Difference between revisions of "Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis"

From Wikipedia Quality
Jump to: navigation, search
(wikilinks)
(infobox)
Line 1: Line 1:
 +
{{Infobox work
 +
| title = Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis
 +
| date = 2009
 +
| authors = [[Amir Hossein Jadidinejad]]<br />[[Fariborz Mahmoudi]]
 +
| link = http://ceur-ws.org/Vol-1175/CLEF2009wn-adhoc-HosseinEt2009.pdf
 +
}}
 
'''Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Amir Hossein Jadidinejad]] and [[Fariborz Mahmoudi]].
 
'''Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Amir Hossein Jadidinejad]] and [[Fariborz Mahmoudi]].
  
 
== Overview ==
 
== Overview ==
 
Combining the language model and inference network, as implemented in the Indri search engine, is efficient and verified approach. In this retrieval model, the user’s information need is exhibited as Indri’s Structural Query Language. Although the SQL allows expert users to richly represent its information needs but unfortunately, the complicacy of SQLs make them unpopular in the WEB for ordinary ones. Automatically detecting the concepts in a user’s information need and generate a richly structured equivalent query is a good solution. It needs a concept repository and a way to extracting appropriate concepts from the user’s information need. Authors utilize [[Wikipedia]] as a great, [[multilingual]], free-content encyclopedia for knowledge base and also some state of the art algorithms for extracting Wikipedia’s concepts from the user’s information need. This process is called “Query Wikification”. Mining Wikipedia concept repository help us to propose a solution that supports usability in multilingual environments, cross-language retrievals, scalability and covering erratum, various equivalents and synonyms of a concept. Experimental results verify that automatic structured query construction is an efficient and scalable method that has a very good potential to apply on the WEB. Authors experiments over TEL corpus in CLEF2009 achieves +23% improvement in Mean Average Precision and retrieves more than 600 relevant documents against the Indri baselines. In Persian track, authors evaluated a simple stemmer so-called “Perstem”, a stemmer and light morphological analyzer for Persian language. Authors experimental results show that using this stemmer in indexing and retrieval phase can significantly improve both precision (+91%) and recall (+43%).
 
Combining the language model and inference network, as implemented in the Indri search engine, is efficient and verified approach. In this retrieval model, the user’s information need is exhibited as Indri’s Structural Query Language. Although the SQL allows expert users to richly represent its information needs but unfortunately, the complicacy of SQLs make them unpopular in the WEB for ordinary ones. Automatically detecting the concepts in a user’s information need and generate a richly structured equivalent query is a good solution. It needs a concept repository and a way to extracting appropriate concepts from the user’s information need. Authors utilize [[Wikipedia]] as a great, [[multilingual]], free-content encyclopedia for knowledge base and also some state of the art algorithms for extracting Wikipedia’s concepts from the user’s information need. This process is called “Query Wikification”. Mining Wikipedia concept repository help us to propose a solution that supports usability in multilingual environments, cross-language retrievals, scalability and covering erratum, various equivalents and synonyms of a concept. Experimental results verify that automatic structured query construction is an efficient and scalable method that has a very good potential to apply on the WEB. Authors experiments over TEL corpus in CLEF2009 achieves +23% improvement in Mean Average Precision and retrieves more than 600 relevant documents against the Indri baselines. In Persian track, authors evaluated a simple stemmer so-called “Perstem”, a stemmer and light morphological analyzer for Persian language. Authors experimental results show that using this stemmer in indexing and retrieval phase can significantly improve both precision (+91%) and recall (+43%).

Revision as of 07:46, 26 June 2020


Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis
Authors
Amir Hossein Jadidinejad
Fariborz Mahmoudi
Publication date
2009
Links
Original

Query Wikification: Mining Structured Queries from Unstructured Information Needs Using Wikipedia-Based Semantic Analysis - scientific work related to Wikipedia quality published in 2009, written by Amir Hossein Jadidinejad and Fariborz Mahmoudi.

Overview

Combining the language model and inference network, as implemented in the Indri search engine, is efficient and verified approach. In this retrieval model, the user’s information need is exhibited as Indri’s Structural Query Language. Although the SQL allows expert users to richly represent its information needs but unfortunately, the complicacy of SQLs make them unpopular in the WEB for ordinary ones. Automatically detecting the concepts in a user’s information need and generate a richly structured equivalent query is a good solution. It needs a concept repository and a way to extracting appropriate concepts from the user’s information need. Authors utilize Wikipedia as a great, multilingual, free-content encyclopedia for knowledge base and also some state of the art algorithms for extracting Wikipedia’s concepts from the user’s information need. This process is called “Query Wikification”. Mining Wikipedia concept repository help us to propose a solution that supports usability in multilingual environments, cross-language retrievals, scalability and covering erratum, various equivalents and synonyms of a concept. Experimental results verify that automatic structured query construction is an efficient and scalable method that has a very good potential to apply on the WEB. Authors experiments over TEL corpus in CLEF2009 achieves +23% improvement in Mean Average Precision and retrieves more than 600 relevant documents against the Indri baselines. In Persian track, authors evaluated a simple stemmer so-called “Perstem”, a stemmer and light morphological analyzer for Persian language. Authors experimental results show that using this stemmer in indexing and retrieval phase can significantly improve both precision (+91%) and recall (+43%).