Information Retrieval Applications Research Articles

The digital lab manager: Automating research support

Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.

Text Summarization Based on Semantic Similarity

In the contemporary information age, the sheer volume of textual data poses a significant challenge for efficient comprehension and utilization. This project endeavors to address this challenge by developing a Text Summarization System grounded in semantic similarities. The primary goal is to create a robust and intuitive tool that extracts key information from large textual datasets, offering users a concise and meaningful summary. The proposed system employs advanced Natural Language Processing (NLP) techniques to analyze the semantic relationships within the text. Rather than relying solely on syntactic structures, the model identifies and leverages semantic similarities, such as shared concepts, themes, and contextual relationships, to distill the essential content. This approach enhances the summarization process by ensuring that the generated summaries reflect a deeper understanding of the underlying semantics, thereby capturing the core meaning of the text. Throughout the development of this project, the B.Tech student will delve into the intricacies of semantic analysis, exploring techniques to recognize and prioritize key concepts. The system's effectiveness will be evaluated through rigorous testing on diverse textual datasets, assessing its ability to generate coherent and relevant summaries across various domains. This project not only contributes to the field of NLP but also has practical applications in information retrieval, document summarization, and content curation. By providing an innovative solution to the challenges of information overload, the Text Summarization System based on semantic similarities offers a valuable tool for enhancing efficiency in information processing and decision-making. Index terms Text Summarization, Semantic Similarities, Natural Language Processing (NLP), Semantic Analysis, Information Retrieval, Document Summarization, Content Curation, Information Overload, Decision Making, Textual Data Analysis, Key Concept Recognition, Conceptual Relationships, Syntactic Structures, Semantic Understanding, Textual Datasets Evalution.

Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data

In neural memory decoding, a concept being mentally recalled is identified using brain data. Recently, the feasibility of neural memory decoding with EEG data has been demonstrated. Here we propose a new application – neural information retrieval – that uses neural memory decoding to allow a document to be retrieved merely by thinking about it. In this paper we describe neural memory decoding, define the application of neural information retrieval, present experimental results related to the practicality of the application, and discuss issues of deployment and data privacy.

FFEC: Fast and forward-secure equivalence-based ciphertext comparability for multiple users in cloud environment

With the expansion of cloud computing, an increasing amount of sensitive data is being encrypted and stored in public clouds to alleviate storage and management burdens. Secure equivalence-based retrieval of ciphertexts for multiple users is crucial in a cloud environment where diverse user data resides for processing purposes. Public key encryption with equality test (PKEET) has been introduced as a cryptographic tool to verify if two ciphertexts under different public keys contain the same message. However, existing PKEET schemes often face misuse of trapdoors due to their unlimited lifespan, potentially leading to unauthorized disclosure of user privacy. In this paper, we propose a novel approach called fast and forward-secure equivalence-based comparability (FFEC) for multiple users by employing a forward-secure PKEET (FS-PKEET). This restricts the retrieval process only to ciphertexts generated prior to the most recent trapdoor update. We present a concrete FS-PKEET scheme based on bilinear pairing and demonstrate its security using Bilinear Diffie–Hellman (BDH) assumption in the random oracle model. Comprehensive performance evaluation shows that our work has much efficiency of decryption, trapdoor generation and test execution thanks to greatly reducing the cost of trapdoor generation and thus is practical for the application of secure ciphertext information retrieval in cloud environment.

Blockchain Technology Applications in Search and Information Retrieval in the Digital Environment for Libraries: An Analytical Study

Blockchain Technology Applications in Search and Information Retrieval in the Digital Environment for Libraries: An Analytical Study

An Optimal Algorithm for Finding Champions in Tournament Graphs

A tournament graph is a complete directed graph, which can be used to model a round-robin tournament between $n$ players. In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. In detail, we aim to investigate algorithms that find the champion by playing a low number of matches. Solving this problem allows us to speed up several Information Retrieval and Recommender System applications, including question answering, conversational search, etc. Indeed, these applications often search for the champion inducing a round-robin tournament among the players by employing a machine learning model to estimate who wins each pairwise comparison. Our contribution, thus, allows finding the champion by performing a low number of model inferences. We prove that any deterministic or randomized algorithm finding a champion with constant success probability requires $\Omega(\ell n)$ comparisons, where $\ell$ is the number of matches lost by the champion. We then present an asymptotically-optimal deterministic algorithm matching this lower bound without knowing $\ell$, and we extend our analysis to three variants of the problem. Lastly, we conduct a comprehensive experimental assessment of the proposed algorithms on a question answering task on public data. Results show that our proposed algorithms speed up the retrieval of the champion up to $13\times$ with respect to the state-of-the-art algorithm that perform the full tournament.

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Discovery and recognition of formula concepts using machine learning

Citation-based Information Retrieval (IR) methods for scientific documents have proven effective for IR applications, such as Plagiarism Detection or Literature Recommender Systems in academic disciplines that use many references. In science, technology, engineering, and mathematics, researchers often employ mathematical concepts through formula notation to refer to prior knowledge. Our long-term goal is to generalize citation-based IR methods and apply this generalized method to both classical references and mathematical concepts. In this paper, we suggest how mathematical formulas could be cited and define a Formula Concept Retrieval task with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While FCD aims at the definition and exploration of a ‘Formula Concept’ that names bundled equivalent representations of a formula, FCR is designed to match a given formula to a prior assigned unique mathematical concept identifier. We present machine learning-based approaches to address the FCD and FCR tasks. We then evaluate these approaches on a standardized test collection (NTCIR arXiv dataset). Our FCD approach yields a precision of 68% for retrieving equivalent representations of frequent formulas and a recall of 72% for extracting the formula name from the surrounding text. FCD and FCR enable the citation of formulas within mathematical documents and facilitate semantic search and question answering, as well as document similarity assessments for plagiarism detection or recommender systems.

Librarians’ ICT skills and service delivery in private universities in Nigeria

This paper examined the Information and Communication Technology (ICT) skills of librarians for service delivery in private universities with particular reference to Southern Nigeria. The study adopted the survey research method. Forty-five (45) private university libraries in Southern Nigeria and their librarians constituted the sample size for the study. The study’s samples consisted of 181 librarians in the private universities, hence, total enumeration was adopted. Questionnaire was used for data collection from the respondents. Out of the 181 copies of the questionnaire distributed, 173 were returned and found valid for analysis. Data gathered was analysed using the Statistical Package for Social Sciences (SPSS) Version 21 and the results were presented in frequency count, percentage, mean and chart. Findings of the study revealed that librarians in private universities in Southern Nigeria delivered various library services to their users. The librarians have acquired basic ICT skills and were versatile in its application for Internet searches, information retrieval, social communication and Microsoft-office related activities with the exception of programming skills. Majority of the librarians acquired their ICT skills through self-learning. The finding revealed that the acquisition of ICT skills by librarians in Southern Nigeria private universities is relevant to their delivery of improved library services. The paper recommended that regular acquisition of relevant ICT skills is essential for librarians and university management should support librarians’ ICT capacity development at national and international levels as this would enhance their relevance and get them abreast of the latest development for effective service delivery.

Extraction and normalization of IR indexing terms and phrases in a highly inflectional language

Abstract Term-based indexing of documents is conventionally implemented by stemmers or their corpus-based improvements, both of which encode implicit linguistic information. Terms are directly derived from document content such that a unique indexing approach is available at indexing run-time. For highly inflectional languages where term variation is high, such techniques are more error-prone. The main focus of the current study is the extraction and normalization of single terms and phrases and the proposal of authenticated control of indexing. The proposed approach relies on the use of explicit linguistic knowledge, appropriately encoded in large language resources. Such control guarantees the highest possible expansion factor for indexing terms as well as indexing consistency. Moreover, it offers a framework where different and eventually contradicting indexing criteria can be practiced, conventional and Natural Language Processing (NLP)-based Information Retrieval (IR) applications can be served, while adaptations can be made for tuning to a specific domain or corpus.

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges.

An Efficient and Robust Semantic Hashing Framework for Similar Text Search

Similar text search aims to find texts relevant to a given query from a database, which is fundamental in many information retrieval applications, such as question search and exercise search. Since millions of texts always exist behind practical search engine systems, a well-developed text search system usually consists of recall and ranking stages. Specifically, the recall stage serves as the basis in the system, where the main purpose is to find a small set of relevant candidates accurately and efficiently. Towards this goal, deep semantic hashing, which projects original texts into compact hash codes, can support good search performance. However, learning desired textual hash codes is extremely difficult due to the following problems. First, compact hash codes (with short length) can improve retrieval efficiency, but the demand for learning compact hash codes cannot guarantee accuracy due to severe information loss. Second, existing methods always learn the unevenly distributed codes in the space from a local perspective, leading to unsatisfactory code-balance results. Third, a large fraction of textual data contains various types of noise in real-world applications, which causes the deviation of semantics in hash codes. To this end, in this paper, we first propose a general unsupervised encoder-decoder semantic hashing framework, namely MASH (short for Memory-bAsed Semantic Hashing), to learn the balanced and compact hash codes for similar text search. Specifically, with a target of retaining semantic information as much as possible, the encoder introduces a novel relevance constraint among informative high-dimensional representations to guide the compact hash code learning. Then, we design an external memory where the hashing learning can be optimized in the global space to ensure the code balance of the learning results, which can promote search efficiency. Besides, to alleviate the performance degradation problem of the model caused by text noise, we propose an improved SMASH (short for denoiSing Memory-bAsed Semantic Hashing) model by incorporating a noise-aware encoder-decoder framework. This framework considers the noise degree for each text from the semantic deviation aspect, ensuring the robustness of hash codes. Finally, we conduct extensive experiments in three real-world datasets. The experimental results clearly demonstrate the effectiveness and efficiency of MASH and SMASH in generating balanced and compact hash codes, as well as the superior denoising ability of SMASH.

Robustness of rank aggregation methods for malicious disturbance

Rank aggregation has widespread applications in social choice, information retrieval, and bioinformatics. Given the enormous social and economic implications of these applications and the consequent incentives for malicious users to disturb the aggregated ranking, evaluating the robustness of rank aggregation methods for malicious disturbance is a key challenge. In this article, we introduce an analytical framework for the robustness of rank aggregation for malicious disturbances, in which we generate rankings with imperfect information and observe the response of rank aggregation methods. Along with the analytical framework, we propose an experimental data generation method and a new robustness measure. Then we investigate the robustness of five common rank aggregation methods using our proposed method. Extensive experiments demonstrate that our method can effectively compare the robustness of different rank aggregation methods for malicious disturbance. Moreover, the experimental results show that the performance of rank aggregation methods for malicious disturbance may be very different from that for random error.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning. Mining core features and performing the text classification still exist as a challenging task. Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach. This paper presented the text document classification that has wide applications in information retrieval, which uses movie review datasets. Here the document indexing based on controlled vocabulary, adjective, word sense disambiguation, generating hierarchical categorization of web pages, spam detection, topic labeling, web search, document summarization, etc. Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search optimization. Positive review and negative review of movie dataset is presented to get the better classification accuracy. Experimental results focused with context mining, feature analysis and classification. By comparing with the previous work, proposed work designed to achieve the efficient results. Overall design is performed with MATLAB 2020a tool.

CRMF: A fault localization approach based on class reduction and method call frequency

AbstractIdentifying the location of faults in real‐world programs is one of the costly processes during software debugging. To reduce the debugging effort, various fault localization techniques have been proposed in recent years. Spectrum‐based fault localization (SBFL) is one kind of widely investigated fault localization technique. Most SBFL techniques first calculate the suspiciousness of program elements (such as statements, methods) to be faulty using the coverage information and execution results of tests. Then a rank list of program elements is generated according to their suspiciousness. However, some SBFL techniques only consider the binary coverage information (i.e., whether the program element is covered) but ignore some of the tests' running behaviors, such as the execution frequency when faults occur in the iteration entities or loop bodies, which are more likely to be faulty followed the propagation‐infection‐execution model. The execution frequency based techniques only replace the feature items of the existing formula limiting their effectiveness in fault localization. In this article, we propose a fault localization technique, class reduction and method call frequency (CRMF), which utilizes mutation analysis and information retrieval techniques. In particular, CRMF first uses mutation analysis to identify and reduce the classes, in which the program elements with a low probability of being faulty. Then we propose a new suspiciousness formula that applies information retrieval and considers method call frequency. To evaluate the effectiveness of CRMF, we conduct empirical studies on 264 real‐world programs from the Defects4J benchmark. Final results show that CRMF outperforms the statement frequency based technique FLSF and SBFL techniques (i.e., Ochiai, OP2, Tarantula, and Dstar) in both single‐fault programs and multiple‐fault programs. Specifically, CRMF can rank 29, 74, and 112 faults at the top 1, 3, 5 ranks and achieve a higher mean reciprocal rank for single‐fault programs and multiple‐fault programs. Finally, we discuss the essence of CRMF and analyze its effectiveness on multi‐fault programs in detail.

Link Prediction in Co-authorship Networks

Besides social network analysis, the Link-Prediction (LP) problem has useful applications in information retrieval, bioinformatics, telecommunications, microbiology, and e-commerce as a forecast of future links in a given context to find what possible connections are based on a local and global statistical analysis of the given graph data. However, in Academic Social Networks (ASNs), the LP issue has recently attracted a lot of attention in academia and called for a variety of link prediction techniques to predict co-authorship among researchers and to examine the rich structural and associated data. As a result, this study investigates the problem of LP in ASNs to forecast the upcoming co-authorships among researchers. In a systematic approach, this review presents, analyses, and compares the primary taxonomies of topological-based, content-based, and hybrid-based approaches, which are used for computing similar scores for each pair of unconnected nodes. Then, this study ends with findings on challenges and open problems for the community to work on for further development of the LP problem of scholarly social networks.

Deep Bayesian Active Learning for Learning to Rank: A Case Study in Answer Selection

Given a question and a set of candidate answers, answer selection is the task of identifying the best answer, which can be viewed as a kind of learning-to-rank tasks. Learning to rank arises in many information retrieval applications, where deep learning models can achieve inspiring results. Training a deep learning model often requires large scale annotated data that are expensive and time-consuming to obtain. Active learning presents a promising approach to this problem by selecting more informative training data to reduce the amount of labelling efforts required. Because traditional active learning methods cannot be directly used for deep learning, researchers have proposed multiple deep active learning methods. However, none of the previous research efforts on deep active learning algorithms presents a specific framework for learning-to-rank tasks. In this work, we introduce a novel deep active learning framework based on <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> eep <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xpected <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> oss <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ptimization (DELO) for the answer selection task. It adopts a data acquisition function based on model uncertainty with Bayesian deep learning and the expected loss optimization. Moreover, a two-step batch-mode procedure, combining DELO and other data acquisition strategies is proposed to further improve the performance of active learning. Experimental results verify the effectiveness of the proposed framework.

Structural Segmentation and Labelling of Tabla Solo Performances

Tabla is a North Indian percussion instrument that serves as both an accompaniment and a captivating solo instrument. Tabla solo is intricate and elaborate, exhibiting rhythmic evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. Each section has a specific structure and name associated with it. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called gharānās. Several compositions by various composers from different gharānās are played in each section. This paper addresses the task of segmenting the tabla solo concert into musically meaningful sections. We then assign suitable section labels and recognise gharānās from the sections. We have curated a diverse collection of more than 38 hours of solo tabla recordings for the task. We motivate the problem and present different challenges and facets of the tasks. Inspired by the distinct musical properties of tabla solo, we compute several rhythmic and timbral features for the segmentation task. This work explores the approach of automatically locating the significant changes in the rhythmic structure by analysing local self-similarity in an unsupervised manner. We also explore the supervised random forest and a convolutional neural network trained on hand-crafted features. Both supervised and unsupervised approaches are also tested on a set of held-out recordings. Segmentation of an audio piece into its structural components and labelling is crucial to many music information retrieval applications like repetitive structure finding, audio summarisation, and fast music navigation. This work enables a comprehensive musical understanding and description of tabla solo concerts.

AN IMPROVED TERM WEIGHTING AND DOCUMENT RANKING METHOD USING RANDOM WALK MODEL FOR INFORMATION RETRIEVAL

Document representation is one of the most fundamental issues in information retrieval application. The graph-based ranking algorithms represent document as a graph. Once a document is represented as graph, the similarity of that document to a query can be calculated in various ways and the calculation provides ranking to documents. This paper introduces an improved random-walk method to rank a document by considering position of a term within a document and information gain of that term within the whole document set. The experiments on various collection sets show that our approach improves the recall and precision than other proposed methods.

N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines

&#x0D; &#x0D; &#x0D; Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis. &#x0D; &#x0D; &#x0D;