Difference between revisions of "Query Expansion via Structural Motifs in Wikipedia Graph"
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| + | | title = Query Expansion via Structural Motifs in Wikipedia Graph | ||
| + | | date = 2016 | ||
| + | | authors = [[Joan Guisado-Gámez]]<br />[[Arnau Prat-Pérez]]<br />[[Josep Lluis Larriba-Pey]] | ||
| + | | link = https://www.amazon.com/Wikipedia-Readers-Guide-Missing-Manual/dp/059652174X | ||
| + | | plink = https://www.arxiv.org/abs/1602.07217 | ||
| + | }} | ||
'''Query Expansion via Structural Motifs in Wikipedia Graph''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Joan Guisado-Gámez]], [[Arnau Prat-Pérez]] and [[Josep Lluis Larriba-Pey]]. | '''Query Expansion via Structural Motifs in Wikipedia Graph''' - scientific work related to [[Wikipedia quality]] published in 2016, 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 too general or inappropriate keywords to express their requests. To overcome this situation, query expansion techniques aim at transforming the user request by adding new terms, referred as expansion [[features]], that better describe the real intent of the users. Authors propose a method that relies exclusively on relevant structures (as opposed to the use of semantics) found in knowledge bases (KBs) to extract the expansion features. Authors call method Structural Query Expansion (SQE). The structural analysis of KBs takes us to propose a set of structural motifs that connect their strongly related entries, which can be used to extract expansion features. In this paper authors use [[Wikipedia]] as KB, which is probably one of the largest sources of 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. Most significantly, authors believe that authors are contributing to open new research directions in query expansion, proposing a method that is orthogonal to many current systems. For example, SQE improves pseudo-relevance feedback techniques up to 13% | The search for relevant information can be very frustrating for users who, unintentionally, use too general or inappropriate keywords to express their requests. To overcome this situation, query expansion techniques aim at transforming the user request by adding new terms, referred as expansion [[features]], that better describe the real intent of the users. Authors propose a method that relies exclusively on relevant structures (as opposed to the use of semantics) found in knowledge bases (KBs) to extract the expansion features. Authors call method Structural Query Expansion (SQE). The structural analysis of KBs takes us to propose a set of structural motifs that connect their strongly related entries, which can be used to extract expansion features. In this paper authors use [[Wikipedia]] as KB, which is probably one of the largest sources of 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. Most significantly, authors believe that authors are contributing to open new research directions in query expansion, proposing a method that is orthogonal to many current systems. For example, SQE improves pseudo-relevance feedback techniques up to 13% | ||
Revision as of 08:02, 5 May 2020
Authors | Joan Guisado-Gámez Arnau Prat-Pérez Josep Lluis Larriba-Pey |
|---|---|
Publication date | 2016 |
Links | Original Preprint |
Query Expansion via Structural Motifs in Wikipedia Graph - scientific work related to Wikipedia quality published in 2016, 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 too general or inappropriate keywords to express their requests. To overcome this situation, query expansion techniques aim at transforming the user request by adding new terms, referred as expansion features, that better describe the real intent of the users. Authors propose a method that relies exclusively on relevant structures (as opposed to the use of semantics) found in knowledge bases (KBs) to extract the expansion features. Authors call method Structural Query Expansion (SQE). The structural analysis of KBs takes us to propose a set of structural motifs that connect their strongly related entries, which can be used to extract expansion features. In this paper authors use Wikipedia as KB, which is probably one of the largest sources of 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. Most significantly, authors believe that authors are contributing to open new research directions in query expansion, proposing a method that is orthogonal to many current systems. For example, SQE improves pseudo-relevance feedback techniques up to 13%
