EXPERT KNOWLEDGE MANAGEMENT

BASED ON ONTOLOGY IN A DIGITAL LIBRARY

Antonio Martín and Carlos León

Departamento de Tecnología Electrónica, Seville University, Avda. Reina Mercedes S/N, Seville, Spain

Keywords: Ontology, Web Services, Case-based Reasoning, Digital Library, Knowledge Management, Semantic Web.

Abstract: The architecture of the future Digital Libraries should be able to allow any users to access available

knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user,

there is a great deal of useless information in addition to the substantial amount of useful information. The

goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic

searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and

Semantic Web have provided both new possibilities and challenges to automatic information processing in

search engine process. The major research tasks involved are to apply appropriate infrastructure for specific

digital library system construction, to enrich metadata records with ontologies and enable semantic

searching upon such intelligent system infrastructure. We study improving the efficiency of search methods

to search a distributed data space like a Digital Library. This paper outlines the development of a Case-

Based Reasoning prototype system based in an ontology for retrieval information in the Digital Library

University of Seville. The results demonstrate that by incorporating ontologies and the use of expert systems

into the search process, the effectiveness of the information retrieval is enhanced.

1 INTRODUCTION

In the current digital libraries and Internet the access

to knowledge depends of the relationship between

people, tools and communication devices used.

Although search engines have developed

increasingly effective, information overload

obstructs precise searches. 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. Thus, sometimes

it takes a long time to search for needed information.

Artificial Intelligent and ontology-based search,

from the semantics perspective, provides added

values in searching over documents which are

semantically related. Despite large investments and

efforts have been made, there are still a lot of

unsolved problems. 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 (Govedarova & Stoyanov,

2008). Our work differs from related projects in that

we build an ontology-based contextual profile and

we introduce an approach used metadata-based in

ontology search and expert systems.

We focus our discussion on case indexing and

retrieval strategies and provide a perception of the

technical aspects of the application. For this reason

we are improving representation by incorporating

more metadata from within the information (Ding,

2004). Our approach for realizing content based

search and retrieval information implies the

application of the Case-Based Reasoning (CBR)

technology.

The paper is organized as follows. Next section

describes the setting of Digital Library domain, the

research problems and current work in it. Then we

present the Ontology design process. Section 3

provides a general overview about our prototype

architecture. 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). Section 4

291

Martín A. and León C. (2010).

EXPERT KNOWLEDGE MANAGEMENT BASED ON ONTOLOGY IN A DIGITAL LIBRARY.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

291-298

DOI: 10.5220/0002906702910298

 SciTePress

exemplifies the usage of jCOLIBRI to create the

system OntoFAMA, sets out our motivation for

choosing this CBR framework, and presents the

results of our ongoing work on the adaptation of the

framework. Finally we outline the conclusions and

future works.

2 DIGITAL LIBRARY

DOMINIUM

The Seville Digital Library (SDL) is dedicated to the

production, maintenance, delivery, and preservation

of a wide range of high-quality networked resources

for scholars and students at University and

elsewhere. 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).

In this paper we study architecture of the search

layer in this particular dominium, a web-based

catalogue for the University of Seville. For this

purpose we present an ontology-based web

architecture for knowledge management in a Digital

Library (Stuckenschmidt & Harmelen, 2001). It

incorporates ontologies and Artificial Intelligent to

enable not only precise location of Web resources

but also the automatic or semi-automatic integration

of hybrid retrieval knowledge and self-learning.

Consequently, there is a need for not only a

retrieval mechanism, but also for a recommendation

system to suggest resources of interest when the

resources may be too difficult to locate with

traditional retrieval systems. Our system proposes a

new form of interaction between people and Digital

Library, where the latter is adapted to individuals

and their surroundings. For this goal in our work we

developed four user profiles based on ontologies:

Staff, Alumni, Administration, and visitor, Figure 1.

Figure 1: Teacher Profile and resources associated.

These user profiles are representation the user's

interests. User profiles are used to specify the search

results. This information will satisfy the quality of

information for a specific kind of user.

2.1 Motivation and Technical

Requirements

We propose a conceptual architecture for a digital

library information retrieval system. We discuss an

proposal in this area of work with a specific view of

intelligent information processing that takes into

account the semantics of the knowledge objects

(Warren, 2005). We concentrate on the critical issue

of metadata/ontology-based search and expert

systems. More specifically the objectives are

decomposed into:

 Explore and understand the requirements for

rendering semantic search in a digital library.

 Investigate from a search perspective possible

intelligent infrastructures form constructing

decentralized digital libraries where no global

schema exists.

 Investigate how the semantic technologies can

be used to provide additional semantics from

existing resources.

 Analyze the implementation results, and evaluate

the viability of our approaches in enabling search

in intelligent-based digital libraries.

This scheme is based on the next principles:

knowledge items are abstracted to a characterization

by metadata description witch are used for further

processing (Taniar & Wenny, 2006).

3 SYSTEM ARCHITECTURE

AND IMPLEMENTATION

We will now discuss the details of providing a CBR

recommender system to retrieve the requested

metadata satisfying a user query. Following this

approach we developed a prototype. For this aim we

have used two technologies: JColibri and Protégé.

The prototype called OntoFAMA is the main tool to

verify that the proposed architecture with ontologies

and an expert system is an applicable solution.

OntoFAMA is composed of three main functional

components: ontology, expert search engine, and

user interface. A more detailed description of these

components and the interaction between them is

presented in next sections. In next figure we can see

the architecture of the system, Figure 2.

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Figure 2: OntoFAMA Search Layer Architecture.

The 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. First element the

Ontology component stores information about

resources and services where concepts are types, or

classes, individuals are allowed values, and relations

are the attributes describing the objects. The

metadata descriptions of the resources and library

objects (cases) are abstracted from the details of

their physical representation and are stored in the

case base (Sure and Studer, 2005). CBR case data

could be considered as a portion of the knowledge

(metadata) about an OntoFama object.

Second element the CBR is widely discussed in

the literature as a technology for building

information systems to support knowledge

management, where metadata descriptions for

characterizing knowledge items are used. Current

research of distributed CBR shows how CBR

systems can benefit from a standardized shared

knowledge representation that implies unambiguous

interpretation of cases. Artificial Intelligent in this

way enables the development of systems that are

able to search across multiple case-bases (Toussaint

& Cheng, 2006).

In our CBR application, searches are described

by metadata concerning desired characteristics of a

library resource, and the solution to the search is a

pointer to a resource described by metadata. These

characterizations are called cases and are stored in a

case base (Luger & George, 2002). Very case

contains two slices:

 A description of a framework problem. The

possible solutions described by means of

framework instantiation actions. These goals will

be formally described in terms of framework

domain taxonomy and they will be used for

indexing cases.

 Solution. Additional information that justifies

these steps. Our experience developing has

shown that execution graphs are a good

technique to represent the list of actions that user

should do to reach a solution, so they will be

used to represent the solutions in our simple

cases.

Finally the acceptability of a system depends to a

great extent on the quality of the user interface

component (Quan and Karger, 2004). The easiest to

implement interfaces to communicate with the user

is through a scrolling dialog. Figure 3.

Figure 3: User Profiles, Graphical User interface.

The user interacts with the system to fill in the

gaps to retrieve the right cases. The interfaces

provides for browsing, searching and facilitating

Web contents and services. It consists of one user

profile, consumer search agent components and

bring together a variety of necessary information

from different user’s resources. 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.

In an intelligence profile setting, people are

surrounded by intelligent interfaces merged, thus

creating a computing-capable environment with

intelligent communication and processing available

to the user by means of a simple, natural, and

effortless human-system interaction. 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

EXPERT KNOWLEDGE MANAGEMENT BASED ON ONTOLOGY IN A DIGITAL LIBRARY

293

been instructed, because the user profiles what he

has learnt during the previous searchers.

4 DEVELOPING CBR

APPLICATIONS

Although Case-Based Reasoning (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 section presents a novel, freely available

tool for rapid prototyping of CBR applications that

focuses on the similarity-based retrieval step. By

providing easy to use model generation, data import,

similarity modelling, explanation, and testing

functionality together with comfortable graphical

user interfaces, the tool enables even CBR novices

to rapidly create their first CBR applications.

Nevertheless, at the same time it ensures enough

flexibility to enable expert users to implement

advanced CBR applications.

We used a Case-Based Reasoning (CBR) shell,

software that can be utilized to develop several

applications that require cased-based reasoning

methodology. In this study we used the CBR object-

oriented framework development environments

JColibri. This is a java-based configuration that

supports the development of knowledge intensive

CBR applications and help in the integration of

ontology in them. This framework work as open

software development environment and facilitate the

reuse of their design as well as implementations. In

this section we describe in more detail how JColibri

supports rapid prototyping of CBR applications

(Bridge & G¨oker, 2006).

Our motivation for choosing this framework is

based on a comparative analysis between it and

other frameworks, designed to facilitate the

development of CBR applications. jColibri enhances

the other CBR shells: CATCBR, CBR*Tools,

IUCBRF, Orenge. 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 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.

Our system consists of Query Engine, Inference

Engine and Knowledge Base. The mapping between

the two layers is realized by connectors. These

connectors read the values of the data base columns

and ontology and return them to the application, i.e.

assign them to the attributes of the case. Query

Engine is responsible for the knowledge and queries

management. Is a Java library that eases the

management of the ontology in an intelligent-based

application. It uses Jena library to implement most

of the required methods for accessing the ontology,

loaded in the reasoner. With this extension the

component can acquire domain knowledge from

ontology, defined in description logics, and achieve

this way uniform case representation, what will

enhance the interoperability of the whole system.

The development of a quite simple Case-Based

Reasoning application already involves a number of

steps, such as collecting case and background

knowledge, modelling a suitable case representation,

defining an accurate similarity measure,

implementing retrieval functionality, and

implementing user interfaces. Compared with other

AI approaches, CBR allows to reduce the effort

required for knowledge acquisition and

representation significantly, which is certainly one

of the major reasons for the commercial success of

CBR applications.

4.1 Similar Cases Process Retrieval

CBR systems typically apply retrieval and matching

algorithms to a case base of past problem-solution

pairs. CBR is based on the intuition that new

searches are often similar to previously encountered

searches, and therefore, that past results may be

reused directly or through adaptation in the current

situation.

In our system a new search is solved by

retrieving one or more previously experienced cases,

reusing the case, revising. The case-based reasoning-

cycle in OntoFAMA may be described by the

following processes.

 Retrieval. Main focus of methods in this

category is to find similarity between cases.

Similarity function can be parameterized through

system configuration.

 Reuse: a complete design where case-based and

slot-based adaptation can be hooked is provided.

 Revise the proposed solution if necessary. Since

the proposed result could be inadequate, this

process can correct the first proposed solution.

 Retain the new solution as a part of a new case.

This process enables CBR to learn and create a

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new solution that should be added to the

knowledge base.

In Jcolibri once the process is modelled, three

modules are used for diagnosis: precycle, cycle, and

postcycle. The CBR methodology as follows, Figure

Figure 4: Search solving phases with CBR.

Since the problem solving methods are domain

independent, the domain specific information should

be first loaded from the persistence media so that

processing with it is possible (Díaz-Agudo &

González-Calero, 2007). The data base connector

will read the values in the table and if encounters a

concept typed attribute it looks for an instance with

the same name in the Ontology. Once found the

connector will fill the values of the attribute of each

case with the corresponding instances of the

ontology, loaded by the Pellet reasoner. It is used as

well by the methods to compute the content-based

similarity between the concepts typed attributes.

5 ONTOLOGY DESIGN

AND DEVELOPMENT

Ontologies are being developed to facilitate

knowledge sharing and reuse and are seen as key

enablers for Digital Library and Semantic Web

(Staab & Studer, 2005). Key benefits of using

semantic Web technology in the current digital

libraries include:

 An integrated, coordinated and richly-

interconnected repository of knowledge of its

libraries.

 Transferring knowledge in an economic and

scalable way to society.

 Providing a unique point of access for all people

interested in information.

 The ontology guarantees interoperation between

different applications, allowing easy addition of

new ones.

 Possibility to export knowledge and applications

to different library areas and dominions.

 Easy interoperation is possible with others

services and resources of another digital library.

From a knowledge engineering perspective,

ontologies are constructed using specialization

generalization relationships to form their taxonomies

and using other semantic relationships to extract the

meaning of concepts and factual knowledge of a

domain. OntoFAMA project contains a collection of

codes, visualization tools, computing resources, and

data sets distributed across the grids, for which we

have developed a well-defined ontology using RDF

language (W3C, 2009). RDF is used to define the

structure of the metadata describing digital library

resources.

Ontology provides a shared understanding to

support communication among human and computer

agents, typically being represented in a machine-

processable language. To achieve a standard

representation we adopt semantic web language such

as RDF as the representation syntax of metadata,

enabling RDF representation of CBR cases to

provide a standard means of representation (Gomez-

Perez & Corcho, 2003).

The primary information managed in the

OntoFama domain is metadata about library

resources, such as books, digital services, etc. We

integrated three essential sources to the system:

electronic resources, catalogue and personal Data

Base. We wrote the description of these classes and

the properties in RDF semantic markup language.

For the manual generation and modelling of the

domain ontology we chose the Protégé editor

(Protégé, 2009).

Figure 5 shows the high level classification of

classes to group together OntoFAMA resources as

well as things that are related with these resources.

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Figure 5: Class hierarchy for the OntoFama ontology.

6 SYSTEM FUNCIONALITY

As we have seen in previous sections our system has

a graphical user interface for determining initial user

requirements early in search. Managing user

requirements by placing focus on identifying,

gathering, and documenting essential information is

a specialized work area or user profiles. This action

permits to reduce useless information or completely

avoided in the search engine process. There is

therefore a need to define, and describe the initial

requirements of the user. In the case of not defining

user requirements for a search the system presents a

default configuration.

Rather than building static user profiles,

contextual systems try to adapt to the user’s current

search. The user’s search is monitored by capturing

information from the different user profiles.

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. For this a

statistical analysis has been done to determine the

importance values and establishing specified user

requirements. This statistical analysis even can in

fact lay the foundation for searches in a particular

user profile.

The user begins the search devising the starting

query Q. In the example shown in the following let

us suppose he/she starts with Q = “computer Science

books”. The outcomes represented in the following

table display the number of important documents

retrieved in OntoFama and the total number of

documents retrieved in a traditional search engine

and the values of precision and recall obtained. The

results include a list of web pages with titles, a link

to the page, and a short description showing where

the keywords have matched content within the page.

Ordered by relevance with the result that

OntoFAMA considers the most important, Figure 6.

Figure 6: Search engine results page.

The retrieval process identifies the features of

the case with the most similar query. Our inference

engine contains the CBR component that

automatically searches for similar queries-answer

pairs based on the knowledge that the system

extracted from the questions text. The system uses

similarity metrics to find the best matching case. We

used a computational based retrieval where

numerical similarity functions are used to assess and

order the cases regarding the query. The retrieval

strategy used in our system is nearest-neighbour

approach. This approach involves the assessment of

similarity between stored cases and the new input

case, based on matching a weighted sum of features.

A typical algorithm for calculating nearest

neighbour matching is next:

()

∑

ffsim

CaseCasesimilarity

),(

(1)

Where w

is the importance weighting of a

feature (or slot), sim is the similarity function of

features, and

f and

f are the values for feature i

in the input and retrieved cases respectively.

An important advantage of similarity-cased

retrieval is that if there is no case that exactly

matches the user’s requirements, this can shown the

cases that are most similar to her query. The use of

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structured representations of cases requires

approaches for similarity assessment that allow to

compares two differently structured objects, in

particular, objects belonging to different object

classes.

7 PERFORMANCE TESTING

Experiments have been carried out in order to test

the efficiency of Artificial Intelligent and Ontologies

in retrieval information in a digital library. These are

conducted to evaluate the effectiveness of run-time

ontology mapping. The main goal has been to check

if the mechanism of query formulation, assisted by

an agent, gives a suitable tool for augmenting the

number of significant documents, extracted from the

Digital Library, to be stored in the CBR.

The library of cases (the “case base”) is initially

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. This is sufficient for our

proof-of-concept demonstration, but would not be

sufficiently efficient to access large resource sets.

Each case contains a set of attributes concerning

both metadata and knowledge. However, our

prototype is currently being extended to enable

efficient retrieval directly from a database, which

will enable its use for large-scale sets of resources.

Due to the complexity of searches, users may not

be able to formulate all the considerations relevant

to their resource choices in advance, it is necessary

to guide the user at each step of the search. Besides,

it has been tested also how many steps are necessary

for retrieving the most of the important documents

for the user, filtering the queries through the profiles

user.

During the experimentation, heuristics and

measures that are commonly adopted in information

retrieval have been used. While the users were

performing these searches, an application was

continually running in the background on the server,

and capturing the content of queries typed and the

results of the searches. Statistical analysis has been

done to determine the importance values in the

results, figure 7.

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.

Figure 7: OntoFAMA search analysis report.

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 must consider that retrieved documents

relevance is subjective. That is different people can

assign distinct values of relevance to a same

document. In our study we have agreed different

values to measure the quality of retrieved

documents, excellent, good, acceptable and poor.

After the data was collected, we had a log of

queries averaging 5 queries per user. Of these

queries, some of them had to be removed, either

because there were multiple results clicked, no

results clicked, or there was no information available

for that particular query. The remaining queries were

analyzed and evaluated. 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).

Table 1: Analysis of relevance of retrieved documents

for select queries.

Excellent Good

Acceptable

Poor

OntoFAMA 5,5 %

39,3 % 40,6 % 14,4 %

Google 2,7 %

31 % 44,8 % 21,3 %

We can observe the best final ranking was

obtained for our prototype OntoFAMA and an

interesting improvement over the performance of

Google.

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8 CONCLUSIONS AND FUTURE

WORK

In this study, we addressed the main aspects of a

semantic Web information retrieval system

architecture trying to answer the requirements of the

next-generation semantic Web user. An ontology

and integrated intelligent system architecture for

search operation support system and its

implementation platform have been developed in

this paper. We presented a system based in an

ontology and artificial intelligent architecture for

knowledge management in the Seville Digital

Library. It introduced a web-based CBR retrieval

system which operates on an RDF file store. This

system combines RDF representation and CBR

recommendation methodology to do code selection

for the resources codes; thus it applies a CBR

approach with RDF data model.

A prototype implementation that uses caching

and fat operations was implemented. Besides 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.

Evaluation results have illustrated the feasibility

of our approach. The test results show that the

proposed service is a feasible solution that fields

predictable performance in terms of response time

and scalability.

A decisive role in it plays the jColibri-based and

Protégé components that are the principal elements

in the proposed architecture. Because jColibri is

domain independent, and the domain-specific

information for the system is captured entirely in the

RDF ontology and ontology instances, the developed

system could be easily transferred to other domains

as well.

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. It is also necessary the

development of an authoring tool for user

authentication, efficient ontology parsing and real-

life applications.

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