https://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&feed=atom&action=historyComputing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy - Revision history2024-03-28T10:10:33ZRevision history for this page on the wikiMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&diff=27800&oldid=prevLaurie: + categories2021-02-22T06:53:13Z<p>+ categories</p>
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</table>Lauriehttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&diff=26923&oldid=prevLindsay: + Embed2021-01-23T08:03:33Z<p>+ Embed</p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics [[relatedness]] of natural language words needs a lot of background knowledge.Studies have shown that [[Wikipedia]],which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich [[semantic information]],is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics [[relatedness]] of natural language words needs a lot of background knowledge.Studies have shown that [[Wikipedia]],which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich [[semantic information]],is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Embed ==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">=== Wikipedia Quality ===</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><code></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><nowiki></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Zheng, Liang. (2011). "[[Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy]]".</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></code></ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">=== English Wikipedia ===</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><code></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><nowiki></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{cite journal |last1=Zheng |first1=Liang |title=Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy |date=2011 |url=https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy}}</ins></div></td></tr>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></code></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">=== HTML ===</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><code></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><nowiki></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Zheng, Liang. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy">Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy</a>&amp;quot;.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></nowiki></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></code></ins></div></td></tr>
</table>Lindsayhttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&diff=26684&oldid=prevMelinda: Adding infobox2021-01-16T04:13:28Z<p>Adding infobox</p>
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<td colspan="2" style="background-color: white; color:black; text-align: center;">Revision as of 04:13, 16 January 2021</td>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{Infobox work</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">| title = Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">| date = 2011</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">| authors = [[Liang Zheng]]</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-XXWX201111019.htm</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">}}</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Liang Zheng]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Liang Zheng]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics [[relatedness]] of natural language words needs a lot of background knowledge.Studies have shown that [[Wikipedia]],which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich [[semantic information]],is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics [[relatedness]] of natural language words needs a lot of background knowledge.Studies have shown that [[Wikipedia]],which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich [[semantic information]],is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td></tr>
</table>Melindahttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&diff=22918&oldid=prevIsabelle: + wikilinks2019-12-18T07:30:59Z<p>+ wikilinks</p>
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<td colspan="2" style="background-color: white; color:black; text-align: center;">Revision as of 07:30, 18 December 2019</td>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy''' - scientific work related to Wikipedia quality published in 2011, written by Liang Zheng.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy''' - scientific work related to <ins class="diffchange diffchange-inline">[[</ins>Wikipedia quality<ins class="diffchange diffchange-inline">]] </ins>published in 2011, written by <ins class="diffchange diffchange-inline">[[</ins>Liang Zheng<ins class="diffchange diffchange-inline">]]</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Overview ==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics relatedness of natural language words needs a lot of background knowledge.Studies have shown that Wikipedia,which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich semantic information,is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Any attempt to compute semantics <ins class="diffchange diffchange-inline">[[</ins>relatedness<ins class="diffchange diffchange-inline">]] </ins>of natural language words needs a lot of background knowledge.Studies have shown that <ins class="diffchange diffchange-inline">[[</ins>Wikipedia<ins class="diffchange diffchange-inline">]]</ins>,which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich <ins class="diffchange diffchange-inline">[[</ins>semantic information<ins class="diffchange diffchange-inline">]]</ins>,is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div></td></tr>
</table>Isabellehttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Chinese_Wikipedia_Links_and_Taxonomy&diff=18226&oldid=prevHannah: Starting an article - Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy2019-07-02T07:13:52Z<p>Starting an article - Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy</p>
<p><b>New page</b></p><div>'''Computing Semantic Relatedness Using Chinese Wikipedia Links and Taxonomy''' - scientific work related to Wikipedia quality published in 2011, written by Liang Zheng.<br />
<br />
== Overview ==<br />
Any attempt to compute semantics relatedness of natural language words needs a lot of background knowledge.Studies have shown that Wikipedia,which is the largest encyclopedia and could be used not only as a corpus but also a knowledge base with rich semantic information,is the ideal resource for semantic computation.In this paper,a new algorithm based on Wikipedia links and taxonomy is proposed to compute semantic relatedness of words.Since the algorithm uses only Wikipedia link structure and taxonomy,there is no need for complex text processing and the computation overhead required is smaller.Tests on a number of manual defined data sets show that it′s better than algorithms only based on Wikipedia links or on Wikipedia taxonomy.In the best case,the Spearman correlation coefficient has been increased 30.96%.</div>Hannah