Information Retrieval Model Research Articles

Review on Recent Arabic Information Retrieval Techniques

Information retrieval is an important field that aims to provide a relevant document to a user information need, expressed through a query. Arabic is a challenging language that gained much attention recently in the information retrieval domain. To overcome the problems related to its complexity, many studies and techniques have been presented, most of them were conducted to solve the stemming problem. This paper presents an overview of the Arabic information retrieval process, including various text processing techniques, ranking approaches, evaluation measures, and some important information retrieval models. The paper finally presents some recent related studies and approaches in different Arabic information retrieval fields.

What is policy content and how is the public's policy support? A policy cognition study based on natural language processing and social psychology.

Public policy plays a critical role in coordinating social resources, providing public services, and promoting public value. However, few studies have systematically investigated the public's policy cognition from the objective aspect of policy content and the subjective aspect of target audience's policy support. To fill this gap, we explore the policy content via natural language processing and also policy support from the perspective of social psychology. Specifically, regarding the policy content analysis, we collect over one thousand policy documents and design an annotation system by analyzing the policy content, basic structure and text features, and then utilize text classification and information retrieval models based on the Chinese large-scale pre-trained models. Moreover, according to the policy tools identified in the content analysis, our paper investigates the audience's policy support for these tools and its influential factors. Based on a sample of 476 respondents from the whole country, we adopt the ordinary least square method to explore the key factors of policy support for supply, demand and environmental tools from three variables: political trust, policy effectiveness and knowledge of policies. This study contributes to existing literature by forming a structured understanding of policies and exploring public support for policy tools, which could benefit the practitioners during the policy-making process and also enhance successfully policy implementation.

Design of Cross-Platform Information Retrieval System of Library Based on Digital Twins.

In order to improve the library's ability of cross-platform information retrieval and data scheduling and distribution, a library cross-platform information retrieval system based on digital twin technology is designed. Using data warehouse decision support and data source structured query methods, the spectral characteristics of Library cross-platform information resources are extracted. Using the method of Hadoop data parallel loading, the library cross-platform operation data is divided into decision-making data, computing resource pool data, and Hadoop parallel loading data. A library cross-platform information digital twin parallel retrieval and information fusion feature matching model is established, and the retrieval channels are allocated through multiple complex and balanced task scheduling sequences. According to the queue configuration model of Library cross-platform information retrieval, the optimization design of Library cross-platform information retrieval system is realized. The simulation test results show that the designed system has good recall ability of cross-platform information retrieval data, and improves the utilization rate of cross-platform resources and the dynamic scheduling ability of online resources.

A Tailored Approach: A model for literature searching in complex systematic reviews

Our previous work identified that nine leading guidance documents for seven different types of systematic review advocated the same process of literature searching. We defined and illustrated this process and we named it ‘the Conventional Approach’. The Conventional Approach appears to meet the needs of researchers undertaking literature searches for systematic reviews of clinical interventions. In this article, we report a new and alternate process model of literature searching called ‘A Tailored Approach’. A Tailored Approach is indicated as a search process for complex reviews which do not focus on the evaluation of clinical interventions. The aims of this article are to (1) explain the rationale for, and the theories behind, the design of A Tailored Approach; (2) report the current conceptual illustration of A Tailored Approach and to describe a user’s interaction with the process model; and (3) situate the elements novel to A Tailored Approach (when compared with the Conventional Approach) in the relevant literature. A Tailored Approach suggests investing time at the start of a review, to develop the information needs from the research objectives, and to tailor the search approach to studies or data. Tailored Approaches should be led by the information specialist (librarian) but developed by the research team. The aim is not necessarily to focus on comprehensive retrieval. Further research is indicated to evaluate the use of supplementary search methods, methods of team-working to define search approaches, and to evaluate the use of conceptual models of information retrieval for testing and evaluation.

Rough set generalization models based information retrieval

World Wide Web has become the largest pool of knowledge in terms of volume, but web users still continue to have difficulty in finding the relevant information. Two issues influence the output of information retrieval (IR) systems: first, information overload and second, the vagueness and imprecision in document representations as well as user information need. Rough set theory (RST) proved successful in tackling ambiguous, uncertain, imprecise, or incomplete information. RST is being used in a range of artificial intelligence applications as a result of this. RST has several uses, one of which is IR. This survey focused on Rough Sets and their generalization models in IR. Some previous studies attempted to comprehend rough set-based IR models, but their scope was limited. This research examines various rough set-based IR models, their basic methodologies, features, strengths, and limitations systematically. For critical analysis, a comparison of the existing frameworks is also presented.

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.

On the analysis and evaluation of information retrieval models for social book search

On the analysis and evaluation of information retrieval models for social book search

Dataset Search and Augmentation

Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to effectively perform dataset search and augmentation. We start by providing an end-to-end description of a dataset search engine following the lifecycle of datasets. Our review includes web dataset acquisition techniques, dataset profiling and augmentation methods, and dataset search tasks and corresponding methods. In order to extract datasets from research articles, we present an information extraction framework to determine triples of interest which can be used for academic dataset search. We propose a feature-based method to augment tabular datasets with additional schema labels to help users and systems to better understand the datasets. We develop three methods for tabular dataset search: the first utilizes generated schema labels to enhance the search results; the second adopts pretrained language models to learn matching features; the third models the complex relations in the datasets as one or more graphs and uses graph neural networks to learn representations of queries and tables. To support dataset search in which a query is also a dataset, we propose universal dataset encoders which regard a dataset as a point set so that the encoded dataset representations can be used to search for similar datasets. Extensive experiments across multiple tasks demonstrate the superiority of our proposed methods over the state of the art. Awarded by: Lehigh University, Bethlehem, USA on 10 May 2022. Supervised by: Brian D. Davison. Available at: https://github.com/Zhiyu-Chen/Dissertation/blob/main/Dissertation_Dataset_Search.pdf.

IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision

IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision

Research on Semantic Information Retrieval Based on Improved Fish Swarm Algorithm

In order to improve the effectiveness of semantic information retrieval, the improved fish swarm algorithm is proposed to carry out semantic information retrieval. Firstly, the system of semantic information retrieval is designed, and theory model of search engine is established. Secondly, the information retrieval model based on semantic similarity is constructed, and the mathematical model is deduced. Thirdly, the improved fish algorithm is established, and the analysis procedure of it is designed. Finally, the simulation analysis of semantic information retrieval is carried out, results show that the proposed model can obtain higher classification accuracy, precision rare and recall rate, therefore it has higher performance on semantic information retrieval.

Meta-search based approach for Arabic information retrieval

PurposeThe semantic relations between Arabic word representations were recognized and widely studied in theoretical studies in linguistics many centuries ago. Nonetheless, most of the previous research in automatic information retrieval (IR) focused on stem or root-based indexing, while lemmas and patterns are under-exploited. However, the authors believe that each of the four morphological levels encapsulates a part of the meaning of words. That is, the purpose is to aggregate these levels using more sophisticated approaches to reach the optimal combination which enhances IR.Design/methodology/approachThe authors first compare the state-of-the art Arabic natural language processing (NLP) tools in IR. This allows to select the most accurate tool in each representation level i.e. developing four basic IR systems. Then, the authors compare two rank aggregation approaches which combine the results of these systems. The first approach is based on linear combination, while the second exploits classification-based meta-search.FindingsCombining different word representation levels, consistently and significantly enhances IR results. The proposed classification-based approach outperforms linear combination and all the basic systems.Research limitations/implicationsThe work stands by a standard experimental comparative study which assesses several NLP tools and combining approaches on different test collections and IR models. Thus, it may be helpful for future research works to choose the most suitable tools and develop more sophisticated methods for handling the complexity of Arabic language.Originality/valueThe originality of the idea is to consider that the richness of Arabic is an exploitable characteristic and no more a challenging limit. Thus, the authors combine 4 different morphological levels for the first time in Arabic IR. This approach widely overtook previous research results.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0515

Structural Information Retrieval in XML Documents: A Graph-based Approach

Although retrieval engines are becoming more and more functional and efficient, they still have the drawback of not being able to locate the relevant documentary granularity, which results in ignoring the structural aspect. In the context of XML document, Information Retrieval Systems allow to return the user’s documentary granules. Several studies have used graphs to represent XML documents. However, in the scope of this research, the semi-structured document’s structure and that of a user’s query can be seen as arborescences composed of a hierarchy of nested elements. By using graph theory, by calculating the structural proximity and especially the intersection between these two arborescences. The article presents a model for structural information retrieval based on graphs. A collection of multimedia documents are randomly extracted from INEX (Initiative for the Evaluation of XML Retrieval) 2010 to validate the approach. The first results shows the interest of such an approach.

Is anybody in there? Towards a model of affect and trust in human–AI information interactions

Advancements in search engines that utilize machine learning increase the likelihood that users will perceive these systems as worthy of trust.Methods. The nature and implications of trust in the context of algorithmic systems that utilize machine learning is examined and the resulting conception of trust is modelled. While current artificial intelligence does not meet the requirements of moral autonomy necessary to be considered trustworthy, people may still engage in misplaced trust based on the perception of moral autonomy. Users who place their trust in algorithmic systems limit their critical engagement with, and assessment of, the information interaction. A preliminary high-level model of trust’s role in information interactions adapting Ingwersen and Jarvelin’s Integrative Model for Interactive Information Seeking and Retrieval is proposed using the Google search engine as an example. We need to recognize that is it possible for users to react to information systems in a social manner that may lead to the formation of trust attitudes. As information professionals we want to develop interventions that will encourage users to stay critically engaged with their interactions with information systems, even when they perceive them to be autonomous.

Potential assessment of an increased exchange of core information for remanufacturing in automotive reverse supply chains

An increasing awareness of sustainability and scarcity of resources requires to enhance a product’s lifetime. Within the context of a circular economy, remanufacturing therefore can be applied. Used products (so-called ‘cores’) are returned to at least their original performance. An efficient planning and management of reverse supply chains (RSC) is a prerequisite for successful remanufacturing. Besides the required material flow from the customer via service centers, collection stations, and core brokers to the remanufacturing plants, the reverse flow of core information is essential. The multi-tier exchange of core information potentially facilitates reducing existent uncertainties in RSC, i.e. regarding timing, quantity, and quality of returned products. Yet, current approaches neither consider the multi-tier information flow nor the differentiation of core information for different downstream tasks in detail. Specifically, information flow in the automotive RSC remains to be researched in detail. Therefore, the potential of an increased exchange of core information for remanufacturing in RSC is explored in this paper. A literature review and expert interviews with the stakeholders in the automotive RSC are conducted to assess the status quo of information exchange. Based on this, relevant core-specific information, and their impact on increasing the efficiency of different downstream planning processes are identified. To quantify the potential of an increased information exchange, both a model for the categorical use of information and a receptor model are developed. It can be concluded that an increase in the exchange of information in remanufacturing goes along with a multitude of facilitations, e.g. regarding logistics optimization, material requirement planning and stochastic routing of cores. Moreover, due to the prioritization of core information, a sequence model for core information retrieval can be derived.

Neutrosophic Image Segmentation: An Approach for the Treatment of Uncertainty in Multimodal Information Systems

Information explosion in this era has led to the proliferation of digital data in form of images, text, video, and audio. Uncertainty is a major issue in information access and retrieval models, and incomplete information needs to be treated in information systems because imprecision indicates the existence of a value that cannot be measured. There is no denial of the fact that uncertainty puts a hindrance in obtaining information in real-time systems, and as per knowledge rarely does any study of information retrieval using image segmentation treat imprecise and inconsistent information inherited in information systems. This work proposes to transform images in the neutrosophic domain for the treatment of uncertainty that persists in information recovery. Later, the image is segmented using the neutrosophic segmentation algorithm and its results are compared with the Modified Fuzzy c-Means segmentation algorithm, which is the earlier used segmentation algorithm in information systems. The experiment is conducted on a variety of multimodal images from the Berkeley Segmentation Dataset and Benchmark, showing the effectiveness of the proposed method for information systems. The proposed image segmentation using neutrosophy seems to yield a smaller error of 0.011, but the error obtained using the fuzzy c-means (MFCM) method is 0.13, which is larger than the proposed approach. The work also demonstrates how well neutrosophic segmentation can segment images having different noise levels as well as clean images. The results show that the proposed algorithm yields the most accurate segmented image for feature extraction which can be utilized while designing effective information systems.

EFFICIENCY INCREASING OF INFORMATION SEARCH ON A GIVEN TOPIC ON THE INTERNET

The article discusses the ways to overcome the difficulties associated with surfing the Internet for certain information. Many users are not able to explicitly make up a search request according to their information needs, and their search is often limited to haphazard use of several most popular search engines. Most search engine research is technical. At the same time, in recent years more and more researchers have paid the attention to the study of information search issues in the global network. These studies are at the junction of computer science and psychology. It focuses on information behavior and user needs, cognitive factors and information practices of users. The article examines the previously applied conceptual models of information retrieval, formulates a conceptual model of information retrieval on a given topic. The main properties of user interaction with search engines and factors that should be taken into account are considered. The procedure for information search and analysis in universal and scientific search systems is described. The description of experimental testing of the proposed conceptual search model is provided. The article proposes a new conceptual model of search in universal and scientific search engines. This model contributes to the formation of a metacognitive search strategy. In addition, a generalized indicator of the efficiency of information retrieval using such a model is proposed. The proposed conceptual search model can be applied in the educational process. In particular, the article provides an example of students’ use of the elements of the formed metacognitive strategy when doing Bachelor degree paper research.

A performance tunable CPIR-based privacy protection method for location based service

Location-based service has become one of the essential parts of our daily lives, which raises a problem: how to preserve location privacy. Computational Private Information Retrieval (CPIR) protocol, which does not need a trusted third party, allows users to retrieve data from a service provider without revealing users’ query information. However, the CPIR protocol scans the entire data space, which causes high costs and limits the scale of CPIR-based applications. To address this problem, we propose a performance tunable Computational Private Information Retrieval (PTCPIR) model. We divide data space into subspaces and scan a subset of subspaces to achieve better performance while sacrificing some privacy. By tuning the percentage of scanned subspaces, we can achieve a tradeoff between performance and privacy. We design a hierarchical encrypted secure index to support data users to retrieve subspace information safely and efficiently, which facilitates privacy-preserving subspace information retrieval. Furthermore, we propose a novel subspace information retrieval method where the confused query keyword sequence makes servers unable to count query probability. Hence access pattern privacy is protected. Experiments on Sequoia and synthetic datasets demonstrate that our PTCPIR model provides a tunable tradeoff between performance and privacy with high efficiency and good scalability.

A User-Centric Multi-Context Hybrid Reasoning Information Retrieval Model

Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.

Review of Information Retrieval: Models, Performance Evaluation Techniques and Applications

Information Retrieval (IR) is a field that concerns the structure, memory, analysis, and access to pieces of information. It has a wide application in various areas like search engines, communication systems, information filtering, medical search, etc., and helps design efficient and secure applications. This area has been a surge of research from the last few years due to data mining's unparalleled success, deep learning in computer vision, blockchain technology, etc. Core models, performance evaluation techniques, IR system applications, and its role in blockchain technology have been proposed in this literature, calling the need for a broad survey to focus the research in this promising area. This paper fills the space by surveying the state of art approaches with deep learning models, query expansion techniques used, and the use of private information retrieval in blockchain technology. This survey paper includes different IR models like boolean model, vector space model, probabilistic model, language model, N-gram model, fuzzy model, Latent Semantic Indexing (LSI) Model, Bayesian network, Evolutionary algorithm-based models and Machine Learning based models. Applications of IR systems along with different datasets are also included to provide further research in this field.

Neural ranking models for document retrieval

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.