Difference between revisions of "Structural Query Expansion via Motifs from Wikipedia"
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| + | | title = Structural Query Expansion via Motifs from Wikipedia | ||
| + | | date = 2017 | ||
| + | | authors = [[Joan Guisado-Gámez]]<br />[[Arnau Prat-Pérez]]<br />[[Josep Lluis Larriba-Pey]] | ||
| + | | doi = 10.1145/3077331.3077342 | ||
| + | | link = | ||
| + | }} | ||
'''Structural Query Expansion via Motifs from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Joan Guisado-Gámez]], [[Arnau Prat-Pérez]] and [[Josep Lluis Larriba-Pey]]. | '''Structural Query Expansion via Motifs from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Joan Guisado-Gámez]], [[Arnau Prat-Pérez]] and [[Josep Lluis Larriba-Pey]]. | ||
== Overview == | == Overview == | ||
The search for relevant information can be very frustrating for users who, unintentionally, use inappropriate keywords to express their needs. Expansion techniques aim at transforming the users' queries by adding new terms, called expansion [[features]], that better describe the real users' intent. Authors propose Structural Query Expansion (SQE), a method that relies on relevant structures found in knowledge bases (KBs) to extract the expansion features as opposed to the use of semantics. In the particular case of this paper, authors use [[Wikipedia]] because it is probably the largest source of up-to-date information. SQE is capable of achieving more than 150% improvement over non-expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst-case scenario. SQE is designed as an orthogonal method that can be combined with other expansion techniques, such as pseudo-relevance feedback. | The search for relevant information can be very frustrating for users who, unintentionally, use inappropriate keywords to express their needs. Expansion techniques aim at transforming the users' queries by adding new terms, called expansion [[features]], that better describe the real users' intent. Authors propose Structural Query Expansion (SQE), a method that relies on relevant structures found in knowledge bases (KBs) to extract the expansion features as opposed to the use of semantics. In the particular case of this paper, authors use [[Wikipedia]] because it is probably the largest source of up-to-date information. SQE is capable of achieving more than 150% improvement over non-expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst-case scenario. SQE is designed as an orthogonal method that can be combined with other expansion techniques, such as pseudo-relevance feedback. | ||
Revision as of 01:00, 18 January 2021
Authors | Joan Guisado-Gámez Arnau Prat-Pérez Josep Lluis Larriba-Pey |
|---|---|
Publication date | 2017 |
DOI | 10.1145/3077331.3077342 |
Links |
Structural Query Expansion via Motifs from Wikipedia - scientific work related to Wikipedia quality published in 2017, written by Joan Guisado-Gámez, Arnau Prat-Pérez and Josep Lluis Larriba-Pey.
Overview
The search for relevant information can be very frustrating for users who, unintentionally, use inappropriate keywords to express their needs. Expansion techniques aim at transforming the users' queries by adding new terms, called expansion features, that better describe the real users' intent. Authors propose Structural Query Expansion (SQE), a method that relies on relevant structures found in knowledge bases (KBs) to extract the expansion features as opposed to the use of semantics. In the particular case of this paper, authors use Wikipedia because it is probably the largest source of up-to-date information. SQE is capable of achieving more than 150% improvement over non-expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst-case scenario. SQE is designed as an orthogonal method that can be combined with other expansion techniques, such as pseudo-relevance feedback.
