Development of the prototype systemfor collection and integration of concept systems
Hideaki Takeda and Masahiro Hamasaki and Ryutaro Ichise
National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan
takeda@nii.ac.jp

Abstract
In this paper, we describe the outline and the current progressof our project called ”Collection and Integration of Concept
Systems (CICS)” that aims to support development of semantics in the Internet. Since semantics is not always changing, providing fixed ontologies is not stuffiest. Our projectaims to support dynamics of semantics by collecting and integrating various concept systems like Internet directories orbookmarks. We expect that integration of concept systems
different in their size, shareability, purpose and so on, will
yield new insight for both concept system. As the first prototype system, we build WebHical system that can integrate
two concept systems by using both syntactic information (hierarchy of concepts) and semantic information (words in thecontents).

Introduction

Figure 1: Dynamics of Semantics

The amount of information on the Internet has been increasing with the accelerating speed. The problem we are facing
is how to ensure the quality of information within the enormous amount of information. The movement of Semantic
Web is the straight answer to the problem, i.e., providing
the knowledge level markup to documents which are understandable intelligently both by human and machines. Since
markup tags are based on ontologies, shared understandingis ensured. The mechanism is rational, but then the problem is shifted to ”how to provide ontologies” or ”how toconstruct semantic descriptions”? Simply providing some”good” ontologies is not sufficient, because our semantics
in the real world is dynamic in nature. Our semantics is divergent, i.e., from surely shared one to domain dependent oreven private one. It is not adequate to think that all semantics
should be fully shared ideally, rather divergence of shareability is important to keep semantics alive. Meaning sometimes comes from the domain-dependent semantics and becomes public. Meaning in public is sometimes transformed
into the domain-specific one. Meaning is thus dynamic, so
we should support such dynamics of meaning to realize truly
useful knowledge-level descriptions (See Figure 1).

can be used to generate conceptualization for new requirements by mixing and combining existing concept systems.
To realize the goal, the mission of the project is simple, i.e.,just collecting various concept systems in various fields or
domains and relating them loosely. We expect that integration of concept systems different in their size, shareability,purpose and so on, will yield new insight for both concept
system. One or more concept systems usually exist in eachfield / domain ether implicitly or explicitly. They are goodsources of knowledge for that domain. All concept systems
show their view of categorization at least, since the most primary nature of knowledge is categorization. Dealing withconcept systems as categorization is expected to explicate
the most basic level of knowledge. We propose concept
systems repository as the workspace for the above research.
The basic functions of concept systems repository (CSR) are
as follows (Figure 2):
1. Import concept systems
2. Relate concept systems

Collection and integration of concept systems

3. Retrieve concepts or concept systems

Towards support of the dynamics of semantics, we started
a project called ”Collection and integration of concept systems” (CICSS). The goal is to provide an infrastructure that

The strategies to create CSR are (1) treating concept systems
as a whole not as individual concepts, and (2) Emphasizing
variety of concept systems not hiding it. The tasks to realize

Figure 3: Results of mapping of categories between two directories (Literature case)
Figure 2: Concept Systems Repository

CSR are twofold. One is modeling concept systems to treat
concept systems in a system, and the other is the algorithm
to find relationship among them. We will consider the lattertask more in the next section.

A preliminary consideration for algorithms tofind relationship among concept systems
Finding relationship among different concept systems is avery difficult problem. We do not have the exact answer
yet, but show some preliminary consideration and efforts
for it. We adopt the instance-based or extension-based approach for discovery of relationship among concept systems.
Definition-based or intension-based approach is not applicable in principle. If definitions from two concept systems
are comparative, it tells us that they are essentially the same
conceptualization. If they are formed by the different conceptualizations, definitions are not comparative. Since theinstance-based approach does not care definition, it is suitable for our purpose. Of course the instance-based has otherproblems. One is how to identify instances, i.e., even definitions of instances can be different. The other problem is how
to approximate instances, i.e., since instances can be enormous in nature, we will need some methods to say ”almostthe same”. We have two trials based on this line.

Alignment of Internet directories
One is a method to align different Internet directories likeYAHOO!, Lycos, and so on. The Internet directories are
not strictly defined but highly elaborative and practical concept systems for the Internet. In this work, we proposed
the method called HICAL to generate mapping rules fromcategories in one Internet directory to the other by the statistical method (Ichise, Takeda, & Honiden 2001c)(ICHISE,TAKEDA, & HONIDEN 2001a)(Ichise, Takeda, & Honiden
2001b). The basic idea is to find similar categories by evaluating how much they share instances (URLs). An exampleof the results is shown in Figure 3 (see (ICHISE, TAKEDA,Figure 4: Mapping among WWW bookmarks& HONIDEN 2001a) for details). The overall result is very
hopeful; around 80% of instances can be mapped correctlyfrom one directory to the other.

Finding relationship among WWW bookmarks
The other work is to find human relationship through WWWbookmarks. WWW bookmarks are results of implicit orexplicit efforts to represent personal views for the Internet (Takeda, Matsuzuka, & Taniguchi 2000)(Hamasaki &Takeda 2001). Regarding WWW bookmarks as personalconcept systems, we can find relationship among themwith the similar method to the previous work. We callit shared topics network because concepts represent sometopics which the user are interested in and they are shared
among users by the relationship generated by this method.
Figure 4 is an example of the generated shared topics network with three users. We can find some common relationship like (search, IR) and (academia, research-related), andcommunity-dependent relationship like (Unix, academia).
As the evaluation as recommender systems is good enough.
Topics found by the system were apparently more acceptable than pages themselves.

System Architecture
The current architecture for CSR is based on the above twosystems. The key function is to find similarity concepts
among concept systems. We adopt HICAL as the basic algorithm to calculate similarity and expand it for more generaluse. The major extensions are as follows;
1. RDF and bookmark files as input:
The system can accept bookmark files and RDF as dataof concept systems. Bookmark files are used for concept systems by people or groups. RDF files are used
for public directories. It can interpret files described withRDF such as RDF files provided by ”The Open DirectoryProject.”
2. Preprocessing by content-based similarity measurement:
As we discussed in (Ichise, Takeda, & Honiden 2001c),
HICAL is powerful and reliable in computing of similarity of concept systems because of dependency only on
URL categorization, but is less useful when there are few
shared URLs. In order to overcome this disadvantage, wefind closely related URLs by examining their contents. If
two URLs contain similar information enough, we add
one to the category that the other belongs to, and viceversa. We can control the threshold to determine closelyrelated URLs, e.g., the strictest case is the same as theoriginal HICAL (no URLs are changed). We provide thepreprocessing unit to find such relations.
The overall architecture is shown in Figure 5. Relextractor finds pairs of similar pages among pages in thegiven concept systems by analyzing their contents, andCS-extender modifies the given concept systems to include
quasi-identical URLs. We use GETA (Generic Engine forTransposable Association) 1 to calculate matrix of page similarity
We are now testing the system with some small data andare going to apply the realistic data soon. An example witha test data is shown in Figure 6. The example is calculationof a part of Open directory (”Internet”) and a personal bookmark. The upper-left part is the concept systems of the former, and the lower right is the latter. The lines between them
are found links between two concept systems. In this case,there are no shared URLs between two concept systems butthe system found several relationship between them.

Related Work
Integration of ontologies is getting one of the important issues in this field and researchers from different disciplines
work for this theme. From knowledge engineering view,
tools to realize integration are important. It is reasonable
for ontology editors to provide some functions for ontology integration (for example (Noy & Musen 2000)). The
other approach aims to automate ontology integration byusing machine learning techniques(Wang, Zhou, & Liew1999)(Agrawal & Srikant 2001)(Doan et al. 2002). But
these all aim to integrate homogeneous ontologies where we1

http://geta.ex.nii.ac.jp/

Bookmarks Open DirectoryWeb
html2rdf

page data

RDF URLs
1.

Rel-CalculatorStructure

2.

CS-Extender

3.

RDF
1.

Hical
2.

The system gets
URLs from RDF
database
The system gets
page contents andanalyzes them.
The systemgenerates quasiidentical URLs
The system gets
structure(category)from RDF
database
The system
extends it using
quasi-identicalURLs

Integrated hierarchal structure
Figure 5: The architecture of WebHical

aim to integrate heterogeneous ontologies. The idea of ontology repository is firstly realized as Ontolinga server(Farquhar, Fikes, & Rice 1996) but it has no explicit functions tointegrate ontologies.

Concluding remarks
We described a project called collection and integration ofconcept systems (CICSs). The goal is to provide a repositoryof concept systems that can be used to yield new conceptualization. In some sense, it is similar to Cyc, but Unlike Cyc,
we do not create but collect knowledge. Our preliminary
consideration leads to adopt the instance-based approach fordiscovery among concept systems, because concepts under
different conceptualization cannot be compared directly.

References
Agrawal, R., and Srikant, R. 2001. On integrating catalogs. In Proceedings of the 10th International WWW Conference, 603–612.
Doan, A.; Madhavan, J.; Domingos, P.; and Halevy, A.
2002. Learning to map between ontologies on the semantic web. In Proceedings of the 11th International WWW
Conference.
Farquhar, A.; Fikes, R.; and Rice, J. 1996. The ontolingua server: A tool for collaborative ontology construction.
Technical Report KSL 96-26, Stanford University.

Figure 6: An example: Bookmark vs. Open Directory

Hamasaki, M., and Takeda, H. 2001. Experimental results
for a method to discover of human relationship based on
www bookmarks. In Baba, N.; Jain, L. C.; and Howlett,R. J., eds., In Proceedings of Fifth International Conference on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies (KES-2001), volume 2, 1291–1295. Osaka: IOS Press.
ICHISE, R.; TAKEDA, H.; and HONIDEN, S. 2001a. Analignment algorithm between concept hierarchies. Technical Report NII-2001-001E, National Institute of Informatics, Tokyo, Japan.
Ichise, R.; Takeda, H.; and Honiden, S. 2001b. Automatedalignment of multiple internet directories. In Poster Proceedings, The Tenth International World Wide Web Conference, 194–195.
Ichise, R.; Takeda, H.; and Honiden, S. 2001c. Ruleinduction for concept hierarchy alignment. In Proceedings of the IJCAI-01 Workshop on Ontology Learning(OL-2001), 26–29.
(also available at CEUR Workshop Proceedings Vol-38 http://sunsite.informatik.rwthaachen.de/Publications/CEUR-WS/Vol-38/).
Noy, N., and Musen, M. 2000. Prompt: Algorithm andtool for automated ontology merging and alignment. InProceedings of the National Conference on Artificial Intelligence (AAAI).
Takeda, H.; Matsuzuka, T.; and Taniguchi, Y. 2000. Discovery of shared topics networks among people — a simple
approach to find community knowledge from www bookmarks —. In Proceedings of the Pacific Rim International
Conference of Artificial Intelligence (PRICAI 00), Lecture
Notes in Artificial Intelligence, No. 1886, 668–678.
Wang, K.; Zhou, S.; and Liew, S. C. 1999. Building hierarchical classifiers using class proximity. In Proceedingsof the 25th International Conference on Very Large DataBases, 363–374.