CHALLENGES IN DISTRIBUTED INFORMATION SEARCH IN
A SEMANTIC DIGITAL LIBRARY
Antonio Martín and Carlos León
Departamento de Tecnología Electrónica, Seville University, Avda. Reina Mercedes S/N, Seville, Spain
Keywords: Ontology, Semantic Web, Retrieval, Case-based Reasoning, Digital Library, Knowledge Management.
Abstract: Nowadays an enormous quantity of heterogeneous and distributed information is stored in the current digital
libraries. Access to these collections poses a serious challenge, however, because present search techniques
based on manually annotated metadata and linear replay of material selected by the user do not scale
effectively or efficiently to large collections. The artificial intelligent and semantic Web provides a common
framework that allows knowledge to be shared and reused. In this paper we propose a comprehensive
approach for discovering information objects in large digital collections based on analysis of recorded
semantic metadata in those objects and the application of expert system technologies. We suggest a
conceptual architecture for a semantic and intelligent search engine. OntoFAMA is a collaborative effort
that proposes a new form of interaction between people and Digital Library, where the latter is adapted to
individuals and their surroundings. We have used Case Based-Reasoning methodology to develop a
prototype for supporting efficient retrieval knowledge from digital library of Seville University.
1 INTRODUCTION
In the traditional search engines the information is
treated as an ordinary database that manages the
contents and positions. The result generated by the
current search engines is a list of Web addresses that
contain or treat the pattern. The useful information
buried under the useless information cannot be
discovered. It is disconcerting for the end user.
In the historical evolution of digital libraries, the
mechanisms
for retrieval of scientific literature have
been particularly
important. There are a lot of
researches on applying these new technologies into
current Digital Libraries information retrieval
systems, but no research addresses the semantic and
intelligent artificial issues from the whole life cycle
and architecture point of view.
Although search engines have developed
increasingly effective, information overload
obstructs precise searches. We study improving the
efficiency of search methods to search a distributed
data space like a Digital Library. It incorporates
semantic Web and artificial intelligent technologies
to enable not only precise location of digital library
resources but also the automatic or semi-automatic
learning. Our approach for realizing content based
search and retrieval information implies the
application of the Case-Based Reasoning (CBR)
technology (Toussaint and Cheng, 2006). Our work
differs from related projects in that we build an
ontology-based contextual profiles and we introduce
an approaches used metadata-based in ontology
search and expert systems (Warren, 2005).
The contributions are divided into next sections.
In the next section, short descriptions of important
aspects in Digital Library domain, the general
overview about our prototype architecture and
current work in it are reported. Then we summarize
its main components and describe how can interact
Intelligent Artificial and Semantic Web to
enhancement a search engine. Next we study the
CBR framework jColibri and its features for
implementing the reasoning process over ontologies
(GAIA, 2009). Finally we present the results of our
ongoing work on the adaptation of the framework
and we outline the future works.
2 MOTIVATION
In the historical evolution of digital libraries, the
mechanisms for retrieval of scientific literature have
been particularly important. These network
information systems support search and display of
items from organized collections. The semantic Web
provides a common framework that allows
495
Martín A. and León C. (2010).
CHALLENGES IN DISTRIBUTED INFORMATION SEARCH IN A SEMANTIC DIGITAL LIBRARY.
In Proceedings of the 5th International Conference on Software and Data Technologies, pages 495-498
DOI: 10.5220/0002933104950498
Copyright
c
 SciTePress

knowledge to be shared and reused across
community libraries and semantic searchers (Sure &
Studer, 2005).
This begets new challenges to docent community
and motivates researchers to look for intelligent
information retrieval approach and Ontologies that
search and/or filter information automatically based
on some higher level of understanding are required.
We make an effort in this direction by investigating
techniques that attempt to utilize ontologies to
improve effectiveness in information retrieval (Ding,
2004).
In this paper we study architecture of the search
layer in this particular dominium, a web-based
catalogue for the University of Seville. The Seville
Digital Library (SDL) provides tools that support the
construction of online information services for
research, teaching, and learning. SDL include
services to effectively share their materials and
provide greater access to digital content (Witten &
Bainbridge, 2003).
3 ONTOFAMA ARCHITECTURE
The architecture of our system named OntoFAMA is
shown in figure 1, which mainly includes three
parts: search engine, ontology knowledge base, and
intelligent user interface.
Figure 1: System architecture of OntoFAMA.
Inference engine contains a CBR component that
automatically searches for similar queries-answer
pairs based on the knowledge that the system
extracted from the questions text (Bridge and
G¨oker, 2006). We used a Case-Based Reasoning
(CBR) shell, software that can be used to develop
several applications that require cased-based
reasoning methodology. In this work we analyzed
the CBR object-oriented framework development
environments JColibri (Díaz-Agudo and González-
Calero, 2007).
OntoFAMA system uses its internal knowledge
bases and inference mechanisms to process
information about the electronic resources in a
Digital Library. At this stage we consider to use
ontology as vocabulary for defining the case
structure like attribute-value pairs. Ontology will be
considered as knowledge structure that will identify
the concepts, property of concept, resources, and
relationships among them to enable share and reuse
of knowledge that are needed to acquire knowledge
in a specific search domain.
The acceptability of a system depends to a great
extent on the quality of this user interface
component. The interfaces provides for browsing,
searching and facilitating Web contents and services.
The objective of profile intelligence has focused on
creating of user profiles: Staff, Alumni,
Administrator, and Visitor. The user interface helps
to user to build a particular profile that contains his
interest search areas in the digital library domain.
4 CASE-BASED REASONING
Case-Based Reasoning is a problem solving
paradigm that solves a new problem, in our case a
new search, by remembering a previous similar
situation and by reusing information and knowledge
of that situation. A new problem is solved by
retrieving one or more previously experienced cases,
reusing the case, revising. The case-based reasoning-
cycle in our system may be described by the
following processes.
 Retrieval. Main focus of methods in this category
is to find similarity between cases.
 Reuse: a complete design where case-based and
slot-based adaptation can be hooked is provided.
 Revise the proposed solution if necessary.
 Retain the new solution as a part of a new case.
CBR case data could be considered as a portion
of the knowledge (metadata) about an OntoFama
object. Every case contains both a solution pointer
and a problem description used for similarity
assessment. Although CBR claims to reduce the
effort required for developing knowledge-based
systems substantially compared with more
traditional Artificial Intelligence approaches, the
implementation of a CBR application from scratch is
still a time consuming task.
In this study we used the CBR object-oriented
framework development environments JColibri.
jColibri is and open source framework and their
interface layer provides several graphical tools that
help users in the configuration of a new CBR
system. Another decision criterion for our choice is
the easy ontologies integration. jColibri affords the
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
496

opportunity to incorporate ontology in the CBR
application to use it for case representation and
content-based reasoning methods to assess the
similarity between them.
5 ONTOLOGY DESIGN
We need a vocabulary of concepts, resources and
services for our information system described in the
scenario requires definitions about the relationships
between objects of discourse and their attributes. We
have proposed to use ontology together with CBR in
the acquisition of an expert knowledge in a specific
domain. We integrated three essential sources to the
system: electronic resources, catalogue and personal
Data Base.
The W3C defines standards that can be used to
design an ontology (W3C, 2009). We wrote the
description of these classes and the properties in
RDF semantic markup language. We choose Protégé
as ontology editor, which supports knowledge
acquisition and knowledge base development
(Protégé, 2009). It is a powerful development and
knowledge-modelling tool with an open architecture.
Protégé uses OWL and RDF as ontology language to
establish semantic relations. The ontology and its
sub-classes are established according to the
taxonomies profile, as shown in figure 2.
Figure 2: Class hierarchy for the OntoFama ontology.
Our ontology can be regarded as quaternion
OntoSearch:={profile, collection, source) where
profiles represent the user kinds, resources contains
all the services and resources of the digital library
and matter cover the different information sources:
catalogue, history fond, intranet, Web, etc.
After ontology is established, we need to add
enough initial instances and item instances to
knowledge base. For this purpose we choose a
certain item, and create a blank instance for item.
Then the domain expert, in this case the librarian
fills blank units of instance according the domain
knowledge (Horridge and Knublauch, 2004). Then
the library of cases (the “case base”) is generated
from a file store where each case is represented with
RDF syntax. 1100 cases were collected for user
profiles and their different resources and services.
Each case contains a set of attributes concerning
both metadata and knowledge.
6 CONVERSATION INTERFACE
As we have seen in previous sections our system has
a graphical user interface for determining initial user
requirements in search. Rather than building static
user profiles, contextual systems try to adapt to the
user’s current search. OntoFAMA monitors user's
tasks, anticipates search-based information needs,
and proactively provide users with relevant
information. This configuration contains the user
requirements most typically described the relative
needs, tasks, and goals of the user for an individual
search.
The user enters query commands and the system
asks questions during the inference process. Besides,
the user will be able to solve new searches for which
he has not been instructed, because the user profiles
what he has learnt during the previous searchers.
The user inputs the keywords in the user profile
interface. Suppose the user is looking for some
resource about “Computer Science electronic
resource” in the library digital domain of Seville.
The required resources should contain some
knowledge about “Computer Science” and related
issues. After searching, some resources are returned
as results, which are shown in figure 3.
Figure 3: Search engine results page.
The results include a list of web pages with titles,
a link to the page, and a short description showing
CHALLENGES IN DISTRIBUTED INFORMATION SEARCH IN A SEMANTIC DIGITAL LIBRARY
497

where the keywords have matched content within
the page.
7 EVALUATION
Experiments have been carried out in order to test
the efficiency of artificial intelligent and ontologies
in retrieval information in a digital library. For our
experiments we considered 50 users with different
profiles. So that we could establish a context for the
users, they were asked to at least start their essay
before issuing any queries to OntoFAMA. They
were also asked to look through all the results
returned by OntoFAMA before clicking on any
result. We compared the top 10 search results of
each keyword phrase per search engine. Our
application recorded which results on which they
clicked, which we used as a form of implicit user
relevance in our analysis. We have agreed different
values to measure the quality of retrieved
documents, excellent, good, acceptable and poor.
In each experiment we report the average rank of
the user-clicked result for our baseline system,
Google and for our search engine OntoFAMA. Then
we calculated the rank for each retrieval document
by combining the various values and comparing the
total number of extracted documents and documents
consulted by the user (table 1). We can observe the
best final ranking was obtained for our prototype
OntoFAMA and an interesting improvement over
the performance of Google.
Table 1: Analysis of retrieved documents relevance.
Excellent Good
Acceptable
Poor
OntoFAMA 5,5 %
39,3 % 40,6 % 14,4 %
Google 2,7 %
31 % 44,8 % 21,3 %
8 CONCLUSIONS
We have investigated how the semantic technologies
can be used to provide additional semantics from
existing resources in digital libraries. For this
purpose we presented a system based in ontology
and artificial intelligent architecture for knowledge
management in the Seville Digital Library. Our
study addresses the main aspects of a semantic Web
information retrieval system architecture trying to
answer the requirements of the next-generation
semantic Web user.
To put our aims into practice we should first of
all develop the domain ontology and study how the
content-based similarity between the concepts typed
attributes could be assessed in CBR system. A
dedicated inference mechanism is used to answer
queries conforming to the logic formalism and terms
defined in our ontology. We have been working on
the design of entirely ontology based structure of the
case and the development of our own reasoning
methods in jColibri to operate with it. Furthermore
an intelligent agent was illustrated for assisting the
user by suggesting improved ways to query the
system on the ground of the resources in a Digital
Library according to his own preferences, which
come to represent his interests.
Future work will concern the exploitation of
information coming from others libraries and
services and further refine the suggested queries, to
extend the system to provide another type of
support, as well as to refine and evaluate the system
through user testing.
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