=Paper=
{{Paper
|id=Vol-239/paper-2
|storemode=property
|title=Towards an Architecture of Ontological Components for the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-239/paper2.pdf
|volume=Vol-239
|dblpUrl=https://dblp.org/rec/conf/caise/MustaphaAB06
}}
==Towards an Architecture of Ontological Components for the Semantic Web==
https://ceur-ws.org/Vol-239/paper2.pdf
1036 Web Information Systems Modeling
Towards an Architecture of Ontological Components
for the Semantic Web
Nesrine Ben Mustapha1, Marie-Aude Aufaure2, and Hajer Baazhaoui-Zghal1
1 Riadi Lab., ENSI Campus Universitaire de la Manouba, 2010 Tunis, Tunisie
{nesrine.benmustapha, hajer.baazaouizghal} @riadi.rnu.tn
2 Supelec, Computer Science Department, Plateau du Moulon,
91 192 Gif sur Yvette, France
Marie-Aude.Aufaure@Supelec.fr
Abstract. This paper presents an architecture of ontological components for the
Semantic Web. Many methods and methodologies can be found in the litera-
ture. Generally, they are dedicated to particular data types like text, semi-
structured data, relational data, etc. Our work deals with web pages. We first
study the state of the art of methodologies defined to learn ontologies from
texts. Then, our architecture of ontological components for the Semantic web is
defined, in order to improve knowledge discovery from the web. At least, we
detail the incremental construction of the domain ontology component and we
present the general conceptual model associated to it.
1 Introduction
The volume of available information on the web is growing exponentially. Conse-
quently, integration of heterogeneous data sources and information retrieval become
more and more complex. Adding a semantic dimension to web pages is a response to
this problem and is known as the semantic web [1]. Ontologies can be seen as a fun-
damental part of the semantic web. They can be defined as an explicit, formal specifi-
cation of a shared conceptualization [2]. Indeed, their use allows facilitating web
information retrieval, domain knowledge sharing as well as knowledge integration.
Meanwhile, building ontology manually is a long and tedious task. Many approaches
for learning ontologies can be found in the literature. Section 2 synthesizes such on-
tology learning methodologies. We present our architecture of ontological compo-
nents for the semantic web, integrated in a customizable ontology building environ-
ment, in section 3. At least, we conclude and give some perspectives for this work.
2 Ontology building methodologies
Methodologies for ontology building can be classified according to the use or not of
a-priori knowledge (such as thesaurus, existing ontologies, etc.) and to learning meth-
WISM'06 1037
ods. The first ones were dedicated to enterprise ontology development [3] [4] and
manually built. Then, methodologies for building ontologies from scratch were de-
veloped. They do not use a-priori knowledge. An example of such a methodology is
OntoKnowledge [5] which proposes a set of generic techniques, methods and princi-
ples for each process (feasibility study, initialization, refinement, evaluation and
maintenance). Some research work is dedicated to collaborative ontology building
such as CO4 [6] and (KA)2 [7]. Another research area deals with ontology reengi-
neering [8]. Learning methodologies can be distinguished according to their input
data type: texts, dictionaries [9], knowledge bases [10], relational [11], [12] and semi-
structured data [13], [14], [15].
In the following section, we focus on the general dimensions implied in ontology
learning. Section 2.3 deals with ontology learning from texts, including web pages.
2.1 General dimensions implied in ontology learning
The existing methods can be distinguished according to the following criteria: learn-
ing sources, the type of ontology to build, techniques used to extract concepts, rela-
tionships and axioms, and existing tools. The most recent methodologies generally
use a priori knowledge such as thesaurus, minimal ontology, other existing ontolo-
gies, etc. Each one proposes different techniques to extract concepts and relation-
ships, but not axioms. These axioms can represent constraints but also inferential
domain knowledge. As for instance extraction, we can find techniques based on first
order logic [32], on Bayesian learning [33], etc. We have to capitalize the results
obtained by the different methods and to characterize existing techniques, their prop-
erties and how we can combine them. The objective of this section is to synthesize the
characteristics of these methods in order to expose our problematic and to argue our
choices.
Learning ontologies is a process requiring at least the following development
stages:
- Knowledge sources preparation (textual corpus, collection of web docu-
ments), eventually using a priori knowledge (ontology with a high-level ab-
straction, taxonomy, thesaurus, etc.),
- Data sources preprocessing,
- Concepts and relationships learning,
- Ontology evaluation and validation (generally done by experts)..
The ontology is built according to the following dimensions:
- Input type (data sources, a priori knowledge existence or not, …),
- Tasks involved for preprocessing : simple text linguistic analysis, document
classification, text labeling using lexico-syntactic patterns, disambiguating,
etc.,
- Learned elements : concepts, relationships, axioms, instances, thematic roles,
- Learning methods characteristics: supervised or not, classification, clustering,
rules, linguistic, hybrid,
- Automation level: manual, semi-automatic, automatic, cooperative,
1038 Web Information Systems Modeling
- Characteristics of the ontology to build: structure, representation language,
coverage,
- Usage of the ontology and users’ needs [16].
2.2 Learning ontologies from texts
The proposed approaches can be classified according to the technique used, namely
linguistic, lexico-syntactic patterns extraction, clustering or classification, and hybrid
ones. The input data is constituted by linguistic resources like a list of terms and rela-
tionships. The huge volume of these data, their quality and relevance has to be taken
into account by filtering methods. These methods can be guided by an expert (knowl-
edge acquisition from texts) or automatic (text mining, learning).
Linguistic-based techniques include lexical, syntactic and semantic texts analysis.
The objective is to extract a conceptual model of a domain. We can quote two main
methodologies defined by [16] and [17]. The methodology defined by [18] intends to
extract knowledge from technical documents. Two hypothesis are given by the au-
thors: the first one states that the ontology designer has a good knowledge of the
application domain and can determine the relevant terms, while the second one states
that he also have a precise idea of the ontology usage. This methodology analyses a
corpus with tools appropriate for natural language automatic processing and linguistic
techniques and extracts terms and relationships. The normalization step concerns the
mapping between natural language and a formal language. The semantic interpreta-
tion of the texts is managed by usage and expertise. Semantic relationships are ob-
tained from lexical relationships, and the concepts hierarchy is built using the seman-
tic relationships. The formalization step automatically translates the ontology into a
given format like RDF, OWL, etc.
In these linguistic approaches, lexico-syntactic patterns are manually defined by
linguists. Some research work has been proposed to automatically extract lexico-
syntactic patterns. [19] starts from an existing ontology and extract a set of pairs of
concepts linked by relationships, in order to learn hyponymy relationships and pro-
duce lexico-syntactic patterns. These ones are used to discover other relationships,
based on the learned patterns, between the concepts of the existing ontology. This
approach is used to extend an existing lexical ontology. [20] proposes to combine the
previous approach with contextual signatures to improve the classification of new
concepts. The KAT system (Knowledge acquisition from Texts) [21] includes four
steps: learning new concepts, classification, learning relationships and ontology
evaluation. Concept classification consists in analyzing words that appears in the
expression associated to a candidate concept: [word, seed concept], where “word”
can be a noun or an adjective. This classification states that the concept [word, seed
concept] subsumes the seed concept that is equivalent to add a hyponymy relation-
ship.
These techniques, based on lexico-syntatic patterns learning, lead to good results
for learning hyponymy relationships. In the meantime, some problems appear like
terms polysemy or errors produced that are dependant from the corpus. The use of
classification techniques like hierarchical or conceptual clustering is a way to solve
WISM'06 1039
these problems. The methodology proposed by [22] consists in classifying documents
into collections related to words sense, using a labeled corpus and Wordnet. Then for
each collection, the relative frequencies are extracted and compared to the other col-
lections. Topic signatures are computed and compared to discover shared words. This
methodology is dedicated to enrich concepts of existing ontologies by analyzing web
texts. Other methods [23] [24] combine linguistic techniques and clustering to build
or extend an ontology.
Some research work is done to study the distribution of words in texts to improve
concepts clustering by the way of new similarity measures. DOODLE II, an extension
of DOODLE [25], is an environment for the rapid development of domain ontologies.
It is based on the analysis of lexical co-occurrences and the construction of a multi-
dimensional space of words [26]. This approach extracts taxonomic relationships
using Wordnet and non taxonomic relationships learning by searching association
rules and extracting pairs of similar concepts using the words multidimensional space.
2.3 Web-based ontology learning
Our main objective is to define an approach to build ontologies for the semantic web.
This kind of ontology, closely linked to the web usage, has to integrate the dynamic
aspects of the web. In this section, we present some approaches defined specifically
for the web.
Many propositions have been done to enrich an existing ontology using web docu-
ments [22][27]. However, these approaches are not specifically dedicated to web
knowledge extraction.
The approach proposed by [28] attempts to reduce the terminological and concep-
tual confusion between members of a virtual community. Concepts and relationships
are learned from a set of web sites using the Ontolearn tool. The main steps are: the
terminology extraction from web sites and web documents data warehouse, the se-
mantic interpretation of terms and the identification of taxonomic relationships.
Some approaches transform html pages into hierarchical semantic structured en-
coded in XML, taking into account html regularities [29].
Finally, we can also point out some approaches only dedicated to ontology con-
struction from web pages without using any a priori knowledge.
The approach described in [30] is based on the following steps: (1) extract some
keywords representative of the domain, (2) find a collection of web sites related to the
previous keywords (using for example Google), (3) exhaustive analysis of each web
site, (4) the analyzer searches the initial keywords in a web site and finds the preced-
ing and following words; these words are candidates to be a concept, (5) for each
selected concept, a statistical analysis is performed based on the number of occur-
rences of this word in the web sites and at last, (6) for each concept extracted using a
window around the initial keyword, a new keyword is defined and the algorithm
recursively iterates.
In [31], a method is proposed to extract domain ontology from web sites without
using a priori knowledge. This approach takes benefit from the web pages structure
and defines a contextual hierarchy. The data preprocessing is an important step to
1040 Web Information Systems Modeling
define the more relevant terms to classify. Weights are associated to the terms accord-
ing to their position in this conceptual hierarchy. Then, these terms are automatically
classified and concepts are extracted.
3 Ontological components for the Semantic Web
Starting from the state of the art in ontology learning, we propose a hybrid approach
to build domain ontology; our objective is to increase the capability of this ontology
to specify and extract web knowledge in order to contribute to the semantic web.
Analyzing the web content is a difficult task relative to relevance, redundancies and
incoherencies of web structures and information. Moreover, semantic similarity
measures highly depends on the quality of data, and the complexity of algorithms
such as conceptual clustering increase with the volume of data. For these reasons,
proposing an approach to build automatically an ontology still remains utopian.
Fig. 1. The cyclic relation between web mining, semantic web and ontology (extracted
from [34]
Our approach is based on the cyclic relation between web mining, semantic web
and ontology building as stated in [34] and resumed in figure 1. Our proposal is based
on the following statements: (1) satisfy the fact that the ontology is useful to specify
and extract knowledge from the web, (2) link the semantic content within the web
documents structure, and (3) combine linguistic and learning techniques taking into
account the scalability and the evolution of the ontology. Our ontology is produced
using web mining techniques. We mainly focus on web content and web structure
WISM'06 1041
mining. Building this ontology leads us to solve two main problems. The first one is
relative to the heterogeneity of web documents structure while the second one is more
technical and concerns technical choices to extract concepts, relationships and axioms
as well as the selection of learning sources and the scalability. We propose an archi-
tecture of ontological components to represent the domain knowledge, the web sites
structure and a set of services. These ontological components (figure 3) are integrated
into a customizable ontology building environment (figure 2).
3.1. Architecture
Learning ontologies from web sites is a complex task because web pages can contain
more images, hypertext and frames than text. Learning concepts is a task that needs
texts able to explicitly specify the properties of a particular domain. A positive point
in the context of learning ontologies from web pages is that web sites structure can be
exploited by web mining techniques.
Starting from the state of the art (section 2.2), we can say that no learning method to
extract concepts and relationships is better (in most cases, the ontology evaluation is
manually done). For these reason, we propose a customizable ontology building envi-
ronment as depicted in figure 2. The customization takes into consideration the gen-
eral dimensions defined in section 2.1.
In this environment, we propose a set of interdependent ontologies to build a Web
knowledge base on a particular domain. These ontologies are related to the content,
structure and services semantics. Such environment is composed by the following
modules: (1) Learning data sources module, (2) Ontological components enrichment
module, (3) Linguistic module, (4), Ontological components editor. Theses modules
uses resources such as: data warehouse in XML format, linguistic resources (Word-
net, general ontologies, thesaurus, patterns collection), web knowledge bases, consti-
tuted by a set of web documents, their structure and associated services, as depicted
in figure 3. We distinguish two types of actors in the environment, namely, software
actors as the miner agents and human actors as the linguist expert, the domain experts
and the system adminstrators. The functionnalities provided by this system are in-
spired from the web discovery processus, starting from the data sources pretreatment
to the knowledge discovery, including the datawarehouse building. Besides, this
environment intends to relate domain ontologies, ontologies of services and web
structure ontologies in order to build and enrich web knowledge bases of the domain.
1042 Web Information Systems Modeling
Fig. 2. Customizable ontology building environment
We distinguish three ontologies, namely a generic ontology of web sites structures,
a domain ontology and a service ontology.
The generic ontology of web sites structure contains a set of concepts and relation-
ships allowing a common structure description of HTML, XML and DTD web pages.
This ontology enables to learn axioms that specify the semantic of web documents
patterns. The main objective is to ease structure web mining knowing that the results
can help to populate the domain ontology. The ontology of Web sites structure is
useful to improve the extraction of the concepts and the relevant relations of the do-
main by studying semantics of the various markup elements of the languages HTML
or XML. We can find in different sites the same contents but presented by different
manners which indicates the degree of importance granted to some information ac-
cording to the context. The second perspective is to translate a semantic relation (e.g.
“part-of” relationship, etc.) into a set of adequate markup elements (e.g. a relationship
between a concept and instances can be represented by a list, a markup “Small” be-
fore a word can express that this word is less important than the previous one). Min-
ing the structure allows us to extract some regularity from which we can define an
axiom. Indeed, the axioms of the structure ontology help in elaborating the mapping
of HTML markups to the semantic relations inferred. Such an ontology may also
adjust the gap existing between the physical and logical structure of a document Web.
WISM'06 1043
Fig. 3. Architecture of Ontological Components for the Semantic web
The domain ontology is divided into three layers according to their level of ab-
straction. The first level is a lexical one: this layer specifies high-level lexical knowl-
edge which can help discovering lexico-syntactic patterns. The lexical knowledge
covers the concepts, relationships and general axioms of the central layer of our do-
main ontology. The last layer is more operational and, additionally to concepts and
relationships instances, also contains a set of axioms specifying domain knowledge.
These axioms are incrementally enhanced by web content and web structure mining.
The ontology of services is defined starting from the concept of task ontology [35].
In our web context, we speak of web services instead of tasks. This ontology specifies
the domain services and will be useful to map web knowledge into a set of interde-
pendent services. This is built from the central layer of our domain ontology in order
to constitute a set of domain services, and web pages constitute the instances of these
services. This ontology is a macroscopic view of the domain and is hierarchically
structured: the upper level is the root service while the leaves are elementary tasks for
which a triplet “concept-relation-concept” belonging to the domain ontology is asso-
ciated. These three ontological components are interdependent where the axioms
included in an ontology are used to enhance another ontology component. As an
example, if we consider the axiom defined on the structure ontology in figure 3, we
can say that if the label of a “combobox” is a concept of the domain ontology then all
1044 Web Information Systems Modeling
the elements of this structure are instances of this concept. This axiom is used to
populate the ontology domain. Meanwhile, these ontologies differ from their use. The
domain ontology is used to specify the domain knowledge. The service ontology
specifies the common services that can be solicited by web users and can be attached
to several ontologies defined on subparts of the domain. As we said previously, the
axioms of the structure ontology are used to extract instances of the domain ontology.
The structure of our domain ontology is more complex than usual domain ontolo-
gies. This particularity is oultlined in figure 4.
Fig. 4. Domain ontology abstract conceptual model
This figure shows the abstract concepts from which the learned concepts and rela-
tionships will be inserted. Confidence weight according to the learning technique
used, are associated to concepts and relationships in this ontology. This abstract
model is divided into two levels. The linguistic level specifies how a concept was
extracted using linguistic techniques, namely the lexicosyntactic patterns [19, 20] or
the syntactic frame learning techniques [24]. The domain concepts and relationships
are derived from the root of the ontology. They are referenced by verbs or nominal
groups. Concepts are extracted by learned lexicosyntaxiques patterns. Such concepts
can also be a frequent object or a subject of a syntactic frame. A syntactic frame is a
triplet (< subject > < verb > < Object >). The extracted concepts will be weighted by
the frequency of the learned patterns in the corpus. Relationships are extracted if a
syntactic frame is frequent between two concepts having a close semantic distance.
The similarity measures are extensible. We distinguish several semantic distances
WISM'06 1045
according to the chosen measure. We quote for example the cosine between two vec-
tors of concepts in the word space and the measure of the closest neighbors between
two concepts in the graph of the concepts. As an example, a concept can be learned
using a lexico-syntactic pattern. In this case, the weight associated to this concept is
relative to the appearance frequency of the concept satisfying the pattern. These
weights are updated at each step dedicated to the ontology enhancement. The seman-
tic distance in the conceptual model is related to the similarity measure between two
concepts. This measure is computed from a multidimensional space of words.
3.2. Building the domain ontology
In this section, we focus on the domain ontology extraction. Our strategy (figure 5) is
based on three steps. The first one is the initialization step. The second one is an in-
cremental learning process based on linguistic and statistic techniques. The last one is
a learning step based on web structure mining. We now define them.
The initialization is based on the following steps:
- The design and manual building of a minimal ontology related to the domain;
this construction is based on concepts and relationships of Wordnet,
- Composition of concepts and relationships learning sources:
o Web search of documents related to our domain using the concepts
defined in the minimal ontology as requests,
o Classification of these web documents,
o Composition of a textual corpus containing a set of phrases in which
we can find at least one concept of the minimal domain ontology,
o Composition of a corpus of HTML and XML documents indexed by
their URL.
Each iteration of the second stage includes two steps. The first one (Procedure A)
is defined by the following tasks:
- Enrichment of the ontology with new concepts extracted from semi-structured
data found in the web pages (XML, DTD, tables),
- Construction of a word space [36] based on the concepts of the minimal do-
main ontology,
- Lexico-syntactic patterns learning based on the method defined in [20] (by
combining lexico-syntactic patterns and topic signatures); these patterns are
related to non taxonomic relationships between the concepts of the minimal
ontology,
- Lexico-syntactic patterns learning to extract synonymy, hyponymy and part-of
relationships (lexical layer of the domain ontology),
- Similarity matrix building: this matrix allows computing the similarity be-
tween pairs of concepts found in the multidimensional space word.
The second step (Procedure B) consists in:
- Update the textual corpus and the web documents collection by searching
them according to the concepts defined in the minimal ontology,
- New concepts and non taxonomic relationships extraction by the application
of lexico-syntactic patterns,
1046 Web Information Systems Modeling
- Attribution of a weight for each extracted relationship relative to the fre-
quency of the relationships that apply the lexico-syntactic pattern,
- Update the minimal ontology.
Each iteration can be validated by the domain expert. This process is incremental: we
repeat the procedures A and B until we do not want to integrate new data.
The last stage consists in an enrichment of the structure ontology and an extraction
of structure patterns for each relationship of the domain ontology. The objective is to
ease instances extraction using the tagged structure of web pages.
The implementation of this strategy is still in progress.
Fig. 5. Learning domain ontology strategy
We have realized a little case study to identify the main characteristics for learning
ontology from web sites. From this case study, we have concluded that structure min-
ing techniques are useful for the extraction of non taxonomic and sub-part of relation-
ships as well as for the construction of services ontology. Building a words space is
also useful to compute the similarity between concepts but highly depends on web
sites corpus. The techniques used are dependant from the learning sources. For this
reason, defining a flexible environment should help satisfying the personalization of
ontology learning.
WISM'06 1047
4 Conclusion and perspectives
The methodologies for building ontologies described in this paper are fairly comple-
mentary. Indeed, methodologies defined to build ontologies from scratch are oriented
towards ontological engineering and ontology life-cycle and are based on information
systems development methodologies. Learning methodologies try to give a response
to the time-consuming manual ontology building task. Learning techniques can be
either numeric or symbolic. They have been exploited to semi-automate some founda-
tion tasks such as concept hierarchy building, taxonomic relationships extraction, non
taxonomic relationships learning, etc. All these research works constitute a methodo-
logical toolbox which can be used to semi-automate ontology construction. We have
to take into account previous experiences and to solve cited problems. Let us now
outline our contributions in the field of ontology learning and building. Firstly, we
conceived an ontological architecture based on a semantic triplet, namely, semantics
of the contents, the structure and the services of a domain. So, we take into account
both the content and the structure, and a set of services are based upon the domain
ontology concepts. The second point is that all the ontologies defined in our architec-
ture contain the weightings of their concepts, relationships and axioms according to
their sources. These ontologies could be handled by the inference engine. Concepts
and the relationships can be represented by facts and predicates. Axioms are the in-
ference rules allowing the insertion of new facts and predicates.
At least, lexico-syntactic patterns will be stored in the linguistic layer of the do-
main ontology, so that such patterns will allow the specification of the axioms. These
ones define the existing types of relationships, in order to identify such relationships
in the web pages. In, our case, axioms are the basis of the incremental enrichment of
the ontology. Moreover, the data sources are incrementally updated to satisfy a good
semantic coverage of the ontology.
Our perspective is to use semantic web mining techniques and to restructure web
pages in order to implement an adaptive web based on the semantic structure, content
and services. Our framework presented in this paper is based on an ontological com-
ponents architecture integrated into a customizable ontology construction environ-
ment. We first focus on the automation of the domain ontology construction. Then,
we will implement the other ontological components. The final goal is to build a
knowledge web base on a specific domain. From our case study, we can conclude that
we must take into account various learning sources (like on-line dictionaries) and
structure regularities in web sites to go further in the implementation.
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