Keyword Queries Research Articles

Efficient Strong Privacy-Preserving Conjunctive Keyword Search Over Encrypted Cloud Data

Searchable symmetric encryption (SSE) supports keyword search over outsourced symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a variant of SSE, further enables data updating. Most DSSE works with conjunctive keyword search primarily consider forward and backward privacy. Ideally, the server should only learn the result sets involving all keywords in the conjunction. However, existing schemes suffer from keyword pair result pattern (KPRP) leakage, revealing the partial result sets containing two of query keywords. We propose the first DSSE scheme to address aforementioned concerns that achieves strong privacy-preserving conjunctive keyword search. Specifically, our scheme can maintain forward and backward privacy and eliminate KPRP leakage, offering a higher level of security. The search complexity scales with the number of documents stored in the database in several existing schemes. However, the complexity of our scheme scales with the update frequency of the least frequent keyword in the conjunction, which is much smaller than the size of the entire database. Besides, we devise a least frequent keyword acquisition protocol to reduce frequent interactions between clients. Finally, we analyze the security of our scheme and evaluate its performance theoretically and experimentally. The results show that our scheme has strong privacy preservation and efficiency.

Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature

BackgroundDrug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper.ResultsPubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively.ConclusionsBy integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.

How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.

Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries

Geo-textual objects with both geographical location and textual description are gaining in prevalence. Over the past decades, substantial research has been conducted on spatial keyword queries, which integrate location into keyword-based querying of geo-textual content. However, existing proposals mostly focus on efficiency for processing spatial keyword queries, and little effort was made to address the effectiveness perspectives. In this work, using two datasets with ground truth query results, we evaluate the effectiveness of standard spatial keyword queries. Our evaluation results show that the TkQ query that ranks objects by a weighted combination of spatial proximity and text relevance is the most effective. Motivated by the finding, we propose a Deep relevance with Weight learning (DrW) model to further improve the effectiveness of the retrieval ranking. DrW is featured with two novel ideas: First, we propose a neural network architecture to learn the text relevance matching over the local interaction between the query and geo-textual objects. Second, we find that a query-dependent weight to balance text relevance and spatial proximity in ranking can improve effectiveness, and we develop a learning-based method to learn the query-dependent weight. Experimental results reveal that our model outperforms state-of-the-art methods on effectiveness, with improvements up to 32.15%, 32.34%, and 33.00% in terms of NDCG@3, NDCG@5, and MRR.

Learned-Index-Based Semantic Keyword Query on Blockchain

Blockchain has become increasingly popular for data management in recent years. However, the existing blockchain systems lack efficient semantic queries, particularly keyword queries. To address this issue, we propose a learned-index-based semantic keyword query architecture on blockchain. First, our architecture records data semantics information to support semantic keyword queries. Second, we establish the lookup table index for semantic information among blocks and the block-level recursive model index for blocks to improve the query efficiency. We store the lookup table in the extended block headers to maintain the result’s completeness, and we store recursive model indexes off chain to optimize the maintenance efficiency. Third, we propose a verifiable query algorithm based on our proposed architecture to maintain the result’s correctness. Finally, the experimental results show that combining the lookup table and the learned index effectively improves the query efficiency on blockchain.

Symmetric searchable encryption with supporting search pattern and access pattern protection in multi‐cloud

SummarySymmetric searchable encryption (SSE) enables users to search the ciphertext stored on the untrusted cloud without revealing the search keywords, effectively protecting users' privacy. However, most of the existing SSE schemes reveal the search or access pattern during the keyword query, which can be used by the adversary to infer the sensitive information in ciphertext, thus posing a great threat to users' privacy. To address this, we propose an SSE scheme supporting search pattern and access pattern protection in multi‐cloud, called SAPM‐SSE. In our scheme, an index shuffle protocol is proposed to change the content and location of the index after each query, which helps to achieve the protection of search and access pattern. Furthermore, with the purpose of improving the efficiency of shuffling, we construct a shuffling algorithm based on index cache, the number of index entries for shuffling reduced from γ to γ/n (n≥1). Besides, our scheme supports the dynamic update of documents and achieves the forward security in update. Finally, security analysis and experimental results show that our scheme can achieve the protection of search pattern and access pattern with high efficiency.

CASE-SSE: Context-Aware Semantically Extensible Searchable Symmetric Encryption for Encrypted Cloud Data

Traditional searchable symmetric encryption (SSE) schemes rarely support context-aware semantic extension, and then lead to the searched results being incomplete or deviating from the user’s query intention. To address this problem, a new context-aware semantically extensible searchable symmetric encryption based on Word2vec model (CASE-SSE) is proposed to achieve context-aware semantic extension in this article. The proposed scheme utilizes outsourced datasets as corpora to extract all keywords for training the Word2vec model, and the trained results is the ontology knowledge base that can be used to extend the semantics of query keywords directly. Further, to facilitate multi-keyword search using the extended query vector, we use the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clustering algorithm to classify outsourced datasets. We then construct an AVL-tree index and an inverted index based on the classified results, thereby achieving efficient context-aware semantically extensible SSE. The security analysis indicates it is secure and effective. The experimental results show that our scheme is superior in both efficiency and accuracy.

Systematic Literature Review of Conversational Code-Switching in Multilingual Society From a Sociolinguistic Perspective

Code-switching (CS) is widely used across the globe despite the unclear research trends and gaps in CS studies due to under-researched reviews on it. The current study is a systematic literature review (SLR) of conversational CS from a sociolinguistic perspective from 2010 to 2022 using the PRISMA 2020 framework. Keywords query was performed at Oct 31, 2022 on Scopus and Web of Science databases. As a result, a total of 117 articles were included for further analysis. It is found that the number of CS studies was continuously increasing before encountering declines from 2019. Previously, scholars preferred empirical studies, qualitative designs, and data collection methods including discourse analysis, observation, interview and questionnaire. Regarding research objectives, a majority of studies examined the factor of CS, mainly from the micro levels. Besides that, many studies had explored attitude and identity towards CS in the past five years. As for research contexts, Asia became the research centre of previous CS studies. However, there was a lack of CS studies worldwide, especially among Oceania, South America and Africa. Multilingual societies in the Expanding Circle require more discussion.

Antarctic science in Chile: a bibliometric analysis of scientific productivity during the 2009–2019 period

AbstractThe changes implemented in 2005 in the development strategies of Antarctic science carried out by Chile have had a positive impact on the scientific productivity of the Chilean Antarctic Science Program (PROCIEN). We analysed scientometric indicators from between 2009 and 2019. The bibliographic data were extracted from the Web of Science database using search query keywords. We used multiple correspondence analysis to identify specific trends and also network analyses of international collaboration in VOSviewer. The number of Antarctic science publications in Chile has gradually increased from 21 in 2009 to 95 in 2019. The rise in the number of articles was higher in journals for the first impact factor quartile. Research lines showing increased first-quartile impact factor papers corresponded to Antarctic ecosystems, biotechnology and geosciences. The main geographical domains in which such research activities have been carried out corresponded to in the South Shetland Islands and the Antarctic Peninsula. Fieldwork data are the main sources for the production of scientific articles, and there are three science platforms within which most of these papers concentrate. The diversification of funding sources, the implementation of improvements in the selection process and Chile's alignment with Scientific Committee on Antarctic Research programmes have contributed to improving the science that Chile has developed in Antarctica.

Keywords Query of uncertain spatiotemporal data based on XML

Keywords Query of uncertain spatiotemporal data based on XML

An Efficient Association Rule Mining-Based Spatial Keyword Index

Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.

Supporting Schema References in Keyword Queries Over Relational Databases

Supporting Schema References in Keyword Queries Over Relational Databases

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.

Dynamic monitoring of network public opinion diffusion of major public crisis based on deviation degree

In order to solve the problems existing in traditional methods such as low accuracy and long monitoring time, a dynamic monitoring method of network public opinion diffusion of major public crisis based on deviation degree is proposed. Firstly, the crawler is used to collect network public opinion data, and the collected data is preprocessed. Secondly, the commonly used query keywords in search engines are classified into statistics, and the diffusion anomalies of network public opinions on major public crisis are monitored with the deviation degree. Finally, according to the monitoring results, a report on the network public opinion of major public crisis is generated to realise the dynamic monitoring of the spread of network public opinion of major public crisis. Experimental results show that the designed dynamic monitoring method has the characteristics of high accuracy, low deviation, short monitoring time and high reliability, and the practical application effect is good.

A Public Key Searchable Encryption Method Based on Multiple Keywords

In recent years, the secure search of encrypted cloud data has become a hot research topic and a challenging task. Several secure search schemes have been proposed to address this challenge. However, existing public-key searchable encryption schemes still face many problems. Among these schemes, most of them are based on single-key searchable encryption schemes, and although some schemes are designed for multi-key search, they still disclose the secret information of the encrypted index. Based on this, this paper proposes a multi-key public-key searchable encryption scheme without a secure channel, which uses a random element padding method to construct an encrypted index to ensure the security of the index information, and then improves the efficiency of the search by aggregating the query keyword information to generate query trapdoors. The simulation results of the algorithm show that the algorithm improves the query efficiency and query accuracy under the condition that the index and trapdoor are secure.

PM-Search: State-of-the-art Evidence Retriever for Precision Medicine: Algorithm Development and Validation.

Under the paradigm of Precision Medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace. In this work, we propose the PM-Search system to facilitate the retrieval of clinical literature that contains critical evidence for or against giving specific therapies to certain cancer patients. The PM-Search system combines a Baseline Retriever that selects document candidates at a large scale and an Evidence Re-ranker that finely reorders the candidates based on their evidence quality. The Baseline Retriever uses query expansion and keyword matching with the Elasticsearch retrieval engine, and the Evidence Re-ranker fits pre-trained language models to expert annotations that are derived from an active learning strategy. The PM-Search system achieves the best performance in the retrieval of high-quality clinical evidence at the TREC PM Track 2020, outperforming the second-ranking systems by large margins (0.4780 vs. 0.4238 for standard NDCG@30 and 0.4519 vs. 0.4193 for exponential NDCG@30). We present PM-Search, a state-of-the-art search engine to assist the practicing of evidence-based PM. PM-Search uses a novel BioBERT-based active learning strategy that models evidence quality and improves the model performance. Our analyses show that evidence quality is a distinct aspect from general relevance, and specific modeling of evidence quality beyond general relevance is required for a PM search engine.

On the role of human and machine metadata in relevance judgment tasks

In order to evaluate the effectiveness of Information Retrieval (IR) systems it is key to collect relevance judgments from human assessors. Crowdsourcing has successfully been used as a method to scale-up the collection of manual relevance judgments, and previous research has investigated the impact of different judgment task design elements (e.g., highlighting query keywords in the document) on judgment quality and efficiency. In this work we investigate the positive and negative impacts of presenting crowd human assessors with more than just the topic and the document to be judged. We deploy different variants of crowdsourced relevance judgment tasks following a between-subjects design in which we present different types of metadata to the human assessor. Specifically, we investigate the effect of human metadata (e.g., what other human assessors think of the current document, as in which relevance level has already been selected by the majority crowd workers), machine metadata (e.g., how IR systems scored this document such as its average position in ranked lists, statistics about the document such as term frequencies). We look at the impact of metadata on judgment quality (i.e., the level of agreement with trained assessors) and cost (i.e., the time it takes for workers to complete the judgments) as well as at how metadata quality positively or negatively impact the collected judgments.

Evaluation of the effect of the COVID-19 pandemic on mucormycosis studies with bibliometric analysis

Coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) is a relatively new concept in the literature that emerged during the pandemic. Bibliometric analysis is a type of analysis that uses mathematical and statistical methods to study the formal properties of knowledge areas. This study aimed to reveal the main themes, conceptual structures, and trends of bibliometric studies on mucormycosis in 2 different periods, pre-and during the pandemic. This study consisted of 2 periods: pre-COVID-19 and COVID-19. Articles were collected from the Web of Science (WOS) Core Collection database. We provided AND and OR connectors for the keyword query and selected studies based on relevant keywords. Collected data were classified based on their publication date and examined using the R programming language (Version 4.0.3) package Bibliometrix and SciMAT Software. A total of 1261 articles were investigated, and performance and information structure analyses were conducted. Based on Bradford's law, the Journal of Fungi was the top-ranked journal in both periods. Cureus and mycoses were placed 2nd and 3rd in the second period. India is the largest contributor. In performance analysis, conceptual structures such as Rhizopus oryzae, epidemiology, diagnosis, management, treatment, and outcomes were at the forefront of mucormycosis publications during the COVID-19 period. Research trends have shifted to the clinical treatment and management of COVID-19. Therefore, pathogenesis, diagnosis, follow-up, and treatment strategies for CAM should be developed in the future.

Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of ML applications in any domain can only be ensured if intelligent capabilities cover all of its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. First, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Second, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/cross-domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned previously be addressed with improved ML architecture and agile operations and propose an ML pipeline based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain, and multi-layer environment and provide decision making on a global or holistic rather than an individual function level.