Research on Bayesian Network Retrieval Model in Electronic Commerce

2012 ◽  
Vol 263-266 ◽  
pp. 2726-2731
Author(s):  
Shuang Zhao ◽  
Yong Min Lin
Keyword(s):  
Information Retrieval ◽  
Bayesian Network ◽  
Electronic Commerce ◽  
Theoretical Basis ◽  
Query Expansion ◽  
Posterior Probability ◽  
Expansion Method ◽  
Analysis Method ◽  
Retrieval Model ◽  
Retrieval Performance

On the analysis about the problem of information retrieval in the Electronic Commerce environment, this paper presents a Bayesian network retrieval model. This model adopts the topology of three layer nodes, and uses co-occurrence analysis method to mine relationships between the terms. A query expansion method according to domain ontology is used to extend the users query. Finally the similarity between the document and the query can be measured by calculating the posterior probability of relevance of the document. Experiments show that the model which will provide a theoretical basis for the problem of information retrieval in the Electronic Commerce environment can effectively improve the retrieval performance.

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.

scholarly journals An ensemble information retrieval method for the biomedical domain (Preprint)

2021 ◽  
Author(s):  
Zhiqiang Liu ◽  
Jingkun Feng ◽  
Zhihao Yang ◽  
Lei Wang
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Text Classification ◽  
Query Expansion ◽  
Ensemble Method ◽  
Classification Model ◽  
Retrieval Performance ◽  
Matching Model ◽  
Ranking List ◽  
Initial Retrieval

BACKGROUND With the development of biomedicine, the number of biomedical documents has increased rapidly, which brings a great challenge for researchers retrieving the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in some specific retrieval tasks and thereby increases the difficulty of biomedical information retrieval. OBJECTIVE This study aims to find a more systematic method to retrieve relevant scientific literature for a given patient. METHODS In the initial retrieval stage, we supplement query terms through query expansion strategies and apply query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employ a text classification model and relevance matching model to evaluate documents respectively from different dimensions, then we combine the outputs through logistic regression to re-rank all the documents from the initial ranking list. RESULTS The proposed ensemble method contributes to the improvement of biomedical retrieval performance. Comparing with the existing deep learning-based methods, experimental results show that our method achieves state-of-the-art performance on the data collection provided by TREC 2019 Precision Medicine Track. CONCLUSIONS In this paper, we propose a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and the relevance matching model can better capture semantic context information and improve retrieval performance.

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.

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.

An Information Retrieval Expansion Model Based on Wikipedia

2014 ◽  
Vol 977 ◽  
pp. 464-467
Author(s):  
Li Xin Gan ◽  
Wei Tu
Keyword(s):  
Information Retrieval ◽  
Classification Accuracy ◽  
Query Expansion ◽  
Semantic Network ◽  
Computational Cost ◽  
Retrieval Performance ◽  
Network Information ◽  
Key Technologies ◽  
Query Classification ◽  
Expansion Model

Query expansion is one of the key technologies for improving precision and recall in information retrieval. In order to overcome limitations of single corpus, in this paper, semantic characteristics of Wikipedia corpus is combined with the standard corpus to extract more rich relationship between terms for construction of a steady Markov semantic network. Information of the entity pages and disambiguation pages in Wikipedia is comprehensively utilized to classify query terms to improve query classification accuracy. Related candidates with high quality can be used for query expansion according to semantic pruning. The proposal in our work is benefit to improve retrieval performance and to save search computational cost.

scholarly journals Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

2018 ◽  
Vol 14 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Aicha Ghoulam ◽  
Fatiha Barigou ◽  
Ghalem Belalem ◽  
Farid Meziane
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Query Expansion ◽  
Medical Information ◽  
Expansion Method ◽  
Relevant Information ◽  
Medical Texts ◽  
Named Entities ◽  
Medical Information Retrieval ◽  
Large Container

This article describes how many users' queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are element rich with information that helps better target relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this article, the authors present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that their approach reveals interesting improvements in precision, recall and MAP in medical information retrieval.

Study on Information Retrieval Sorting Algorithm in Network-Based Manufacturing Environment

2014 ◽  
Vol 484-485 ◽  
pp. 183-186 ◽  
Author(s):  
Ji Ying Yang ◽  
Bei Zhang ◽  
Yu Mao
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Nonsense Word ◽  
Sorting Algorithm ◽  
Retrieval Performance ◽  
The Core ◽  
Sorting Algorithms ◽  
Response Information ◽  
Pros And Cons ◽  
Core Problem

The core problem of information retrieval is concentrated in the document for the user to retrieve the most relevant sub-set of documents, relying on sorting algorithms on the search results according to relevance sort, sorted the results as the user asked the query response information retrieval performance is determined by many factors, such as to query expressions quality index stemmer nonsense word disabled, query expansion technology, but fundamentally it is determined by the sort function sort function in some Standards document query indicates the degree of matching with the user, and accordingly to make a document with respect to the user's judgment, then the document in accordance with the degree of correlation with respect to the user in descending order, and returns the ordered list of documents as a result of the retrieval the pros and cons of the sorting algorithm directly affect the efficiency of the retrieval.

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