Difference between revisions of "Cross Domain Search by Exploiting Wikipedia"
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| + | {{Infobox work | ||
| + | | title = Cross Domain Search by Exploiting Wikipedia | ||
| + | | date = 2012 | ||
| + | | authors = [[Chen Liu]]<br />[[Sai Wu]]<br />[[Shouxu Jiang]]<br />[[Anthony K. H. Tung]] | ||
| + | | doi = 10.1109/ICDE.2012.13 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=2310257.2310370 | ||
| + | }} | ||
'''Cross Domain Search by Exploiting Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Chen Liu]], [[Sai Wu]], [[Shouxu Jiang]] and [[Anthony K. H. Tung]]. | '''Cross Domain Search by Exploiting Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Chen Liu]], [[Sai Wu]], [[Shouxu Jiang]] and [[Anthony K. H. Tung]]. | ||
== Overview == | == Overview == | ||
The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, authors can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, authors propose an approach for linking tagged resources to concepts extracted from [[Wikipedia]], which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. Authors develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that solution achieves good performance. | The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, authors can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, authors propose an approach for linking tagged resources to concepts extracted from [[Wikipedia]], which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. Authors develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that solution achieves good performance. | ||
Revision as of 22:28, 3 October 2020
Authors | Chen Liu Sai Wu Shouxu Jiang Anthony K. H. Tung |
|---|---|
Publication date | 2012 |
DOI | 10.1109/ICDE.2012.13 |
Links | Original |
Cross Domain Search by Exploiting Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Chen Liu, Sai Wu, Shouxu Jiang and Anthony K. H. Tung.
Overview
The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, authors can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, authors propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. Authors develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that solution achieves good performance.
