Difference between revisions of "Ranking Web Search Results Exploiting Wikipedia"

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== Overview ==
 
== Overview ==
 
It is widely known that search engines are the dominating tools for finding information on the web. In most of the cases, these engines return web page references on a global ranking taking in mind either the importance of the web site or the relevance of the web pages to the identified topic. In this paper, authors focus on the problem of determining distinct thematic groups on web search engine results that other existing engines provide. Authors additionally address the problem of dynamically adapting their ranking according to user selections, incorporating user judgments as implicitly registered in their selection of relevant documents. Authors system exploits a state of the art semantic web data mining technique that identifies semantic entities of [[Wikipedia]] for grouping the result set in different topic groups, according to the various meanings of the provided query. Moreover, authors propose a novel probabilistic Network scheme that employs the aforementioned topic identification method, in order to modify ranking of results as the users select documents. Authors evaluated in practice implemented prototype with extensive experiments with the ClueWeb09 dataset using the TREC’s 2009, 2010, 2011 and 2012 Web Tracks’ where authors observed improved retrieval performance compared to current state of the art re-ranking methods.
 
It is widely known that search engines are the dominating tools for finding information on the web. In most of the cases, these engines return web page references on a global ranking taking in mind either the importance of the web site or the relevance of the web pages to the identified topic. In this paper, authors focus on the problem of determining distinct thematic groups on web search engine results that other existing engines provide. Authors additionally address the problem of dynamically adapting their ranking according to user selections, incorporating user judgments as implicitly registered in their selection of relevant documents. Authors system exploits a state of the art semantic web data mining technique that identifies semantic entities of [[Wikipedia]] for grouping the result set in different topic groups, according to the various meanings of the provided query. Moreover, authors propose a novel probabilistic Network scheme that employs the aforementioned topic identification method, in order to modify ranking of results as the users select documents. Authors evaluated in practice implemented prototype with extensive experiments with the ClueWeb09 dataset using the TREC’s 2009, 2010, 2011 and 2012 Web Tracks’ where authors observed improved retrieval performance compared to current state of the art re-ranking methods.
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=== Wikipedia Quality ===
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Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2016). "[[Ranking Web Search Results Exploiting Wikipedia]]". World Scientific Publishing Company. DOI: 10.1142/S0218213016500184.
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=== English Wikipedia ===
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{{cite journal |last1=Kanavos |first1=Andreas |last2=Makris |first2=Christos |last3=Plegas |first3=Yannis |last4=Theodoridis |first4=Evangelos |title=Ranking Web Search Results Exploiting Wikipedia |date=2016 |doi=10.1142/S0218213016500184 |url=https://wikipediaquality.com/wiki/Ranking_Web_Search_Results_Exploiting_Wikipedia |journal=World Scientific Publishing Company}}
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=== HTML ===
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Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ranking_Web_Search_Results_Exploiting_Wikipedia">Ranking Web Search Results Exploiting Wikipedia</a>&amp;quot;. World Scientific Publishing Company. DOI: 10.1142/S0218213016500184.
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Revision as of 00:06, 18 January 2021


Ranking Web Search Results Exploiting Wikipedia
Authors
Andreas Kanavos
Christos Makris
Yannis Plegas
Evangelos Theodoridis
Publication date
2016
DOI
10.1142/S0218213016500184
Links
Original

Ranking Web Search Results Exploiting Wikipedia - scientific work related to Wikipedia quality published in 2016, written by Andreas Kanavos, Christos Makris, Yannis Plegas and Evangelos Theodoridis.

Overview

It is widely known that search engines are the dominating tools for finding information on the web. In most of the cases, these engines return web page references on a global ranking taking in mind either the importance of the web site or the relevance of the web pages to the identified topic. In this paper, authors focus on the problem of determining distinct thematic groups on web search engine results that other existing engines provide. Authors additionally address the problem of dynamically adapting their ranking according to user selections, incorporating user judgments as implicitly registered in their selection of relevant documents. Authors system exploits a state of the art semantic web data mining technique that identifies semantic entities of Wikipedia for grouping the result set in different topic groups, according to the various meanings of the provided query. Moreover, authors propose a novel probabilistic Network scheme that employs the aforementioned topic identification method, in order to modify ranking of results as the users select documents. Authors evaluated in practice implemented prototype with extensive experiments with the ClueWeb09 dataset using the TREC’s 2009, 2010, 2011 and 2012 Web Tracks’ where authors observed improved retrieval performance compared to current state of the art re-ranking methods.

Embed

Wikipedia Quality

Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2016). "[[Ranking Web Search Results Exploiting Wikipedia]]". World Scientific Publishing Company. DOI: 10.1142/S0218213016500184.

English Wikipedia

{{cite journal |last1=Kanavos |first1=Andreas |last2=Makris |first2=Christos |last3=Plegas |first3=Yannis |last4=Theodoridis |first4=Evangelos |title=Ranking Web Search Results Exploiting Wikipedia |date=2016 |doi=10.1142/S0218213016500184 |url=https://wikipediaquality.com/wiki/Ranking_Web_Search_Results_Exploiting_Wikipedia |journal=World Scientific Publishing Company}}

HTML

Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2016). &quot;<a href="https://wikipediaquality.com/wiki/Ranking_Web_Search_Results_Exploiting_Wikipedia">Ranking Web Search Results Exploiting Wikipedia</a>&quot;. World Scientific Publishing Company. DOI: 10.1142/S0218213016500184.