An Intelligent Digital Library System for Biologists
Jeffrey Stone
Dept. of Computer Science,
University of Vermont
jestone@zoo.uvm.edu
Xindong Wu
Dept. of Computer Science,
University of Vermont
xwu@emba.uvm.edu
Marc Greenblatt
Department of Medicine,
University of Vermont
Marc.Greenblatt@vtmednet.org
Abstract
To aid researchers in obtaining, organizing and
managing biological data, we have developed a
sophisticated digital library system that utilizes
advanced data mining techniques. Our digital
library system is centralized with Web links to
publicly accessible data repositories. Our digital
library is based on a framework used for
conventional libraries and an object-oriented
paradigm, and will provide personalized usercentered
services based on the user’s areas of
interests and preferences. To make personalized
service possible, a “user profile” that represents the
preferences of an individual user is constructed
based upon the user’s past activities, goals indicated
by the user, and options. Utilizing these user
profiles, our system will make relevant information
available to the user in an appropriate form,
amount, and level of detail with minimal user effort.
1. Introduction
Recent advances in the fields of computational
biology, cloning and genetics have resulted in vast
amounts of data, which are providing an
unprecedented volume of knowledge to researchers
and medical personnel. This information will be
critical for the understanding of biological structure
and function and has allowed for the development of
new treatment approaches for disease such as gene
therapy and pharmacogenetics. However, the
amount of data that the researcher must digest on a
daily basis has become unmanageable
the view of biological phenomena as being composed
of a number of sub disciplines (e.g., structural
biology, genomics, proteomics, and biochemistry)
has served to further complicate the issue. To obtain
a coherent picture of biological phenomena at the
molecular, cellular, and organism levels, one must
both look at all of these attributes and at the
relationships among them. To do that currently
requires finding which databases contain the relevant
information and then searching through the databases
one by one.
To aid the researcher in this task of information
retrieval and organization, we are developing a
sophisticated digital library system that utilizes data
mining techniques and user profiling to recommend
items to the user. Our digital library system is
centralized with Web links to publicly accessible data
repositories. Based on the framework of a
conventional library, our system will also provide
user-centered services based on a user’s past activities
and preferences.
The core of our project is an agent architecture
that provides advanced services by combining data
mining capabilities with domain knowledge in the
form of a semantic network. The semantic network
will impart a knowledge structure through which the
system can “reason” and draw conclusions about
biological data objects and will provide a federated
view of the many disparate databases of interest to
biologists. In the development of our semantic
network, we have included the concepts from several
established controlled vocabularies, chief among
them being the National Library of Medicine’s
Unified Medical language System (UMLS). Our
complete semantic network consists of 183 semantic
types and 69 relationships.
. Furthermore,
2. Library System Design
Our approach begins from the centralized,
structured view of a conventional library, and seeks
to provide access to the digital library through
electronic means including the Internet, while
maintaining the advantages of decentralization, rapid
evolution and flexibility of the Web. The core of our
project is a knowledge object modeling of data
Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB 2004)
0-7695-2194-0/04 $20.00 ฉ 2004
IEEE
repositories, and an agent architecture that provides
advanced services by combining data mining
capabilities. The knowledge objects are defined to be
an integration of the object-oriented paradigm with
rules, the proper integration of which provides a
flexible and powerful environment for deductive
retrieval and pattern matching.
To make personalized service possible, a “user
profile” representing the preferences of an individual
user is constructed based upon the user’s past
activities, goals indicated by the user, and options.
Utilizing these user profiles, our system will make
relevant information available to the user in an
appropriate form, amount, and level of detail, and
especially with minimal user effort.
3. Semantic network based dictionary
One crucial component of our digital library
system is a dictionary of biological terminology.
This dictionary will play an important role in
building the user profiles as well as the categorization
rules of each item in our digital library.
In the construction of the dictionary, we are
presented with some difficulties due to the nature of
biological data. Some of the problems encountered
are multiple names for the same protein or gene in
different organisms, the dependency of the biological
state in which the function is taking place and
multiple functions for the same protein. These
problems preclude the use of a simple hierarchical
dictionary structure.
To overcome these obstacles and provide a model
that can accurately model the information contained
in multiple biological databases, we have developed
our dictionary as a semantic network of biological
terminology utilizing a directed graph based
paradigm. Our semantic network strives to provide a
categorization of biological concepts and
relationships among these concepts. The semantic
network will impart a knowledge structure through
which our system can “reason” and draw conclusions
about biological data objects. The Unified Medical
language System (UMLS) contains a large semantic
network of its own that we have used as a base for
our system [1]. However the UMLS is in some
aspects not general enough for use in categorizing
multiple biomedical databases and also contains too
many terms that are outside of the scope of our
project. Therefore, we have trimmed some of the
detail from the UMLS system and added new types
and relationships to this system to provide a more
general coverage of biological databases. Our
complete semantic network consists of 183 semantic
types and 69 relationships.
Our semantic network is comprised of nodes
representing semantic types and relationships between
these nodes. Each node represents a category of
either a biological entity or an event. The entities
and events used in our semantic network result from
a merging of some of the concept names in the
National Library of Medicine’s Unified Medical
Language System and the Gene Ontology
Consortium’s controlled vocabulary [2].
4. Generation of rules
The rules that are used for the recommendation of
items in our system are generated with the popular
open source Weka data mining package [2].
Regeneration of rules will take place upon the end of
each session, or optionally during a session when
prompted by the user. At the time of regeneration of
the rules, relevant data is extracted from the user’s
profile and passed to the Weka J48 program. This
program will generate the classification rules that will
be the basis for the recommendation of items in the
library. After the rules are generated, they are saved
into the user’s profile after generation.
5. Conclusions
Through the combination of data mining, user
profiles and a semantic network, our system can aid
researchers in obtaining, organizing and managing
biological data. Through the use of recommendation
agents, our system will help the novice researcher
discover new topics and items to look at, and the
expert to quickly view new items that pertain to their
research area.
6. References
[1]
Library of Medicine,
NLM Unified Medical Language System, National
http://www.nlm.nih.gov/research/umls
.
[2]
http://www.geneontology.org
[3] Ian H. Witten and Eibe Frank,
Practical machine learning tools with Java
implementations
2000.
Gene Ontology Consortium.Data Mining:, Morgan Kaufmann, San Francisco,
Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB 2004)
0-7695-2194-0/04 $20.00 ฉ 2004 IEEE
