Query expansion with a medical ontology to improve a multimodal information retrieval system

2009 ◽  
Vol 39 (4) ◽  
pp. 396-403 ◽  
Author(s):  
M.C. Díaz-Galiano ◽  
M.T Martín-Valdivia ◽  
L.A. Ureña-López
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Multimodal Information ◽  
Multimodal Information Retrieval

An Improved VSM Based Information Retrieval System and Fuzzy Query Expansion

2005 ◽  
pp. 537-546 ◽  
Author(s):  
Jiangning Wu ◽  
Hiroki Tanioka ◽  
Shizhu Wang ◽  
Donghua Pan ◽  
Kenichi Yamamoto ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Fuzzy Query

Towards a Possibilistic Information Retrieval System Using Semantic Query Expansion

2012 ◽  
pp. 216-242
Author(s):  
Bilel Elayeb ◽  
Ibrahim Bounhas ◽  
Oussama Ben Khiroun ◽  
Fabrice Evrard ◽  
Narjès Bellamine-BenSaoud
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Possibility Theory ◽  
Information Retrieval System ◽  
Test Collection ◽  
Expansion Process ◽  
Semantic Query ◽  
Linguistic Resources ◽  
Relevance Measure

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary “Le Grand Robert”. First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure (possibility and necessity) between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard ‘LeMonde94’ test collection.

scholarly journals Luppar: An Information Retrieval System for Closed Document Collections

2018 ◽  
Author(s):  
Fabiano Tavares Da Silva ◽  
José Everardo Bessa Maia
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Local Context ◽  
Semantic Model ◽  
Context Analysis ◽  
Document Collections

This article presents Luppar, an Information Retrieval tool for closed collections of documents which uses a local distributional semantic model associated to each corpus. The system performs automatic query expansion using a combination of distributional semantic model and local context analysis and supports relevancy feedback. The performance of the system was evaluated in databases of different domains and presented results equal to or higher than those published in the literature.

scholarly journals Query Expansion using Semantic Network for Information Retrieval in Telugu Language

2019 ◽  
Vol 8 (2) ◽  
pp. 874-877
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Query Expansion ◽  
Retrieval System ◽  
Semantic Network ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Information Retrieval System ◽  
Word Sense ◽  
Sense Disambiguation

Now-a-days digital documents are playing a major role in all the areas /web, as such all the information is digitalised. Queries are used by the search engines to retrieve the information. Query plays a major role in information retrieval system, as a result relevant and non relevant documents are retrieved. Query expansion techniques will better the performance of the information retrieval system. Our proposed query expansion technique is Word Sense Disambiguation. This is to find the correct sense of the ambiguous word in regional Telugu language. In Query expansion, if the added query term is an ambiguous word, accuracy of relevant documents will be very less. So to avoid this, proposed method Word Sense Disambiguation (WSD) is used, which is related to NLP Natural Language Processing and Artificial Intelligence AI. WSD improves the accuracy of information retrieval system.

Towards a Possibilistic Information Retrieval System Using Semantic Query Expansion

2011 ◽  
Vol 7 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Bilel Elayeb ◽  
Ibrahim Bounhas ◽  
Oussama Ben Khiroun ◽  
Fabrice Evrard ◽  
Narjès Bellamine-BenSaoud
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Possibility Theory ◽  
Information Retrieval System ◽  
Test Collection ◽  
Expansion Process ◽  
Semantic Query ◽  
Linguistic Resources ◽  
Relevance Measure

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary “Le Grand Robert”. First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure (possibility and necessity) between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard ‘LeMonde94’ test collection.

scholarly journals An Ontology Based Information Retrieval System

2020 ◽  
Vol 9 (2) ◽  
pp. 638-643
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Retrieval System ◽  
Keyword Search ◽  
Domain Ontology ◽  
Information Retrieval System ◽  
Search System ◽  
Domain Specific ◽  
System A ◽  
Schema Graph

Ontology provide a structured way of describing knowledge. Ontology's are usually repositories of concepts and relations between them, so using them in information retrieval seems to be a reasonable goal. The main objective in this report is to provide efficient means to move from keyword-based to concept-based information retrieval utilizing ontology's for conceptual definitions [1]. In this paper, we present the skeleton of such an IR system which works on a collection of domain specific documents and exploits the use of a domain specific ontology to improve the overall number of relevant documents retrieved. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. We propose a system that matches the query terms in the ontology/schema graph and exploits the surrounding knowledge to derive an enhanced query. The enhanced query is given to the underlying basic keyword search system LUCENE [2]. In this approach we try to make use of more ontological Knowledge than IS-A and HAS-A relationships and synonyms for information retrieval.

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