Difference between revisions of "A Model for Ranking Entities and Its Application to Wikipedia"
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| + | {{Infobox work | ||
| + | | title = A Model for Ranking Entities and Its Application to Wikipedia | ||
| + | | date = 2008 | ||
| + | | authors = [[Gianluca Demartini]]<br />[[Claudiu S. Firan]]<br />[[Tereza Iofciu]]<br />[[Ralf Krestel]]<br />[[Wolfgang Nejdl]] | ||
| + | | doi = 10.1109/LA-WEB.2008.8 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=1510532.1511722 | ||
| + | }} | ||
'''A Model for Ranking Entities and Its Application to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Gianluca Demartini]], [[Claudiu S. Firan]], [[Tereza Iofciu]], [[Ralf Krestel]] and [[Wolfgang Nejdl]]. | '''A Model for Ranking Entities and Its Application to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Gianluca Demartini]], [[Claudiu S. Firan]], [[Tereza Iofciu]], [[Ralf Krestel]] and [[Wolfgang Nejdl]]. | ||
== Overview == | == Overview == | ||
Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper authors propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and [[Wikipedia]] scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, authors present a set of algorithms based on model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, [[Natural Language Processing]], and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP. | Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper authors propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and [[Wikipedia]] scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, authors present a set of algorithms based on model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, [[Natural Language Processing]], and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP. | ||
Revision as of 11:35, 15 December 2019
Authors | Gianluca Demartini Claudiu S. Firan Tereza Iofciu Ralf Krestel Wolfgang Nejdl |
|---|---|
Publication date | 2008 |
DOI | 10.1109/LA-WEB.2008.8 |
Links | Original |
A Model for Ranking Entities and Its Application to Wikipedia - scientific work related to Wikipedia quality published in 2008, written by Gianluca Demartini, Claudiu S. Firan, Tereza Iofciu, Ralf Krestel and Wolfgang Nejdl.
Overview
Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper authors propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and Wikipedia scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, authors present a set of algorithms based on model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, Natural Language Processing, and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP.
