Integration of association rules and ontologies for semantic query expansion

2007 ◽  
Vol 63 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Min Song ◽  
Il-Yeol Song ◽  
Xiaohua Hu ◽  
Robert B. Allen
Keyword(s):  
Association Rules ◽  
Query Expansion ◽  
Semantic 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 Arabic Query Expansion Using WordNet and Association Rules

2016 ◽  
Vol 12 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ahmed Abbache ◽  
Farid Meziane ◽  
Ghalem Belalem ◽  
Fatma Zohra Belkredim
Keyword(s):  
Performance Improvement ◽  
Association Rules ◽  
Query Expansion ◽  
Arabic Language ◽  
Selection Method ◽  
Retrieval Performance ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Significant Performance ◽  
Selection Of

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, the authors review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, the authors are able to exploit Arabic WordNet to improve the retrieval performance. Their empirical results on a sub-corpus from the Xinhua collection showed that their automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents.

A Study of Semantic Query Expansion Based on Domain Ontology and Local Co-Occurrence Probability Model

2015 ◽  
Vol 731 ◽  
pp. 231-236
Author(s):  
Wu Xia Ning ◽  
Qiang Wang ◽  
Jin Kai Li ◽  
Feng Wang
Keyword(s):  
Query Expansion ◽  
Probability Model ◽  
Expansion Method ◽  
Domain Ontology ◽  
Occurrence Probability ◽  
Semantic Query ◽  
Retrieval Efficiency ◽  
Online Book ◽  
Filtering Function ◽  
Typical Solution

Keyword-based online book retrieval can not fully understand the user's query intent. Query expansion is a typical solution, but the rate of recall and precision is still very low in existing methods. In response to these problems, this paper presents a semantic query expansion method based on domain ontology and local co-occurrence probability model. First, ontology reasoning and concepts related calculation are used to obtain the initial expansion terms. Furthermore, the local co-occurrence probability model is used to filter the candidate expansion terms and the filtering function is used for secondary selection. Experiment results show that this method can effectively improve retrieval efficiency.

scholarly journals A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining

2019 ◽  
Vol 9 (6) ◽  
pp. 5016
Author(s):  
Siham Jabri ◽  
Azzeddine Dahbi ◽  
Taoufiq Gadi
Keyword(s):  
Association Rules ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Weighted Graph ◽  
Strong Correlations ◽  
Retrieval Performance ◽  
Dominance Relations ◽  
Baseline System ◽  
Text Query ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query.  However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique.

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