scholarly journals TopicSearch - Personalized Web Clustering Engine Using Semantic Query Expansion, Memetic Algorithms and Intelligent Agents

Polibits ◽  
2013 ◽  
Vol 47 ◽  
pp. 31-45 ◽  
Author(s):  
Carlos Cobos ◽  
Martha Mendoza ◽  
Elizabeth León ◽  
Milos Manic ◽  
Enrique Herrera-Viedma
Keyword(s):  
Intelligent Agents ◽  
Query Expansion ◽  
Memetic Algorithms ◽  
Semantic Query ◽  
Web Clustering

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.

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

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 Tweet Contextualization Approach Using a Semantic Query Expansion

2021 ◽  
Vol 192 ◽  
pp. 387-396
Author(s):  
Amira Dhokar ◽  
Lobna Hlaoua ◽  
Lotfi Ben Romdhane
Keyword(s):  
Query Expansion ◽  
Semantic Query ◽  
Tweet Contextualization

scholarly journals Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

2020 ◽  
Vol 1 ◽  
pp. 1-17
Author(s):  
Gengchen Mai ◽  
Krzysztof Janowicz ◽  
Sathya Prasad ◽  
Meilin Shi ◽  
Ling Cai ◽  
...  
Keyword(s):  
Search Engine ◽  
Query Expansion ◽  
Scoring Function ◽  
Geospatial Data ◽  
Document Similarity ◽  
Geographic Information Retrieval ◽  
Limited Ability ◽  
Semantic Query ◽  
Implicit Information

Abstract. Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user’s search intentions. To better understand a user’s search intention, query expansion can be used to enrich the user’s query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user’s query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user’s query. This semantic query expansion framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user’s search intention and significantly outperforms a well-established baseline – Lucene’s practical scoring function – with more than 3.0 increments in DCG@K (K=3,5,10).

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