Difference between revisions of "Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts"

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{{Infobox work
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| title = Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts
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| date = 2016
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| authors = [[Wei Wang]]<br />[[Xiaoyan Yang]]<br />[[Shouxu Jiang]]
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| doi = 10.1007/978-3-319-49304-6_41
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| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-49304-6_41.pdf
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}}
 
'''Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Wei Wang]], [[Xiaoyan Yang]] and [[Shouxu Jiang]].
 
'''Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Wei Wang]], [[Xiaoyan Yang]] and [[Shouxu Jiang]].
  
 
== Overview ==
 
== Overview ==
 
The increasing popularity of [[social network]]ing systems (SNSs) has created large quantities of data from multiple modalities such as text and image. Retrieval of data, however, is constrained to a specific modality. Moreover, text on SNSs is usually short and noisy, and remains active for a (short) period. Such characteristics, conflicting with settings of traditional text search techniques, render them ineffective in SNSs. To alleviate these problems and bridge the gap between searches over different modalities, authors propose a new algorithm that supports cross-modal search about social documents as text and images on SNSs. By exploiting [[Wikipedia]] concepts, text and images are transformed into a set of common concepts, based on which searches are conducted. A new ranking algorithm is designed to rank social documents based on their informativeness and semantic relevance to a query. Authors evaluate ranking algorithm on both [[Twitter]] and [[Facebook]] datasets. The results confirm the effectiveness of approach.
 
The increasing popularity of [[social network]]ing systems (SNSs) has created large quantities of data from multiple modalities such as text and image. Retrieval of data, however, is constrained to a specific modality. Moreover, text on SNSs is usually short and noisy, and remains active for a (short) period. Such characteristics, conflicting with settings of traditional text search techniques, render them ineffective in SNSs. To alleviate these problems and bridge the gap between searches over different modalities, authors propose a new algorithm that supports cross-modal search about social documents as text and images on SNSs. By exploiting [[Wikipedia]] concepts, text and images are transformed into a set of common concepts, based on which searches are conducted. A new ranking algorithm is designed to rank social documents based on their informativeness and semantic relevance to a query. Authors evaluate ranking algorithm on both [[Twitter]] and [[Facebook]] datasets. The results confirm the effectiveness of approach.

Latest revision as of 10:16, 12 August 2019


Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts
Authors
Wei Wang
Xiaoyan Yang
Shouxu Jiang
Publication date
2016
DOI
10.1007/978-3-319-49304-6_41
Links
Original

Cross-Modal Search on Social Networking Systems by Exploring Wikipedia Concepts - scientific work related to Wikipedia quality published in 2016, written by Wei Wang, Xiaoyan Yang and Shouxu Jiang.

Overview

The increasing popularity of social networking systems (SNSs) has created large quantities of data from multiple modalities such as text and image. Retrieval of data, however, is constrained to a specific modality. Moreover, text on SNSs is usually short and noisy, and remains active for a (short) period. Such characteristics, conflicting with settings of traditional text search techniques, render them ineffective in SNSs. To alleviate these problems and bridge the gap between searches over different modalities, authors propose a new algorithm that supports cross-modal search about social documents as text and images on SNSs. By exploiting Wikipedia concepts, text and images are transformed into a set of common concepts, based on which searches are conducted. A new ranking algorithm is designed to rank social documents based on their informativeness and semantic relevance to a query. Authors evaluate ranking algorithm on both Twitter and Facebook datasets. The results confirm the effectiveness of approach.