While the explosive increase in information published
on the Web, researchers have to filter information when searching for
conference related information. To make it easier for users to search
related information, this paper uses Topic Maps and social information
to implement ontology since ontology can provide the formalisms and
knowledge structuring for comprehensive and transportable machine
understanding that digital information requires. Besides enhancing
information in Topic Maps, this paper proposes a method of
constructing research Topic Maps considering social information.
First, extract conference data from the web. Then extract conference
topics and the relationships between them through the proposed
method. Finally visualize it for users to search and browse. This paper
uses ontology, containing abundant of knowledge hierarchy structure,
to facilitate researchers getting useful search results. However, most
previous ontology construction methods didn-t take “people" into
account. So this paper also analyzes the social information which helps
researchers find the possibilities of cooperation/combination as well as
associations between research topics, and tries to offer better results.
[1] Ma Z. Z., & Yu, K. H. "Research paradigms of contemporary knowledge
management studies: 1998-2007," Journal of Knowledge Management,
(14:2), 2010, pp. 175-189.
[2] Weng, S. S., Tsai, H. J., Liu, S. C., & Hsu, C. H. "Ontology construction
for information classification," Expert Systems with Applications(31:1),
2006, pp. 1-12.
[3] Jiang, S. Q., Du, J., Huang, Q. M., Huang, T. J., & Gao, W. "Visual
ontology construction for digitized art image retrieval," Journal of
Computer Science and Technology(20:6), 2005, pp. 855-860.
[4] Jung, Y., Ryu, J., Kim, K. M., & Myaeng, S. H. "Automatic construction
of a large-scale situation ontology by mining how-to instructions from the
web," Web Semantics: Science, Services and Agents on the World Wide
Web(8:2-3), 2010, pp. 110-124.
[5] Santoso, H. A., Haw, S. C., & Abdul-Mehdi, Z. T. "Ontology extraction
from relational database: Concept hierarchy as background knowledge,"
Knowledge-Based Systems(24:3), 2011, pp. 457-464.
[6] Yi, M. "Information organization and retrieval using a Topic Maps-based
ontology: Results of a task-based evaluation," Journal of the American
Society for Information Science and Technology(59:12), 2008, pp.
1898-1911.
[7] Kim, J. M., Shin, H., & Kim, H. J. "Schema and constraints-based
matching and merging of Topic Maps," Information Processing &
Management(43:4), 2007, pp. 930-945.
[8] Pepper, S. "The TAO of Topic Maps," 2002 (available online at
http://www.ontopia.net/topicmaps/materials/tao.html#d0e632)
[9] Jiang, X., & Tan, A. H. "Learning and inferencing in user ontology for
personalized Semantic Web search," Information Sciences(179:16),
2009, pp. 2794-2808.
[10] Liu, L., Li, J., & Lv, C. G. "A method for enterprise knowledge map
construction based on social classification," Systems Research and
Behavioral Science, (26:2), 2009, pp. 143-153.
[1] Ma Z. Z., & Yu, K. H. "Research paradigms of contemporary knowledge
management studies: 1998-2007," Journal of Knowledge Management,
(14:2), 2010, pp. 175-189.
[2] Weng, S. S., Tsai, H. J., Liu, S. C., & Hsu, C. H. "Ontology construction
for information classification," Expert Systems with Applications(31:1),
2006, pp. 1-12.
[3] Jiang, S. Q., Du, J., Huang, Q. M., Huang, T. J., & Gao, W. "Visual
ontology construction for digitized art image retrieval," Journal of
Computer Science and Technology(20:6), 2005, pp. 855-860.
[4] Jung, Y., Ryu, J., Kim, K. M., & Myaeng, S. H. "Automatic construction
of a large-scale situation ontology by mining how-to instructions from the
web," Web Semantics: Science, Services and Agents on the World Wide
Web(8:2-3), 2010, pp. 110-124.
[5] Santoso, H. A., Haw, S. C., & Abdul-Mehdi, Z. T. "Ontology extraction
from relational database: Concept hierarchy as background knowledge,"
Knowledge-Based Systems(24:3), 2011, pp. 457-464.
[6] Yi, M. "Information organization and retrieval using a Topic Maps-based
ontology: Results of a task-based evaluation," Journal of the American
Society for Information Science and Technology(59:12), 2008, pp.
1898-1911.
[7] Kim, J. M., Shin, H., & Kim, H. J. "Schema and constraints-based
matching and merging of Topic Maps," Information Processing &
Management(43:4), 2007, pp. 930-945.
[8] Pepper, S. "The TAO of Topic Maps," 2002 (available online at
http://www.ontopia.net/topicmaps/materials/tao.html#d0e632)
[9] Jiang, X., & Tan, A. H. "Learning and inferencing in user ontology for
personalized Semantic Web search," Information Sciences(179:16),
2009, pp. 2794-2808.
[10] Liu, L., Li, J., & Lv, C. G. "A method for enterprise knowledge map
construction based on social classification," Systems Research and
Behavioral Science, (26:2), 2009, pp. 143-153.
@article{"International Journal of Business, Human and Social Sciences:61977", author = "Hei-Chia Wang and Che-Tsung Yang", title = "Research Topic Map Construction", abstract = "While the explosive increase in information published
on the Web, researchers have to filter information when searching for
conference related information. To make it easier for users to search
related information, this paper uses Topic Maps and social information
to implement ontology since ontology can provide the formalisms and
knowledge structuring for comprehensive and transportable machine
understanding that digital information requires. Besides enhancing
information in Topic Maps, this paper proposes a method of
constructing research Topic Maps considering social information.
First, extract conference data from the web. Then extract conference
topics and the relationships between them through the proposed
method. Finally visualize it for users to search and browse. This paper
uses ontology, containing abundant of knowledge hierarchy structure,
to facilitate researchers getting useful search results. However, most
previous ontology construction methods didn-t take “people" into
account. So this paper also analyzes the social information which helps
researchers find the possibilities of cooperation/combination as well as
associations between research topics, and tries to offer better results.", keywords = "Ontology, topic maps, social information,
co-authorship.", volume = "7", number = "2", pages = "456-3", }