https://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&feed=atom&action=historyRelated Entity Finding Using Semantic Clustering based on Wikipedia Categories - Revision history2024-03-28T14:20:10ZRevision history for this page on the wikiMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&diff=27675&oldid=prevRachel: cat.2021-02-15T08:17:15Z<p>cat.</p>
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</table>Rachelhttps://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&diff=25838&oldid=prevBella: Adding embed2020-11-02T10:05:38Z<p>Adding 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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval [[measures]] to choose the most relevant. For each one authors look up its [[Wikipedia]] page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic [[relatedness]] between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval [[measures]] to choose the most relevant. For each one authors look up its [[Wikipedia]] page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic [[relatedness]] between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</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;">Stratogiannis, Georgios; Siolas, Georgios; Stafylopatis, Andreas. (2013). "[[Related Entity Finding Using Semantic Clustering based on Wikipedia Categories]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-38634-3_18. </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=Stratogiannis |first1=Georgios |last2=Siolas |first2=Georgios |last3=Stafylopatis |first3=Andreas |title=Related Entity Finding Using Semantic Clustering based on Wikipedia Categories |date=2013 |doi=10.1007/978-3-642-38634-3_18 |url=https://wikipediaquality.com/wiki/Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories |journal=Springer, Berlin, Heidelberg}}</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;"></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;">Stratogiannis, Georgios; Siolas, Georgios; Stafylopatis, Andreas. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories">Related Entity Finding Using Semantic Clustering based on Wikipedia Categories</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-38634-3_18. </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>
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</table>Bellahttps://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&diff=25078&oldid=prevElla: infobox2020-07-06T05:58:39Z<p>infobox</p>
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<td colspan="2" style="background-color: white; color:black; text-align: center;">Revision as of 05:58, 6 July 2020</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 = Related Entity Finding Using Semantic Clustering based on Wikipedia Categories</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 = 2013</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 = [[Georgios Stratogiannis]]<br />[[Georgios Siolas]]<br />[[Andreas Stafylopatis]]</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;">| doi = 10.1007/978-3-642-38634-3_18</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 = https://link.springer.com/chapter/10.1007/978-3-642-38634-3_18</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>'''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Georgios Stratogiannis]], [[Georgios Siolas]] and [[Andreas Stafylopatis]].</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>'''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Georgios Stratogiannis]], [[Georgios Siolas]] and [[Andreas Stafylopatis]].</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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval [[measures]] to choose the most relevant. For each one authors look up its [[Wikipedia]] page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic [[relatedness]] between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval [[measures]] to choose the most relevant. For each one authors look up its [[Wikipedia]] page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic [[relatedness]] between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</div></td></tr>
</table>Ellahttps://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&diff=25030&oldid=prevLillian: wikilinks2020-07-03T06:30:36Z<p>wikilinks</p>
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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>'''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to Wikipedia quality published in 2013, written by Georgios Stratogiannis, Georgios Siolas and Andreas Stafylopatis.</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>'''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to <ins class="diffchange diffchange-inline">[[</ins>Wikipedia quality<ins class="diffchange diffchange-inline">]] </ins>published in 2013, written by <ins class="diffchange diffchange-inline">[[</ins>Georgios Stratogiannis<ins class="diffchange diffchange-inline">]]</ins>, <ins class="diffchange diffchange-inline">[[</ins>Georgios Siolas<ins class="diffchange diffchange-inline">]] </ins>and <ins class="diffchange diffchange-inline">[[</ins>Andreas Stafylopatis<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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval measures to choose the most relevant. For each one authors look up its Wikipedia page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic relatedness between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</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>Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval <ins class="diffchange diffchange-inline">[[</ins>measures<ins class="diffchange diffchange-inline">]] </ins>to choose the most relevant. For each one authors look up its <ins class="diffchange diffchange-inline">[[</ins>Wikipedia<ins class="diffchange diffchange-inline">]] </ins>page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic <ins class="diffchange diffchange-inline">[[</ins>relatedness<ins class="diffchange diffchange-inline">]] </ins>between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</div></td></tr>
</table>Lillianhttps://wikipediaquality.com/index.php?title=Related_Entity_Finding_Using_Semantic_Clustering_based_on_Wikipedia_Categories&diff=19814&oldid=prevAmelia: New work - Related Entity Finding Using Semantic Clustering based on Wikipedia Categories2019-08-01T07:06:02Z<p>New work - Related Entity Finding Using Semantic Clustering based on Wikipedia Categories</p>
<p><b>New page</b></p><div>'''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to Wikipedia quality published in 2013, written by Georgios Stratogiannis, Georgios Siolas and Andreas Stafylopatis.<br />
<br />
== Overview ==<br />
Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval measures to choose the most relevant. For each one authors look up its Wikipedia page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic relatedness between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.</div>Amelia