highest value 1 when A and B
are the same concept [
Doan, 2002].
In this paper, we dealt with the problem of
information integration from different sources.
Integrating information from web sources starts by
extracting the data from the Web pages exported by
the data sources. So, we have proposed a
framework for extracting reliable data and to convert
standard form.
By using shared ontology, we also address
terminological semantic heterogeneity in semantic
integration. With the proliferation of data sharing
applications that involve multiple ontologies, the
development of automated techniques for ontology
matching will be crucial to their success.
Bayrak C., Kolukısaoğlu H., 2003. Data Extraction from
Repositories on the Web: A Semi-automatic
Approach, Computer Science Department, University
of Arkansas at Little Rock, Little Rock, AR, U.S.A. ,
SEPTEMBER, Vol. 7, No. 4, pp. 13-23.
Cui Z., Jones D., Brien P. O, 2001. Issues in Ontology-
based Information Integration, Intelligent Business
Systems Research Group Intelligent Systems Lab.
Doan A., Madhavan J., Domingos P., Halevy A., 2002.
Learning to Map between Ontologies on the Semantic
Web. In Proceedings of the World-Wide Web
Conference (WWW-2002), pages 662-673, ACM
Embley D. W., Campbell D. M., Smith R. D., and Liddle
S. W., 1998. Ontology-based extraction and
structuring of information from data-rich unstructured
documents. In International Conference on
Information and Knowledge Management (CIKM).
Gruber T.R., 2003. A Translation Approach to Portable
Ontology Specification, Knowledge Acquisition, 199–
Hendler J., Lee T.B., Miller E., 2002. Integrating
Application on the Semantic Web, Journal of the
Institute of Electrical Engineers of Japan, Vol. 122
Maedche A., 2002. Tying Up Information Integration and
Web Site Management by Ontologies, IEEE Data
Engineering Bulletin.
Stuckenschmidt H., 2002.
Information Sharing on the
Semantic Web, AI Department, Vrije University,
Amsterdam, De Boelelaan 1081a, 1081HV
Amsterdam, The Netherlands.
Soderland S., 1998. Learning information extraction rules
for semi-structured and free text,
www.cs.washington.edu/homes/soderlan/ WHISK.
Staab S., Maedche A., 2001. Comparing Ontologies
Similarity Measures and a Comparison Study, Internal
Report No. 408.
Staab S., 2002. The Semantic Web-New Ways to Present
and Integrate Information, Institute of Applied
Informatics and Formal Description Methods (AIFB),
University of Darlsruhe.
Wache H., Vögele T., Visser U., Stuckenschmidt H.,
Schuster G., Neumann H., Hübner S., 2001.
Ontology-Based Integration of Information: A Survey
of Existing Approaches.