Difference between revisions of "Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network"

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'''Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network''' - scientific work related to Wikipedia quality published in 2013, written by Daniil Mirylenka and Andrea Passerini.
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{{Infobox work
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| title = Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network
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| date = 2013
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| authors = [[Daniil Mirylenka]]<br />[[Andrea Passerini]]
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| doi = 10.1145/2505515.2505621
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| link = https://dl.acm.org/citation.cfm?id=2505621
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}}
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'''Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Daniil Mirylenka]] and [[Andrea Passerini]].
  
 
== Overview ==
 
== Overview ==
Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.
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Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and [[categories]] of [[Wikipedia]], constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.
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== Embed ==
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=== Wikipedia Quality ===
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Mirylenka, Daniil; Passerini, Andrea. (2013). "[[Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network]]".DOI: 10.1145/2505515.2505621.
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=== English Wikipedia ===
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{{cite journal |last1=Mirylenka |first1=Daniil |last2=Passerini |first2=Andrea |title=Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network |date=2013 |doi=10.1145/2505515.2505621 |url=https://wikipediaquality.com/wiki/Navigating_the_Topical_Structure_of_Academic_Search_Results_via_the_Wikipedia_Category_Network}}
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=== HTML ===
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Mirylenka, Daniil; Passerini, Andrea. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Navigating_the_Topical_Structure_of_Academic_Search_Results_via_the_Wikipedia_Category_Network">Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network</a>&amp;quot;.DOI: 10.1145/2505515.2505621.
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[[Category:Scientific works]]

Latest revision as of 12:25, 11 January 2021


Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network
Authors
Daniil Mirylenka
Andrea Passerini
Publication date
2013
DOI
10.1145/2505515.2505621
Links
Original

Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network - scientific work related to Wikipedia quality published in 2013, written by Daniil Mirylenka and Andrea Passerini.

Overview

Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.

Embed

Wikipedia Quality

Mirylenka, Daniil; Passerini, Andrea. (2013). "[[Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network]]".DOI: 10.1145/2505515.2505621.

English Wikipedia

{{cite journal |last1=Mirylenka |first1=Daniil |last2=Passerini |first2=Andrea |title=Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network |date=2013 |doi=10.1145/2505515.2505621 |url=https://wikipediaquality.com/wiki/Navigating_the_Topical_Structure_of_Academic_Search_Results_via_the_Wikipedia_Category_Network}}

HTML

Mirylenka, Daniil; Passerini, Andrea. (2013). &quot;<a href="https://wikipediaquality.com/wiki/Navigating_the_Topical_Structure_of_Academic_Search_Results_via_the_Wikipedia_Category_Network">Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network</a>&quot;.DOI: 10.1145/2505515.2505621.