Difference between revisions of "Leveraging Wikipedia Knowledge for Entity Recommendations"
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| + | | title = Leveraging Wikipedia Knowledge for Entity Recommendations | ||
| + | | date = 2015 | ||
| + | | authors = [[Nitish Aggarwal]]<br />[[Peter Mika]]<br />[[Roi Blanco]]<br />[[Paul Buitelaar]] | ||
| + | | link = http://ceur-ws.org/Vol-1486/paper_81.pdf | ||
| + | }} | ||
'''Leveraging Wikipedia Knowledge for Entity Recommendations''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Nitish Aggarwal]], [[Peter Mika]], [[Roi Blanco]] and [[Paul Buitelaar]]. | '''Leveraging Wikipedia Knowledge for Entity Recommendations''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Nitish Aggarwal]], [[Peter Mika]], [[Roi Blanco]] and [[Paul Buitelaar]]. | ||
== Overview == | == Overview == | ||
User engagement is a fundamental goal of commercial search engines. In order to increase it, they provide the users an opportunity to explore the entities related to the queries. As most of the queries can be linked to entities in knowledge bases, search engines recommend the entities that are related to the users’ search query. In this paper, authors present [[Wikipedia]]-based Features for Entity Recommendation (WiFER) that combines different [[features]] extracted from Wikipedia in order to provide related entity recommendations. Authors evaluate WiFER on a dataset of 4.5K search queries where each query has around 10 related entities tagged by human experts on 5-level label scale. | User engagement is a fundamental goal of commercial search engines. In order to increase it, they provide the users an opportunity to explore the entities related to the queries. As most of the queries can be linked to entities in knowledge bases, search engines recommend the entities that are related to the users’ search query. In this paper, authors present [[Wikipedia]]-based Features for Entity Recommendation (WiFER) that combines different [[features]] extracted from Wikipedia in order to provide related entity recommendations. Authors evaluate WiFER on a dataset of 4.5K search queries where each query has around 10 related entities tagged by human experts on 5-level label scale. | ||
Revision as of 08:53, 19 November 2019
Authors | Nitish Aggarwal Peter Mika Roi Blanco Paul Buitelaar |
|---|---|
Publication date | 2015 |
Links | Original |
Leveraging Wikipedia Knowledge for Entity Recommendations - scientific work related to Wikipedia quality published in 2015, written by Nitish Aggarwal, Peter Mika, Roi Blanco and Paul Buitelaar.
Overview
User engagement is a fundamental goal of commercial search engines. In order to increase it, they provide the users an opportunity to explore the entities related to the queries. As most of the queries can be linked to entities in knowledge bases, search engines recommend the entities that are related to the users’ search query. In this paper, authors present Wikipedia-based Features for Entity Recommendation (WiFER) that combines different features extracted from Wikipedia in order to provide related entity recommendations. Authors evaluate WiFER on a dataset of 4.5K search queries where each query has around 10 related entities tagged by human experts on 5-level label scale.
