Information Retrieval Problem Research Articles

GENERALIZED PASCAL’S PYRAMIDS AND COMBINATORIAL INFORMATION RETRIEVAL PROBLEMS

The work studies combinatorial objects of pyramidal structure. It proposes fundamental relations for generalizations of Pascal’s triangle and pyramid. It considers problems of finding new ways of constructing information relevance indices and offers a way of constructing such an index with the help of Pascal’s generalized pyramids. Using structures of a type of Pascal’s generalized pyramids with weight factors allow construction of an index that reflects a degree of relevance of keywords, as well as makes it possible to allocate subsets of keywords with specific properties. The offered methods of the combinatorial analysis of hierarchical structures in information retrieval problems can be used in creating and analyzing knowledge databases.

Maximizing the spread of information through content optimization

As data-driven prediction models advance, an increasing number of people are enjoying news personalized to their interests. The primary problem such recommendation models solve is to precisely match information with users and, in so doing, ensure that news spreads with greater efficiency. However, these techniques only help the media platform; they do not help those who produce the news. Hence, we devised a propagation framework based on a human-in-the-loop simulation that helps content authors maximize the spread of their messages through social networks. The framework works by acting on feedback provided by the simulation model. Additionally, the spread of information is formulated as a multi-objective optimization problem in which propagation is data-driven and simulated with machine learning techniques that leverage data on the historical behaviors of users. We additionally describe an implementation for this framework as an example of how the framework might be used in real life. On the practical side, the implementation uses text data from a blog to simulate the message's propagation, while, from a technical point of view, the multi-objective optimization problem is divided into an information retrieval problem and an integer programming problem, the results of which are fed back into the content editor as content operation strategies. A case study with the Sina Weibo microblog site not only validates the framework but also provides practitioners with insights into how to maximize the spread of information through social networking platforms. The results show that the proposed propagation framework is capable of increasing retweets by 7.9575 %. As an interesting aside, our experiments also show that the Weibo retweet lottery is both popular and a highly effective mechanism for increasing reposts.

Cross media knowledge information retrieval model based on D-S evidence theory

Cross media knowledge information retrieval provides strong support for information processing and utilization in the information society, but there are problems such as heterogeneity in cross media knowledge information. Therefore, a cross media knowledge information retrieval model using D-S evidence theory is proposed, which involves using approximate calculation methods to improve this theory for information fusion, reducing computational complexity, and using deep networks for fine-grained information retrieval to improve retrieval accuracy. The results showed that the improved theory enhanced computational efficiency by about 27.23 %. The memory usage was <60 %, and the average accuracy of information fusion reached 93.14 %. It also exhibited high recall and low false alarm rates. The cross media knowledge information retrieval model proposed in the study achieved accuracy of 92.64 %, 96.49 %, and 97.46 % on the three datasets used in the experiment, respectively. The study provides an effective, computationally efficient, and highly accurate model for cross media knowledge information retrieval, which is expected to promote research and application in this field. The combination of improved D-S evidence theory and deep networks provides a powerful approach to solving the problem of cross media heterogeneous information retrieval, which has a positive promoting effect on the processing and utilization of information in the information society.

Training of information retrieval in the State university of Vilnius: some contential and technological aspects of education in the field of librarianship and bibliography

The article deals with the problem of professional education of librarians at the University of Vilnius. The changing role of information, which is becoming one of the natural resources of the country, and the rapid development of computer technology reveal new professional demands for librarians. As a result of this tendency, teaching online bibliographical searching has become of great importance in the library education curriculum. Specific conditions of development and practical needs of libraries and information services, main traits, and opportunities of higher education in Lithuania are revealed. This makes it possible to create an original conception of automated information retrieval training. The problems of information retrieval are studied in subjects such as Librarianship, Bibliography, Information Retrieval Systems, Information, and Technical Aids for Library Work, which are based on a specially created training automated information retrieval system (AIRS). Three different kinds of connections between the revealed subjects are presented in a scheme and commented upon. The choice of education technology is discussed. The main forms are various practical studies, realized on the basis of AIRS. These also help overcome the psychological barrier that arises when working with computers.

Comparing point‐wise and pair‐wise relevance judgment with brain signals

AbstractHow to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments.

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval

With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the pre-training stage. However, the rich structured information in Wikipedia, such as the titles, abstracts, hierarchical heading (multi-level title) structure, relationship between articles, references, hyperlink structures, and the writing organizations, has not been fully explored. In this paper, we devise four pre-training objectives tailored for IR tasks based on the structured knowledge of Wikipedia. Compared to existing pre-training methods, our approach can better capture the semantic knowledge in the training corpus by leveraging the human-edited structured data from Wikipedia. Experimental results on multiple IR benchmark datasets show the superior performance of our model in both zero-shot and fine-tuning settings compared to existing strong retrieval baselines. Besides, experimental results in biomedical and legal domains demonstrate that our approach achieves better performance in vertical domains compared to previous models, especially in scenarios where long text similarity matching is needed. The code is available at https://github.com/oneal2000/Wikiformer.

Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This article introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token–event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F 1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.

Deceptive Information Retrieval.

We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databases while deceiving the databases by making the databases' predictions on the user-required file index incorrect with high probability. Conceptually, DIR is an extension of private information retrieval (PIR). In PIR, a user downloads a required file without revealing its index to any of the databases. The metric of deception is defined as the probability of error of databases' prediction on the user-required file, minus the corresponding probability of error in PIR. The problem is defined on time-sensitive data that keep updating from time to time. In the proposed scheme, the user deceives the databases by sending real queries to download the required file at the time of the requirement and dummy queries at multiple distinct future time instances to manipulate the probabilities of sending each query for each file requirement, using which the databases' make the predictions on the user-required file index. The proposed DIR scheme is based on a capacity achieving probabilistic PIR scheme, and achieves rates lower than the PIR capacity due to the additional downloads made to deceive the databases. When the required level of deception is zero, the proposed scheme achieves the PIR capacity.

Information retrieval from textual data: Harnessing large language models, retrieval augmented generation and prompt engineering

This paper describes how recent advancements in the field of Generative AI (GenAI), and more specifically large language models (LLMs), are incorporated into a practical application that solves the widespread and relevant business problem of information retrieval from textual data in PDF format: searching through legal texts, financial reports, research articles and so on. Marketing research, for example, often requires reading through hundreds of pages of financial reports to extract key information for research on competitors, partners, markets and prospective clients. It is a manual, error-prone and time-consuming task for marketers, where until recently there was little scope for automation, optimisation and scaling. The application we have developed combines LLMs with a retrieval augmented generation (RAG) architecture and prompt engineering to make this process more efficient. We have developed a chatbot that allows the user to upload multiple PDF documents and obtain a summary of predefined key areas as well as to ask specific questions and get answers from the combined documents’ content. The application’s architecture begins with the creation of an index for each of the PDF files. This index includes embedding the textual content and constructing a vector store. A query engine, employing a small-to-big retrieval method, is then used to accurately respond to a set of predefined questions for each PDF to create the summary. The prompt has been designed in a manner that minimises the risk of hallucination which is common in this type of model. The user interacts with the model via a chatbot feature. It utilises similar small-to-big retrieval techniques over the indices for straightforward queries, and a more complex sub-questions engine for in-depth analysis, providing a comprehensive and interactive tool for document analysis. We have estimated that the implementation of this tool would reduce the time spent on manual research tasks by around 60 per cent, based on the discussions we have had with potential users.

Personalized and Diversified: Ranking Search Results in an Integrated Way

Ambiguity in queries is a common problem in information retrieval. There are currently two solutions: search result personalization and diversification. The former aims to tailor results for different users based on their preferences, but the limitations are redundant results and incomplete capture of user intents. The goal of the latter is to return results that cover as many aspects related to the query as possible. It improves diversity yet loses personality and cannot return the exact results the user wants. Intuitively, such two solutions can complement each other and bring more satisfactory reranking results. In this article, we propose a novel framework, namely, PnD , to integrate personalization and diversification reasonably. We employ the degree of refinding to determine the weight of personalization dynamically. Moreover, to improve the diversity and relevance of reranked results simultaneously, we design a reset RNN structure (RRNN) with the “reset gate” to measure the influence of the newly selected document on novelty. Besides, we devise a “subtopic learning layer” to learn the virtual subtopics, which can yield fine-grained representations of queries, documents, and user profiles. Experimental results illustrate that our model can significantly outperform existing search result personalization and diversification methods.

Decoy-state quantum private query protocol with two-way communication

Quantum private query (QPQ) is a communication protocol established under the condition of mutual distrust between communication parties, which is used to solve the symmetrically private information retrieval problem in the quantum field. However, most of the existing QPQ protocols are based on the ideal light source. In practice, the multi-photon pulse generated by the transmitter will bring great security problems. We analyze the actual security of QPQ protocol in two-way communication, and find that database security and user privacy will be seriously threatened under multi-photon pulse. So we adopt the decoy state method to solve the actual security problem of QPQ protocol in view of the user side as the light source. The results show that the decoy state method is suitable for QPQ protocol of two-way communication, and can effectively defend against multi-photon pulse attacks.

Author and organization profiles in Dimensions and Lens information systems: The study of functionality

The paper demonstrates the features of author and organizational publication profiles in Dimensions and Lens information systems. In recent years, these systems have been successfully competing with many subscription-based systems. The authors discuss the advantages of these databases in terms of coverage of research content in publication profiles as compared to that of commercial bibliographic platforms, as well as the wide range of functional capabilities. As illustrated by publication profiles of two research organizations and their authors, we describe original approaches to searching bibliographic information, as well as editing bibliographic errors in databases. These features enable (a) to monitor effectively the scholarly output of research organizations and authors, and (b) to enhance visibility of research findings in the international information space. Solving information-retrieval problems and editing publication profiles of Russian organizations and authors in Dimensions and Lens prove to be similar to that of commercial systems, i. e. Web of Science and Scopus. The vast experience of working with proprietary databases and subscription-based systems, would enable Russian academic libraries to adapt to new interfaces in a short time frame.

Syntactic-Informed Graph Networks for Sentence Matching

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.

Dynamic Hybrid Fragmentation Method for Multimedia Databases

Multimedia databases store high-volume data, which causes problems in efficient information retrieval, and increases execution costs and response times of the queries. To solve this problem, data fragmentation techniques exist to improve query performance, increase information availability, and efficiently execute more operations accessing less irrelevant data. This article presents a comprehensive review of 34 methods related to hybrid fragmentation and subsequently proposes the design of a hybrid fragmentation method that adapts the scheme according to workload changes to maintain efficient retrieval of multimedia data. The proposed technologies are Java as a programming language, Java Server Faces (JSF) as a framework, MySQL and MongoDB database management systems, and NetBeans as an Integrated Development Environment (IDE), following the UWE methodology (Unified Modeling Language-based Web Engineering).

Multi-User PIR with Cyclic Wraparound Multi-Access Caches.

We consider the problem of multi-access cache-aided multi-user Private Information Retrieval (MACAMuPIR) with cyclic wraparound cache access. In MACAMuPIR, several files are replicated across multiple servers. There are multiple users and multiple cache nodes. When the network is not congested, servers fill these cache nodes with the content of the files. During peak network traffic, each user accesses several cache nodes. Every user wants to retrieve one file from the servers but does not want the servers to know their demands. This paper proposes a private retrieval scheme for MACAMuPIR and characterizes the transmission cost for multi-access systems with cyclic wraparound cache access. We formalize privacy and correctness constraints and analyze transmission costs. The scheme outperforms the previously known dedicated cache setup, offering efficient and private retrieval. Results demonstrate the effectiveness of the multi-access approach. Our research contributes an efficient, privacy-preserving solution for multi-user PIR, advancing secure data retrieval from distributed servers.

Late multimodal fusion for image and audio music transcription

Music transcription, which deals with the conversion of music sources into a structured digital format, is a key problem for Music Information Retrieval (MIR). When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Recognition (OMR), or audio recordings, which is the case of Automatic Music Transcription (AMT). The different nature of the aforementioned input data has conditioned these fields to develop modality-specific frameworks. However, their recent definition in terms of sequence labeling tasks leads to a common output representation, which enables research on a combined paradigm. In this respect, multimodal image and audio music transcription comprises the challenge of effectively combining the information conveyed by image and audio modalities. In this work, we explore this question at a late-fusion level: we study four combination approaches in order to merge, for the first time, the hypotheses regarding end-to-end OMR and AMT systems in a lattice-based search space. The results obtained for a series of performance scenarios–in which the corresponding single-modality models yield different error rates–showed interesting benefits of these approaches. In addition, two of the four strategies considered significantly improve the corresponding unimodal standard recognition frameworks.

Bounds on the Capacity of Private Information Retrieval Over Graphs

In the private information retrieval (PIR) problem, a user wants to retrieve a file from a database without revealing any information about the desired file’s identity to the servers that store the database. In this paper, we study the PIR capacity of a graph-based replication system, in which each file is stored on two distinct servers according to an underlying graph. This paper aims to provide upper and lower bounds to the PIR capacity of graphs via various graph properties. In particular, we provide several upper bounds on the PIR capacity that apply to all graphs. We further improve the bounds for specific graph families (which turn out to be tight in certain cases) by utilizing the underlying graph structure. For the lower bounds, we establish optimal rate PIR schemes for star graphs via edge-coloring techniques. Lastly, we provide an improved PIR scheme for complete graphs, implying an improved general lower bound on all graphs’ PIR capacity.

Bug Triage Automation Approaches

Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to a bug report. A personnel (triager) has to analyze the developers' profiles and bug reports for the purpose of making a suitable assignment. Manual bug triage consumes time and effort, so automating this process is a necessity. The previous research studies addressed the triage problem as an information retrieval problem, where the new bug report is the query. Other researchers tackled this problem as a classification problem and utilized traditional machine learning or deep learning techniques. A handful of research studies handled this problem as an optimization problem and utilized optimization algorithms such as Hungarian. This paper briefs and analyzes the previous bug triage approaches in addition to conducting an empirical comparison among five of the previous approaches.

Construction of Digital Management Model of Personnel Archives Information Based on the Concept of Industry 4.0.

In order to improve the effect of personnel archives information management under the background of Industry 4.0, this paper conducts in-depth research on retrieval technology. Aiming at the shortcomings and deficiencies of the existing sorting methods, this paper introduces the basic principle of information retrieval, the retrieval process, and retrieval model and proposes a relevance ranking algorithm based on the user vector calculation method and the record vector calculation method. The algorithm solves the query structure sorting problem of information retrieval, optimizes the retrieval process, and improves the efficiency of information acquisition. Under the guidance of the concept of Industry 4.0, this paper constructs the digital management model of personnel archives information. The experimental results show that the digital management model of personnel archives proposed in this paper has certain effects.

Symmetric Private Information Retrieval at the Private Information Retrieval Rate

We consider the problem of symmetric private information retrieval (SPIR) with user-side common randomness. In SPIR, a user retrieves a message out of K messages from N non-colluding and replicated databases in such a way that no single database knows the retrieved message index (user privacy), and the user gets to know nothing further than the retrieved message (database privacy), i.e., the privacy constraint between the user and the databases is symmetric. SPIR has the following three properties: its capacity is smaller than the capacity of PIR which requires only user privacy; it is infeasible in the case of a single database; and it requires presence of shared common randomness among the databases. We introduce a new variant of SPIR where the user is provided with a random subset of the shared database common randomness, which is unknown to the databases. We determine the exact capacity region of the triple (d,ρS,ρU), where d is the download cost, ρS is the amount of shared database (server) common randomness, and ρU is the amount of available user-side common randomness. We show that with a suitable amount of ρU, this new SPIR achieves the capacity of the conventional PIR. As a corollary, single-database SPIR becomes feasible. Further, the presence of user-side ρU reduces the amount of required server-side ρS.