Query Expansion Research Articles

Query expansion using Haar wavelet transform

Novice users are unable to express their information needs properly, due to this it is difficult to retrieve all the desired relevant documents from the test collection. The problem of word mismatch is fundamental to information retrieval. Query expansion is a technique in which additional terms are added to retrieve relevant documents. In this article, we have expanded the query using pseudo-relevance feedback and Haar wavelet transform. The performance of the proposed technique is evaluated on FIRE 2011 ad hoc English test Collection and Robust dataset. The mean average precision of the proposed model on the FIRE dataset and Robust Track dataset is 0.3334 and 0.2724, respectively.

Design of Question Answering Interactive Open Platform for Power Grid Business Acceptance Robot

In order to improve the matching degree between answers and questions of the power grid business acceptance robot question answering platform, an interactive open platform for power grid business acceptance robot question answering is designed. Firstly, an open platform of questions and answers interaction with data layer and information retrieval layer as the main body is constructed. To improve the question and answer interactive open platform database, the data layer is divided into three modules: data collecting, database setup, and data storage. At the same time, the information retrieval layer is divided into user input, answer display, query expansion, and information retrieval modules. To improve the retrieval accuracy, the corresponding answers are matched by using the similarity between questions and documents in the information retrieval module, and finally, the design of the question and answer interactive open platform of power grid business acceptance robot is realized. The experimental results demonstrate that the designed platform’s service acceptance accuracy is high, the AUC curve is low, and the degree of matching between replies and queries is high.

Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval.

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

THE NECESSITY OF EVIDENCE BASED MEDICINE IN HEALTH CARE SYSTEM - AN OVERVIEW OF THE PRESENT CRITICAL CONDITIONS

The practice of evidence based clinical practice involves assimilation of individual clinical practice and being able to analyze the best available evidence in decision- making concerning patient and family centered care. It is difcult for the health care practitioners to keep pace with the volume of upcoming medical literature. In consequence, most of the questions remain unanswered. To be at par with the information overload, clinicians mainly depend on expert-compiled clinical practice guidelines. The gold standard is to possess guidelines according to the needs of individual countries or regions depending on the demographic distribution of the country. But in cases where this method is not feasible it is the adoption of the International processes to meet the needs. This paper highlights this issue and encourages the fact that the clinical query expansion algorithm can close the existing gaps and aid in the development of effective evidence based clinical practice.

Relevance Feedback and Deep Neural Network-Based Semantic Method for Query Expansion

Machine learning techniques have been widely used in almost every area of arts, science, and technology for the last two decades. Document analysis and query expansion also use machine learning techniques at a broad scale for information retrieval tasks. The state-of-the-art models like the Bo1 model, Bo2 model, KL divergence model, and chi-square model are probabilistic, and they work on DFR-based retrieval models. These models are much focused on term frequency and do not care about the semantic relationship among the terms. The proposed model applies the semantic method to find the semantic similarity among the terms to expand the query. The proposed method uses the relevance feedback method that selects a user-assisted most relevant document from top “ k ” initially retrieved documents and then applies deep neural network technique to select the most informative terms related to original query terms. The results are evaluated at FIRE 2011 ad hoc English test collection. The mean average precision of the proposed method is 0.3568. The proposed method also compares the state-of-the-art models. The proposed model observed 19.77% and 8.05% improvement on the mean average precision (MAP) parameter with respect to the original query and Bo1 model, respectively.

Semantic-Based Hybrid Query Reformulation for Biomedical Information Retrieval

Abstract Query reformulation is a well-known technique intended to improve the performance of Information Retrieval Systems. Among the several available techniques, Query Expansion (QE) reformulates the initial query by adding similar terms, drawn from several sources (corpus, knowledge resources), to the query terms in order to retrieve more relevant documents. Most QE methods are based on the relationships between the original query term and candidate terms (new terms) in order to select the most similar expansion terms. In this paper, we suggested a new hybrid query reformulation through QE and term re-weighting techniques. The suggested approach aimed to demonstrate the effectiveness of QE with a semantic selection of candidate terms according to the specificity of original query terms in the improvement of retrieval performance. To this end, we exploited both relationships defined by knowledge resources and the distributed semantics, recently revealed by neural network analysis. For term re-weighting, we proposed a new semantic method based on semantic similarity measure that assigns a weight to each term of the expanded query. The conducted experiments on OHSUMED and TREC 2014 CDS test collections, including long and short queries, yielded significant results that outperformed the baseline and state-of-the-art approaches.

Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.

Retraction Note to: Query expansion for patent retrieval using a modified stellar-mass black hole optimization

Retraction Note to: Query expansion for patent retrieval using a modified stellar-mass black hole optimization

Transmission and Drug Resistance Characteristics of Human Immunodeficiency Virus-1 Strain Using Medical Information Data Retrieval System.

This study was aimed at exploring the transmission and drug resistance characteristics of acquired immunodeficiency syndrome (AIDS) caused by human immunodeficiency virus-1 (HIV-1). The query expansion algorithm based on Candecomp Parafac (CP) decomposition was adopted to construct a data information retrieval system for semantic web and tensor decomposition. In the latent variable model based on tensor decomposition, the three elements in the triples generated feature vectors to calculate the training samples. The HIV patient data set was selected to evaluate the performance of the system, and then, the HIV gene resistance of 213 patients was retrospectively analyzed based on the electronic medical records. 43 cases showed failure of ribonucleic acid drug resistance, the ART virological failure rate was 24.43% (43/213), and one case was not reported. There was 1 case of RNA hemolysis that could not be detected. There were 50 resistant cases of nonnucleotide reverse transcriptase inhibitors (NNRTI), accounting for 29.94% (50/167), and there were 17 resistant cases of nucleotide reverse transcriptase inhibitors (NRTI), accounting for 10.18% (17/167) of all mutation cases. Among the HIV-1 strains, 19 cases failed the detection of drug resistance sites in the integrase region, and mutations in the integrase region were significantly more than those in the protease region. There were 12 types of HIV-1 strains with drug-resistant mutations. The fusion technical scheme constructed in this study showed excellent performance in medical information retrieval. In this study, the characteristics of HIV-1 of AIDS patients were analyzed from different directions, and effective treatment was performed for patients, so as to provide reference for clinical diagnosis of AIDS patients.

Geodata source retrieval by multilingual/semantic query expansion: the Case of Google Translate and WordNet version 3.1

Abstract. In this article, we examined the potential of the current version of WordNet and Google Translate API to enhance the quality of geodata source retrieval in the Dutch geoinformation portal (PDOK) using semantic keywords for the geographic phenomena requested. Keywords gathered from real users’ questions in natural language extracted in an English corpus. Then, these keywords were expanded using WordNet and Google Translate API. Lastly, the results of query expansion were evaluated compared to a manual gold standard and based on information retrieval metrics. Our study shows that the results of query expansion help users by reformulating good alternative queries.

Improving query expansion using pseudo-relevant web knowledge for information retrieval

• Web knowledge-based query expansion technique uses the top N pseudo relevant web pages • Top N pseudo relevant web pages returned by search engines Google, Bing, and DuckDuckGo as data sources • Proposed weighting techniques have properly capture the term relationship while weighing the expansion terms • Experimental results show an improvement of 25.89% over the unexpanded queries of FIRE dataset In the field of information retrieval, query expansion (QE) has long been used as a technique to deal with the fundamental issue of word mismatch between a user’s query and the target information. In the context of the relationship between the query and expanded terms, existing weighting techniques often fail to appropriately capture the term-term relationship and term to the whole query relationship, resulting in low retrieval effectiveness. Our proposed QE approach addresses this by proposing three weighting models based on (1) tf-idf, (2) k-nearest neighbor (kNN) based cosine similarity , and (3) correlation score. Further, to extract the initial set of expanded terms, we use pseudo-relevant web knowledge consisting of the top N web pages returned by the three popular search engines namely, Google, Bing, and DuckDuckGo, in response to the original query. Among the three weighting models, tf-idf scores each of the individual terms obtained from the web content, kNN-based cosine similarity scores the expansion terms to obtain the term-term relationship, and correlation score weighs the selected expansion terms with respect to the whole query. The proposed model, called web knowledge based query expansion (WKQE), achieves an improvement of 25.89% on the Mean Average Precision (MAP) score and 30.83% on the Geometric Mean Average precision (GMAP) score over the unexpanded queries on the FIRE dataset. A comparative analysis of the WKQE techniques with other related approaches clearly shows significant improvement in the retrieval performance. We have also analyzed the effect of varying the number of pseudo-relevant documents and expansion terms on the retrieval effectiveness of the proposed model.

Contextual Query Expansion for Conducting Technology-Assisted Biomedical Reviews

Given a biomedical research question, formalizing a query for retrieving relevant evidence from Biomedical data-sets is an important methodological step in conducting literature reviews and has been the topic of a number of works on technology-assisted reviews. In this paper, we provide a deep structure that learns to effectively generate terms for expanding Biomedical queries for searching over the data-sets referenced by PubMed, the major search system for Biomedical studies. We use and fine-tune BERT for modelling contextual relationship among terms in the textual content. We evaluate our work on the CORD-19 data-set and show its effectiveness compared with the baselines

Query expansion – Hybrid framework using fuzzy logic and PRF

Several factors hinder information retrieval in the medical profession. Consumers (layman people) often struggle to learn medical terms. Because medical terms are more evident to professionals, it is difficult for consumers to construct a query using medical terms. Consumers would find it easier to access relevant medical information if medical words relevant to their query were automatically added. Various kiosks use approaches using machine vision to form the user queries and monitor their health. This work proposes a hybrid approach to term selection by expanding user queries with medical terms relevant to the medical query. The selection of terms is based on fuzzy similarity reasoning based on two primary term selection strategies. WordNet semantic filtering is applied for preventing query drift, followed by the calculation of BERTScore. The retrieved documents were ranked using Okapi-BM25. For evaluation purposes, six benchmark datasets have been used: CACM, CISI, MEDLINE, ADI, FIRE, and TREC (Covid-19). The results indicate that the suggested technique outperforms the current state-of-the-art.

Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.

CEQE to SQET: A study of contextualized embeddings for query expansion

CEQE to SQET: A study of contextualized embeddings for query expansion

A hybrid query expansion framework for the optimal retrieval of the biomedical literature

With the proliferation of biomedical literature, it is quite challenging for biomedical scientists to keep them updated with the new advancements. In biomedical literature retrieval systems, the keywords in the user-defined queries are often defined with various lexical variants consequently leading to the vocabulary mismatch (VM). One possible way to cope with these issues is to introduce a query expansion (QE) framework to enrich the original queries with the auxiliary semantically similar terms for each keyword mentioned in a query. In this research, we propose a biomedical QE framework to alleviate the VM. The proposed approach combines the clinical diagnosis information (CDI) and word embeddings (WEs) simultaneously to retrieve the relevant biomedical literature. The process of embeddings vocabulary terms as real-valued and low dimensional vectors referred to as word embedding has garnered significant attention by potentially capturing the implicit semantics. We have exploited threefold word embeddings (Domain-Specific, Domain-Agnostic, and Hybrid) and integrated the embeddings outcomes with the CDI to get the best query combination for the efficient retrieval of biomedical literature. Experimental results procured for the Text REtrieval Conference dataset showed that CDI, when used with the hybrid word embeddings surpassed the WEs trained for the domain-specific and domain-agnostic data. The results demonstrate that the utilization of this unique setup of merging two techniques is a valuable addition to the QE process leading to significantly improved precision rate and VM in biomedical literature retrieval. We hope that our approach would assist investigators to use this query combination to retrieve relevant articles.

Re-ranking vehicle re-identification with orientation-guide query expansion

Vehicle re-identification, which aims to retrieve information regarding a vehicle from different cameras with non-overlapping views, has recently attracted extensive attention in the field of computer vision owing to the development of smart cities. This task can be regarded as a type of retrieval problem, where re-ranking is important for performance enhancement. In the vehicle re-identification ranking list, images whose orientations are dissimilar to that of the query image must preferably be optimized on priority. However, traditional methods are incompatible with such samples, resulting in unsatisfactory vehicle re-identification performances. Therefore, in this study, we propose a vehicle re-identification re-ranking method with orientation-guide query expansion to optimize the initial ranking list obtained by a re-identification model. In the proposed method, we first find the nearest neighbor image whose orientation is dissimilar to the queried image and then fuse the features of the query and neighbor images to obtain new features for information retrieval. Experiments are performed on two public data sets, VeRi-776 and VehicleID, and the effectiveness of the proposed method is confirmed.

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

A study of the Web visibility of the SDGs and the 2030 Agenda on university websites

PurposeUniversities play an important role in the promotion and implementation of the 2030 Agenda for Sustainable Development. This study aims to examine the visibility of information about the Sustainable Development Goals (SDGs) on the websites of Spanish and major international universities, by means of a quantitative and qualitative analysis with an online visibility management platform that makes use of big data technology.Design/methodology/approachThe Web visibility of the universities studied in relation to the terms “SDG”, “Sustainable Development Goals” and “2030 Agenda” was determined using the SEMrush tool. Information was obtained on the number of web pages accessed and the queries formulated (query expansion). The content indexed by Google for these universities was compiled, and finally, the search engine optimization (SEO) factors applicable to the websites with the highest Web visibility were identified.FindingsThe universities analysed are content creators but do not have very high Web visibility in Web searches for information on the SDGs. Of the 98 universities analysed, only four feature prominently in search results.Originality/valueAlthough research exists on the application of SEO to different areas, there have not, to date, been any studies examining the Web visibility of universities in relation to Web searches for information on the 2030 Agenda. The main contributions of this study are the global perspective it provides on the Web visibility of content produced by universities about the SDGs and the recommendations it offers for improving that visibility.

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the model based on mean average precision is 0.3176. The proposed model compares with other existing models. An improvement of 6.61%, 6.93%, and 9.07% on MAP value is observed compare to the Original query, BM25 model, and query expansion with the Chi-Square model respectively. The proposed model also retrieves 84, 25, and 81 additional relevant documents compare to the original query, query expansion with Chi-Square model, and BM25 model respectively and thus improves the recall value also. The per query analysis reveals that the proposed model performs well in 30, 36, and 30 queries compare to the original query, query expansion with Chi-square model, and BM25 model respectively.