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http://dx.doi.org/10.13088/jiis.2013.19.1.033

Dynamic Virtual Ontology using Tags with Semantic Relationship on Social-web to Support Effective Search  

Lee, Hyun Jung (Yonsei Institute of Convergence Technology, School of Integrated Technology, Yonsei University)
Sohn, Mye (Department of Industrial Engineering, Sungkyunkwan University)
Publication Information
Journal of Intelligence and Information Systems / v.19, no.1, 2013 , pp. 19-33 More about this Journal
Abstract
In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.
Keywords
Tag; Search; Dynamic Virtual Ontology; Semantics Social-web Resource;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Angeletou, S., M. Sabou, L. Specia, and E. Motta, "Bridging the gap between folksonomies and the semantic web : An experience report",Workshop: Bridging the Gap between Semantic Web and Web 2.0, European Semantic Web Conference, (2007), 30-43.
2 Arias, J. A. and J. A. Sanchez, "Content-Based Search and Annotations in Multimedia Digital Libraries", Proceeding ENC '03 Proceedings of the 4th Mexican International Conference on Computer Science, IEEE Computer Society (2003).
3 Carter, P., "Big Data" to Drive the Next Wave of Investments in Business Analytics for CIOs in Asia/Pacific, IDC Analyze the Future, 2011.
4 Dean, J. and S. Ghemawat, "MapReduce : Simplified Data Processing on Large Clusters", OSDI (2004).
5 Dean, J. and S. Ghemawat, "MapReduce: Simplified Data Processing, on Large Clusters", COMMUNICATIONS OF THE ACM, Vol. 51, No.1(2008).
6 Ganguly, S., S. Bhatnagar, A. Saxena, S. Banerjee, and R. Izmailov, "A Fast Content-based Data Distribution Infrastructure", INFOCOM 2006. 25th IEEE International Conference on Computer Communications, Proceedings(2006).
7 Halvey, M. and M. Keane, "An assessment of tag presentation techniques", Proceedings of the 16th International Conference on World Wide Web, (2007), 1313-1314.
8 Hassan-Monteroa, Y. and V. Herrero-Solanaa, "Improving Tag-Clouds as Visual Information Retrieval Interfaces", International Conference on Multidisciplinary Information Sciences and Technologies, InSciT2006. Merida, Spain. October(2006), 25-28.
9 Hotho, A., R. J"aschke, C. Schmitz, and G. Stumme, "Information Retrieval in Folksonomies : Search and Ranking", The Semantic Web : Research and Applications, Vol.4011 of LNAI, (2006).
10 Jung, J. J., "Collaborative browsing system based on semantic mashup with open APIs", Expert Systems with Applications, Vol.39(2012), 6897-6902.   DOI   ScienceOn
11 Jung, J. J., "Discovering Community of Lingual Practive for Matching Multilingual Tags from Folksonomies", The Computer Journal, (2011).
12 Knautz, K., S. Soubusta, and W. G. Stock, "Tag Clusters as Information Retrieval Interfaces", Proceedings of the 43rd Hawaii International Conference on System Sciences, (2010).
13 Madden S., M. J. Franklin, J. Hellerstein, and W. Hong, "TAG : a Tiny Aggregation Service for Ad-Hoc Sensor Networks", Proceedings of the Fifth Symposium on Operating Systems Design and implementation(OSDI '02), (2002).
14 Markines, B., C. Cattuto, F. Menczer, D. Benz, A. Hotho, and G. Stumme, "Evaluating similarity measures for emergent semantics of social tagging", Proceedings of the 18th International Conference on World Wide Web, (2009).
15 McKinsey Global Institute, Big data: The next frontier for innovation, competition, and Productivity, MacKinsey and Company, 2011.
16 Salonen, J., "Self-organising map based tag clouds: Creating spatially meaningful representations of tagging data", OPAALS conference, (2007).
17 Park, J., N. Kim, M. Choi, Z. Jin, and Y. Choi, "Semantic Search : A Survey", Journal of Intelligence and Information Systems, Vol.17, No.4(2011), 19-36.
18 Riabov, A. V., E. Bouillet, M. D. Feblowitz, Z. Liu, and A. Ranganathan, "Wishful Search : Interactive Composition of Data Mashups", WWW 2008/Refereed Track: Web Engineering-Web Service Composition, WWW2008, April, (2008), 21-25.
19 Specia, L. and E. Motta, "Integrating Folksonomies with the Semantic Web", Lecture Notes in Computer Science, Vol.4519(2007), 624-639.
20 Schrammel, J., M. Leitner, and M. Tscheligi, "Semantically structured tag clouds: An empirical evaluation of clustered presentation approaches", Proceedings of the 27th International Conference on Human Factors in Computing Systems, (2009), 2037-2040.
21 Yoo, K., "Ontology-Based Process-Oriented Knowledge Map Enabling Referential Navigation between Knowledge", Journal of Intelligence and Information Systems, Vol.18, No.2(2012), 61-83.
22 Yoo, D., G. Kim, K. Choi, and Y. Suh, "CTKOS: Categorized Tag-based Knowledge Organization System", Journal of Intelligence and Information Systems, Vol.17, No.4(2011) 59-74.
23 Zhang, L., X. Wu, and Y. Yu, "Emergent semantics from folksonomies: A quantitative study", Journal on Data Semantics VI(Lecture Notes in Computer Science), (2006), 168-186.
24 Zikopoulos, P. C., C. Eaton, D. Deroos, T. Deutsch, G. Lapis, Understanding big data Analytics for Enterprise Class Hadoop and Streaming data, McGraw Hill, (2012).
25 Carter, P., "Big Data", to Drive the Next Wave of Investments in Business Analytics for CIOs in Asia/Pacific, IDC Analyze the Future, (2011).
26 Yang, B. and A. R. Huron, "Semantic-aware data processing: towards cross-modal multimedia analysis and content-based retrieval in distributed and mobile environments", Ph.D. Dissertation, Dept. of Computer Science and Engineering, Pennsylvania State University, (2007).
27 Lv, Q., W. Josephson, Z. Wang, M. Charikar, and K. Li, "Ferret: A Toolkit for Content Based Similarity Search of Feature Rich Data", Euro Sys '06, April, (2006), 18-21.
28 J"aschke, R., L. Marinho, A. Hotho, L. Schmidt- Thieme, and G. Stumme, "Tag Recommendations in Folksonomies",Web Tagging Workshop at the WWW(2006).