Conceptual Information Retrieval Research Articles

A Knowledge-Based Deep Learning Approach for Automatic Fake News Detection using BERT on Twitter

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms.

Information Retrieval Performance in Text Generation using Knowledge from Generative Pre-trained Transformer (GPT-3)

The rise of advanced language models like GPT-3 and text generation has witnessed remarkable progress. However, leveraging the vast amount of knowledge within these models to enhance information retrieval performance remains an area that needs to be explored. This research used Artificial Intelligence, specifically the OpenAI GPT-3 language model, to create an application to help make written content. This research investigates the impact of incorporating GPT-3's knowledge into text generation processes and evaluates its influence on information retrieval tasks. Several features in text generation generate text that requires exact information, such as specifications for a product and accurate descriptions of a job or product, which are included in the concept of information retrieval in text creation by language models. The research used the few-shot learning method in the GPT-3 language model. The generated responses are then evaluated using established information retrieval metrics such as precision, recall, and F1-score. The findings of this research reveal the effectiveness of utilizing GPT-3's knowledge in enhancing information retrieval performance. The generated responses demonstrate improved relevance to user queries, resulting in the same performance precision and recall scores compared to other paid text generator websites. Application results are testing in capabilities of retrieving some information. Application capabilities tested on other commercial text generator engines. The test results obtained BERTscore 86\% (precision), 88\% (recall), and 87\% (F1-Score).

Improving semantic information retrieval by combining possibilistic networks, vector space model and pseudo-relevance feedback

To improve the performance of information retrieval systems (IRSs), we propose in this article a novel approach that enriches the user’s queries with new concepts. Indeed, query expansion is one of the best methods that plays an important role in improving searches for a better semantic information retrieval. The proposed approach in this study combines possibilistic networks (PNs), the vector space model (VSM) and pseudo-relevance feedback (PRF) to evaluate and add relevant concepts to the initial index of the user’s query. First, query expansion is performed using PN, VSM and domain knowledge. PRF is then exploited to enrich, in a second round, the user’s query by applying the same approach used in the first expansion step. To evaluate the performance of the developed system, denoted conceptual information retrieval model (CIRM), several experiments of query expansion are performed. The experiments carried out on the OHSUMED and Clinical Trials corpora showed that using the two measures of possibility and necessity combined the cosinus similarity and PRF improves the query expansion process. Indeed, the improvement rate of our approach compared with the baseline is +28, 49% in terms of P@5.

A Comparative Optimization Model of Japanese Literature Characteristics for Cognitive Retrieval of Cross-Language Information.

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested.

Two dichotomies of recognition memory.

Murdock (1974, Human Memory: Theory and Data, Lawrence Erlbaum) distinguished between the encoding and retrieval of item information (the representation of individual events) and associative information (the representation of relations between separate events). Mandler (1980, Psychological Review, 87, 252-271) proposed that recognition decisions could be based on the sense of familiarity engendered by the stimulus or on the retrieval of conceptual, semantic, and contextual information about the target. These two distinctions have motivated a considerable amount of research over the past 40 years and have provided much of the bases for our current understanding of recognition memory. Selective aspects of this research are reviewed to show how theories of recognition memory have developed to embody these two dichotomies. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.

On the effect of relevance scales in crowdsourcing relevance assessments for Information Retrieval evaluation

Relevance is a key concept in information retrieval and widely used for the evaluation of search systems using test collections. We present a comprehensive study of the effect of the choice of relevance scales on the evaluation of information retrieval systems. Our work analyzes and compares four crowdsourced scales (2-levels, 4-levels, and 100-levels ordinal scales, and a magnitude estimation scale) and two expert-labeled datasets (on 2- and 4-levels ordinal scales). We compare the scales considering internal and external agreement, the effect on IR evaluation both in terms of system effectiveness and topic ease, and we discuss the effect of such scales and datasets on the perception of relevance levels by assessors.Our analyses show that: crowdsourced judgment distributions are consistent across scales, both overall and at the per-topic level; on all scales crowdsourced judgments agree with the expert judgments, and overall the crowd assessors are able to express reliable relevance judgments; all scales lead to a similar level of external agreement with the ground truth, while the internal agreement among crowd workers is higher for fine-grained scales; more fine-grained scales consistently lead to higher correlation values for both system ranking and topic ease; finally, we found that the considered scales lead to different perceived distances between relevance levels.

Document similarity for error prediction

ABSTRACT In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.

Exploiting Papers’ Reference’s Section for Multi-Label Computer Science Research Papers’ Classification

The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.

An Art of Review on Conceptual based Information Retrieval

An Art of Review on Conceptual based Information Retrieval

An Art of Review on Conceptual based Information Retrieval

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

LSA Based Text Summarization

In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the diversity between chosen sentences and produce the final summary as a good summary should have maximum level of diversity. The proposed technique is evaluated on OpinosisDataset1.0.

Current trends in information retrieval systems: review of fuzzy set theory and fuzzy Boolean retrieval models

This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, can be used to expand the set of the documents retrieved in response to a query.The paper concludesby recommending that in an electronic library environment, the librarians and information scientists should acquaint themselves with these terms in order to be more equipped in helping library users retrieve online documents relevant to their information needs.

The interplay between control processes and feature relevance: Evidence from dual-task methodology.

Neuropsychological studies suggest a distinction between (a) semantic knowledge and (b) control processes that shape the retrieval of conceptual information to suit the task or context. These aspects of semantic cognition are specifically impaired in patients with semantic dementia and semantic aphasia, respectively. However, interactions between the structure of knowledge and control processes that are expected during semantic retrieval have not been fully characterised. In particular, domain-general executive resources may not have equal relevance for the capacity to promote weak yet task relevant features (i.e., "controlled retrieval) and to ignore or suppress distracting information (i.e., "selection"). Here, using a feature selection task, we tested the contribution of featural relevance to semantic performance in healthy participants under conditions of divided attention. Healthy participants showed greater dual-task disruption as the relevance value of the distractor feature linearly increased, supporting the emerging view that semantic relevance is one of the organising principles of the structure of semantic representation. Moreover, word frequency, and inter-correlational strength affected overall performance, but they did not show an interaction with dual-task conditions. These results suggest that domain-general control processes, disrupted by divided attention, are more important to the capacity to efficiently avoid distracting information during semantic decision-making than to the promotion of weak target features. The present study therefore provides novel information about the nature of the interaction between structured conceptual knowledge and control processes that support the retrieval of appropriate information and relates these results to a new theoretical framework, termed controlled semantic cognition.

Methods and Trends in Information Retrieval in Big Data Genomic Research

This paper described information retrieval (IR) and the common methods of finding, extracting, and mining information in genomic research through text mining, and natural language processing (NLP). There was a surge of genomic information from the different literature and the production of genome datasets that catapulted the development of several tools for analyzing and presenting new found knowledge in the biomedical and genome research. This paper presented the recent research trends, survey, reviews, experiments, and concepts in information retrieval applied to text, images and object features in big data genomic research. The method used is exploratory survey research in IR uses in genomic research that presents the concepts, methods, evaluation results and next steps described by the key researchers.

CLUSTERING MULTI ATTRIBUTE SIMILARITY INDEX FOR CATEGORICAL DATA STREAMS

Data mining is an aggressively concept in information retrieval based on different attributes from different data sources. For effective data collection from data sources with respect to relevant data, one-class learning is required to perform labeled based classification with individual training sequences on attributes. In clustering, uncertain data with different data set visualization. Uncertain One Class Clustering (UOCC) with support vector machine to explore data summarization in terms of user preference. UOCC process single attributes from reliable data streams for inconsistent data. So that in this paper we propose Clustering with Multi-Attribute Framework (CMAF) to group multiple attributes to explore uncertain data from reliable data. CMAF construct matrix with different reliable attributes based on relevant features. Proposed approach defines effective data summarization for relevant data with attribute partitioning and constructs user profile based on relative attributes. Experimental results come out for proposed approach gives better and expressive results with comparison of state of art methods.

Neural correlates of recognition and naming of musical instruments.

Retrieval of lexical (names) and conceptual (semantic) information is frequently impaired in individuals with neurological damage. One category of items that is often affected is musical instruments. However, distinct neuroanatomical correlates underlying lexical and conceptual knowledge for musical instruments have not been identified. We used a neuropsychological approach to explore the neural correlates of knowledge retrieval for musical instruments. A large sample of individuals with focal brain damage (N = 298), viewed pictures of 16 musical instruments and were asked to name and identify each instrument. Neuroanatomical data were analyzed with a proportional MAP-3 method to create voxelwise lesion proportion difference maps. Impaired naming (lexical retrieval) of musical instruments was associated with damage to the left temporal pole and inferior pre- and postcentral gyri. Impaired recognition (conceptual knowledge retrieval) of musical instruments was associated with a more broadly and bilaterally distributed network of regions, including ventromedial prefrontal cortices, occipital cortices, and superior temporal gyrus. The findings extend our understanding of how musical instruments are processed at neural system level, and elucidate factors that may explain why brain damage may or may not produce anomia or agnosia for musical instruments. Our findings also help inform broader understanding of category-related knowledge mapping in the brain, as musical instruments possess several characteristics that are similar to various other categories of items: They are inanimate and highly manipulable (similar to tools), produce characteristic sounds (similar to animals), and require fine-grained visual differentiation between each other (similar to people). (PsycINFO Database Record

Stock Market Prediction Based on Term Frequency-Inverse Document Frequency

This paper presents a new method to predicting the change of stock prices by utilizing text mining news of the stock market. Term Frequency Inverse Document Frequency (TF- IDF) is one of the most useful and widely used concepts in information retrieval. The method can handle without difficulty unstructured news of Saudi stock market (Tadawul) through reading and analysis of the news and build a relationship between the contents of the news, and keywords (core phrases). This technique must identified by analysts and financial specialists that affect the direction of the share price up or down. The aim of this paper is to explore the possibility of using text mining to automate the identification of financial news articles. The empirical results show that the proposed techniques can predict the up and down on a stock price after the news announce or released. The proposed method presented in the study is straightforward, simple and valuable for the short-term investors.

Аналітичний огляд методів і засобів інформаційного пошуку в Semantic Web

The article describes and analyzes the Information Retrieval (IR) methods and applications in the environment of Semantic Web. The author provided the basic Information Retrieval concepts, problems, models and classification of IR systems on various grounds. Examples of existing modern search engines, as well as highlighted the stages of development and listed a list of functional and architectural features of 3-rd search engines generation. The proposed model of IR extends the classification of search engines and search model with the possibility of finding new objects that have become available in the web, and use knowledge represented in the Semantic Web.

Survey on Relevance Calculation Methods

Relevance is one of the most important concepts in information retrieval. This paper discusses various approaches for relevance calculation available in literature. In Information Retrieval, relevance is how well a retrieved document or set of documents meets the information need. The paper summarizes, what relevance is and a survey of different methods of calculating relevance. Our goal is to study existing methods and to identify usefulness of existing methods for multilingual data and to understand the prerequisites required for these methods while using mime types likes webpages, urls, xml apart from plain text. General Terms Information Retrieval, Pattern Recognition, Algorithms