Automatic Query Expansion Research Articles

Multi cascaded transformer network and hybrid heuristic-aided optimal bi-clustering mechanism for patent retrieval system using query expansion

To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance.

Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications.

An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated "query recommendation" into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

SummaryDue to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy‐based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean‐Average Precision, F‐measure, Precision‐recall, and Precision rank are evaluated and analyzed by using TREC‐COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state‐of‐art works. For a total of 60 queries, the proposed model offers an F1‐Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1‐score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

UFTDRDH: A novel user-oriented solution for full-text database retrieval in Chinese digital humanities

Information technology boosts the development of database retrieval in the Chinese digital humanities domain. However, most database providers adopt a system-oriented design pattern, which fails to handle the problem of query gaps in users’ retrieval process. This issue seriously hinders the effective use of database retrieval functionalities, peculiarly among those historical and humanities researchers. To address it, we propose UFTDRDH, a novel user-oriented solution based on automatic query formulation (AQF) technologies, which integrates a human–machine interactive module for the selection of new query-related expansion terms and a powerful query expansion algorithmic component (UFTDRDH-QEV) optimised by a topic-enhancing relevance feedback model approach (ToQE). To verify the effectiveness of UFTDRDH, several comparative experiments are conducted, including quantitative evaluation for retrieval efficiency and user satisfaction, as well as qualitative studies for interpretative traceability. The empirical results are multidimensional and robust, which not only shows the positive effects of different AQFs on gap reduction, especially the importance of query expansion as the most effective technology, but also underlines the remarkably advantageous performance of UFTDRDH compared with traditional system-oriented automatic query expansion in different task contexts. We believe the application of UFTDRDH can further strengthen the research focus on user-centred design and improve the level of current full-text database retrieval in the field of Chinese digital humanities. Broadly speaking, this solution can be also extended to the full-text database retrieval in other languages and digital humanities domains.

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

Word-embedding-based query expansion: Incorporating Deep Averaging Networks in Arabic document retrieval

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies.

Automatic Query Expansion for Term Selection with BERT Score and WordNet Semantic Filtering

Automatic Query Expansion for Term Selection with BERT Score and WordNet Semantic Filtering

Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval

Technical Q&#x0026;A sites (e.g., Stack Overflow (SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&#x0026;A sites and information retrieval approaches (e.g., word embedding-based) have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user&#x2019;s preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed in a query is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, we propose an interactive query refinement approach for question retrieval, named <i>Chatbot4QR</i>, which can assist users in recognizing and clarifying technical details missed in queries and thus retrieve more relevant questions for users. Chatbot4QR automatically detects missing technical details in a query and generates several clarification questions (CQs) to interact with the user to capture their overlooked technical details. To ensure the accuracy of CQs, we design a heuristic-based approach for CQ generation after building two kinds of technical knowledge bases: a manually categorized result of 1,841 technical tags in SO and the multiple version-frequency information of the tags. We develop a Chatbot4QR prototype that uses 1.88 million SO questions as the repository for question retrieval. To evaluate Chatbot4QR, we conduct six user studies with 25 participants on 50 experimental queries. The results are as follows. (1) On average 60.8 percent of the CQs generated for a query are useful for helping the participants recognize missing technical details. (2) Chatbot4QR can rapidly respond to the participants after receiving a query within approximately 1.3 seconds. (3) The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70 percent of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6 percent in terms of two measurements: Pre<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq1-3016006.gif"/></alternatives></inline-formula>k and NDCG<inline-formula><tex-math notation="LaTeX">$@$</tex-math><alternatives><mml:math><mml:mo>@</mml:mo></mml:math><inline-graphic xlink:href="xia-ieq2-3016006.gif"/></alternatives></inline-formula>k. (4) For 48-88 percent of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines (e.g., the SO search engine and Google) using the original queries.

Semantic approaches for query expansion

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

ACE: the Advanced Cohort Engine for searching longitudinal patient records.

ObjectiveTo propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.Materials and MethodsThe Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE’s temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.ResultsACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.DiscussionACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.ConclusionACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.

Common sense based automatic query expansion

Query expansion techniques are widely applied for improving the efficiency of the textual information retrieval systems. We expand the original query with additional relevant terms which helps in better understanding of the query and user’s mind-set. The need for query expansion comes from the fact that it is difficult to formulate a well-designed query that gives relevant results as per user’s expectations. In this paper, query expansion is performed by adding new terms that users generally skip in their query assuming them to be common sense. This common sense is the link between the terms of the original query. We used ConceptNet, also called Common Sense Network, to add the missing terms. Experimental results show that the proposed method of query expansion greatly enhance the efficiency obtained over traditional queries.

A contemporary combined approach for query expansion

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve.

A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR

Automatic query expansion (AQE) is an effective measure to improve information retrieval performance by including additional terms in a user query. The pseudo relevance feedback (PRF) method employed for AQE so far has suffered from a major problem of query drift. Therefore, keeping it in view, a new hybrid document clustering for PRF based AQE approach is proposed in the present article. In this, Fuzzy logic and Particle Swarm Optimization (PSO) are used to construct document clusters. Further, a new and effective hybrid PSO and Fuzzy logic-based term weighting approach is followed to find more suitable additional query terms using a weighted score of four IR evidences which is considered maximized. Moreover, a combined semantic filtering method along with query terms re-weighting algorithms are also used to remove noisy or irrelevant terms semantically. The performance of the presented approaches in this article is tested and compared with other approaches on three benchmark data sets. The comparative analysis of all the tested approaches illustrates the superior performance of the proposed approach.

A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system

Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches.

Collaborative feature location in models through automatic query expansion

Collaboration with other people is a major theme in the information-seeking process. However, most existing works that address the location of features during the maintenance or evolution of software do not support collaboration, or they are focused on code as the main software artifact. Hence, collaborative feature location in models has not enjoyed much attention to date. In this work, we address this concern by proposing an approach, CoFLiM, that enables the collaboration of several domain experts in order to locate the model fragment of a target feature. CoFLiM uses the feature descriptions of the domain experts and their self-rated confidence level to automatically reformulate the relevant feature descriptions in a single query. This query guides the evolutionary algorithm of our approach that finds the model fragment of the feature being located. We evaluate CoFLiM in a real-world case study from our industrial partner. We analyze the impact of CoFLiM in terms of recall, precision, and the F-measure. Moreover, we compare the reformulation of CoFLiM with four baselines. We also perform a statistical analysis to show that the impact of the results is significant. Our results show that collaboration pays off in the location of features in models. The results also show that the self-rated confidence level can be used to locate features in models. Finally, the results show that there are no significant improvements when more than three domain experts are involved, which is relevant in those industrial contexts where the availability of domain experts is scarce.

Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.

Advances in IR Query Expansion

As the internet is becoming rich with huge information, retrieving the wanted information is a huge challenge. Due to huge variation in framing a query by the individuals, matching of the terms with the document to be searched are facing many challenges. Researchers have worked on problem of identifying the correct essence of the user query by adding additional useful terms. They are currently working on this kind of Query Expansion (QE). There are many proposals like Manually, Iterative and Automatic QE. In this work we compare these different techniques and analyze for their strength and weaknesses.

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval.

The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

Modeling multiple interactions with a Markov random field in query expansion for session search

AbstractHow to automatically understand and answer users' questions (eg, queries issued to a search engine) expressed with natural language has become an important yet difficult problem across the research fields of information retrieval and artificial intelligence. In a typical interactive Web search scenario, namely, session search, to obtain relevant information, the user usually interacts with the search engine for several rounds in the forms of, eg, query reformulations, clicks, and skips. These interactions are usually mixed and intertwined with each other in a complex way. For the ideal goal, an intelligent search engine can be seen as an artificial intelligence agent that is able to infer what information the user needs from these interactions. However, there still exists a big gap between the current state of the art and this goal. In this paper, in order to bridge the gap, we propose a Markov random field–based approach to capture dependence relations among interactions, queries, and clicked documents for automatic query expansion (as a way of inferring the information needs of the user). An extensive empirical evaluation is conducted on large‐scale web search data sets, and the results demonstrate the effectiveness of our proposed models.

Modeling reformulation queries into snippets for improving automatic query expansion

Modeling reformulation queries into snippets for improving automatic query expansion