scholarly journals A query expansion method based on topic modeling and DBpedia features

2021 ◽  
Vol 1 (2) ◽  
pp. 100043
Author(s):  
Sarah Dahir ◽  
Abderrahim El Qadi
Keyword(s):  
Topic Modeling ◽  
Query Expansion ◽  
Expansion Method

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

A query expansion method for retrieving online BIM resources based on Industry Foundation Classes

2015 ◽  
Vol 56 ◽  
pp. 14-25 ◽  
Author(s):  
Ge Gao ◽  
Yu-Shen Liu ◽  
Meng Wang ◽  
Ming Gu ◽  
Jun-Hai Yong
Keyword(s):  
Query Expansion ◽  
Expansion Method ◽  
Industry Foundation Classes

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 Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval

2019 ◽  
Vol 48 (4) ◽  
pp. 626-636
Author(s):  
Bo Xu ◽  
Hongfei Lin ◽  
Yuan Lin ◽  
Kan Xu ◽  
Lin Wang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Expansion Method ◽  
Vector Representation ◽  
Retrieval Accuracy ◽  
Algebraic Vector ◽  
Vector Addition ◽  
Low Dimensional ◽  
User Queries

Microblog information retrieval has attracted much attention of researchers to capture the desired information in daily communications on social networks. Since the contents of microblogs are always non-standardized and flexible, including many popular Internet expressions, the retrieval accuracy of microblogs has much room for improvement. To enhance microblog information retrieval, we propose a novel query expansion method to enrich user queries with semantic word representations. In our method, we use a neural network model to map each word in the corpus to a low-dimensional vector representation. The mapped word vectors satisfy the algebraic vector addition operation, and the new vector obtained by the addition operation can express some common attributes of the two words. In this sense, we represent keywords in user queries as vectors, sum all the keyword vectors, and use the obtained query vectors to select the expansion words. In addition, we also combine the traditional pseudo-relevance feedback query expansion method with the proposed query expansion method. Experimental results show that the proposed method is effective and reduces noises in the expanded query, which improves the accuracy of microblog retrieval.

An Effective XML Identifying Feedback Documents Method Based on Two-Stage Ranking Model for Pseudo-Relevance Feedback

2013 ◽  
Vol 791-793 ◽  
pp. 1593-1596
Author(s):  
Min Juan Zhong
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Expansion Method ◽  
Two Stage ◽  
Relevant Feedback ◽  
Xml Documents ◽  
Ranking Model ◽  
Pseudo Relevance Feedback ◽  
Feedback Source

Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in the top retrieved set and form the good feedback source. In this paper, an effective XML identifying feedback documents method is proposed, in which a two-stage ranking model is presented and the relevant XML documents are found. The experiment results show that the proposed method is reasonable and the quality of feedback source is ensured.

Construction and application of specialty-term information for document re-ranking

2016 ◽  
Vol 40 (7) ◽  
pp. 1054-1070 ◽  
Author(s):  
Shihchieh Chou ◽  
Zhangting Dai
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Retrieval System ◽  
Expansion Method ◽  
Content Type ◽  
Feedback Information ◽  
Current Usage ◽  
Effective Technology ◽  
Practical Implications

Purpose Conventional studies mainly classify a term’s appearance in the retrieved documents as either relevant or irrelevant for application. The purpose of this paper is to differentiate the term’s appearances in the retrieved documents in more detailed situations to generate relevance information and demonstrate the applicability of the derived information in combination with current methods of query expansion. Design/methodology/approach A method was designed first to utilize the derived information owing to term appearance differentiation within a conventional query expansion approach that has been proven as an effective technology in the enhancement of information retrieval. Then, an information retrieval system was developed to demonstrate the realization and sustain the study of the method. Formal tests were conducted to examine the distinguishing capability of the proposed information utilized in the method. Findings The experimental results show that substantial differences in performances can be achieved between the proposed method and the conventional query expansion method alone. Practical implications Since the proposed information resides at the bottom of the information hierarchy of relevance feedback, any technology regarding the application of relevance feedback information could consider the utilization of this piece of information. Originality/value The importance of the study is the disclosure of the applicability of the proposed information beyond current usage of term appearances in relevant/irrelevant documents and the initiation of a query expansion technology in the application of this information.

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