[IEEE Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) - Nigata,...
2
Educational Information Search Service Using Ontology Byoungchol Chang 1 , Dall-ho Ham 1 , Dae-sung Moon 1 , Yong S Choi 2 and Jaehyuk Cha 1* 1 Division of Information and Communications, Hanyang University, Korea 2 Department of Computer Science Education, Hanyang University, Korea [email protected], [email protected], [email protected], [email protected], [email protected]* * Corresponding Author. Abstract This paper describe the case study of developing and providing the ontology-based educational information search service to overcome the limitations of the existing keyword-based search service. We used the OWL-DL 1) to build the core ontology DB from the existing metadata DB represented by the LOM, 2) to build the domain ontology DB of middle school Mathematics contents DB, 3) to merge the core ontology DB and the domain ontology DB. And both the OWL-DL and the SWRL are used to support the educational information search service. The system for the above service was built using the OWL-based deduction engine and the merged ontology DB, that process and deduct user query with enhanced simple and intuitional interface. This service is being provided as the demonstrational service by the EDUNET – the largest nationwide learning contents search system in Korea. 1. Introduction Currently e-Learning resources are developed and provided by separate processes and from different organizations in which they use keyword-based search system in order to retrieve the learning materials. The EDUNET[1] of KERIS (Korea Education & Research Information Service)[2] is the e-Learning contents provider. As seen on many other e-learning contents search systems, EDUENT provide keyword- based search system with highly organized construction of metadata using KEM (Korea Education Metadata)[3] which has been developed and extended based on LOM[4]. However, current keyword-based search system using metadata such as LOM falls short on several aspects as follows[5]: Unsophisticated search due to lack of relativeness between properties of metadata. Inefficiency in search results due to lack of keyword semantics. The learning contents ontology of our study is consisted of contents metadata ontology and domain ontology containing properties of contents. For the deduction of constructed ontology, we built search service prototype with existing deduction engines namely KAON2[6] and Bossam[7]. 2. Building Ontology 2.1. LOM OWL Binding The complete set of binding results of LOM elements is covered in Reference [8]. The generic principles of LOM Ontology binding in this study are as follows. In order to acquire effective search results for the user query, we conducted user survey and made Competency Question to bind the LOM with OWL. Learning resources described with LOM in a whole are defined as LearningResource class and each resource is represented as instance of LearningResource class. By making most of OWL-DL features, we deployed various cardinality related predicates in each class to represent explicit semantics of LOM. Among the principles above, the first and last are the distinctive characteristic of our study compared to [9]. Due to the limited expressiveness of RDF construct, not all of cardinalities can be expressed in Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) 0-7695-2916-X/07 $25.00 © 2007
Transcript of [IEEE Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) - Nigata,...
![Page 1: [IEEE Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) - Nigata, Japan (2007.07.18-2007.07.20)] Seventh IEEE International Conference on Advanced](https://reader035.fdocuments.us/reader035/viewer/2022080421/5750a4aa1a28abcf0cac1955/html5/thumbnails/1.jpg)
Educational Information Search Service Using Ontology
Byoungchol Chang1, Dall-ho Ham1, Dae-sung Moon1, Yong S Choi2 and Jaehyuk Cha1* 1Division of Information and Communications, Hanyang University, Korea 2Department of Computer Science Education, Hanyang University, Korea
[email protected], [email protected], [email protected], [email protected], [email protected]*
* Corresponding Author.
Abstract
This paper describe the case study of developing and providing the ontology-based educational information search service to overcome the limitations of the existing keyword-based search service. We used the OWL-DL 1) to build the core ontology DB from the existing metadata DB represented by the LOM, 2) to build the domain ontology DB of middle school Mathematics contents DB, 3) to merge the core ontology DB and the domain ontology DB. And both the OWL-DL and the SWRL are used to support the educational information search service. The system for the above service was built using the OWL-based deduction engine and the merged ontology DB, that process and deduct user query with enhanced simple and intuitional interface. This service is being provided as the demonstrational service by the EDUNET – the largest nationwide learning contents search system in Korea. 1. Introduction
Currently e-Learning resources are developed and provided by separate processes and from different organizations in which they use keyword-based search system in order to retrieve the learning materials.
The EDUNET[1] of KERIS (Korea Education & Research Information Service)[2] is the e-Learning contents provider. As seen on many other e-learning contents search systems, EDUENT provide keyword-based search system with highly organized construction of metadata using KEM (Korea Education Metadata)[3] which has been developed and extended based on LOM[4]. However, current keyword-based search system using metadata such as LOM falls short on several aspects as follows[5]:
Unsophisticated search due to lack of relativeness
between properties of metadata. Inefficiency in search results due to lack of
keyword semantics. The learning contents ontology of our study is
consisted of contents metadata ontology and domain ontology containing properties of contents. For the deduction of constructed ontology, we built search service prototype with existing deduction engines namely KAON2[6] and Bossam[7].
2. Building Ontology 2.1. LOM OWL Binding
The complete set of binding results of LOM elements is covered in Reference [8].
The generic principles of LOM Ontology binding in this study are as follows.
In order to acquire effective search results for the user query, we conducted user survey and made Competency Question to bind the LOM with OWL.
Learning resources described with LOM in a whole are defined as LearningResource class and each resource is represented as instance of LearningResource class.
By making most of OWL-DL features, we deployed various cardinality related predicates in each class to represent explicit semantics of LOM.
Among the principles above, the first and last are
the distinctive characteristic of our study compared to [9]. Due to the limited expressiveness of RDF construct, not all of cardinalities can be expressed in
Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007)0-7695-2916-X/07 $25.00 © 2007
![Page 2: [IEEE Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) - Nigata, Japan (2007.07.18-2007.07.20)] Seventh IEEE International Conference on Advanced](https://reader035.fdocuments.us/reader035/viewer/2022080421/5750a4aa1a28abcf0cac1955/html5/thumbnails/2.jpg)
LOM RDF binding. However, OWL-DL successfully expresses most of original constructs of LOM. 2.2. Building Domain Ontology
To prove that the deployment of semantic structure can significantly improve the functionality of search, we constructed the actual semantic structure of the “Geometric Figures” in the 8th grade of middle school Mathematics based on the course classification system of the Ministry of Education of Korea in collaboration with teachers and professionals of Mathematics education. Domain ontology construct in this study mainly represents the interrelationship between concepts that shows complicated sub-super relationship of classes.
In order to achieve integration of domain ontology and LOM ontology, we convoyed integration method using namespace. If the relationship between classes or properties within domain ontology and LOM ontology are semantically equivalent, we combined them with OWL properties such as owl:SameAs and owl:equivalentClasses. 3. Implementation and Test
Our system was built on the Linux Kernel 2.6 with the system consisted of Pentium4 2.4Ghz, 1MB Main Memory.
We built user interface based on SPARQL to input query. For users unfamiliar with SQL-Like query of SPARQL, we built categorized drop-down menu on the web that creates SPARQL query statement automatically.
We selected almost 2,300 concepts in the area of middle school Mathematics contents and its metadata in collaboration with KERIS and classified them into domain ontology class in consultation with a Mathematics professional. Then the actual metadata were implemented into as instances of the domain ontology class.
We tested the system in this study with various queries. We could extend the user query to more comprehensive meaning by using the hierarchical structure of class and property and rules written in SWRL. As a result, we were able to present more accurate and meaningful results that not just fit the keyword but rather semantically accurate.
The screen capture of search entry form in our system is shown in Figure 1.
4. Conclusion
The system we presented in this study using ontology has shown the possibility of development of search service that enables customized search. The improved search performance using ontology will extend the reusability of learning contents and provide more systematic learning for learners.
Figure 1: User Interface of the System Prototype
In this study, we have constructed sample domain
ontology to demonstrate the effectiveness of our ontology based system. Domain ontology is essential in achieving effective content search. It is required to build domain ontology of all subjects in the future. 5. References [1] EDUNET. http://edunet4u.net. [2] KERIS(Korea Education & Research Information Service). http://www.keris.or.kr. [3] Jin-Gon Shon, Yong-Sang Cho, Kwang-Sik Chung. Research on Metadata(KEM v3.0) for Higher Educational Information and Digital Rights Management. Research Report KR-2005-27, KERIS, 2005. [4] Wayne Hodgins et al. Draft Standard for Learning Object Metadata. http://ltsc.ieee.org/wg12 /files/LOM_1484_12_ 1_v1_Final_Draft.pdf. IEEE 1484.12.1-2002, 2002. [5] C. Kindle. Ontology-supported Information Retrieval. Proceedings of EUROCON 2005, Nov. 2005. [6] KAON2. http://kaon2.semanticweb.org/ [7] Bossam. http://bossam.wordpress.com [8] Jaehyuk Cha, Yong-Sang Cho, Hyunjong Choe, Yong-S Choi, Nammee Moon. Research on Enchancing the Education Resource management with ontology. Research Report KR-2006-xx, KERIS, Dec. 2006. [9] M. Nilsson, M. Palmér, J. Brase. The LOM RDF Binding - Principles and Implementation. Proceedings of the Third Annual ARIADNE conference, 2003.
Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007)0-7695-2916-X/07 $25.00 © 2007
