Accuracy Of Information Retrieval Research Articles

Text document clustering using mayfly optimization algorithm with k-means technique

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).

Information Embedding in Additively Manufactured Parts Through Printing Speed Control

Abstract Information lithography in manufacturing is a broad set of techniques for encoding sequences of bits as physical or behavioral features in physical parts. It is an effective approach for part traceability and anti-counterfeiting. Several techniques have recently been proposed for embedding 2D codes in 3D printed parts by local control of geometry or material. This paper presents an approach to embed and retrieve information in additive manufacturing (AM) parts by controlling the printing process parameters. The approach leverages variations in printing speed to encode information on the surface of AM parts. Optical imaging devices, such as 2D scanners and optical profilometers, are employed to read the embedded information, enabling the capture of local height differences on the part surfaces that embody 2D codes such as QR codes. The retrieved information is processed using computer vision techniques such as morphological segmentation and binary classification. First, the impact of variations in the encoding parameters on the information retrieval accuracy is characterized. Then, the feasibility and effectiveness of the proposed scheme are demonstrated through experimental results, showcasing a high accuracy in retrieving encoded messages and successfully distinguishing subtle surface features resulting from varying printing speeds. The proposed approach offers an inexpensive and efficient method for information lithography, allowing for the secure embedding of information, e.g., serial numbers and watermarks, while addressing counterfeiting and security concerns in diverse industries.

Knowledge Mapping Applied to the Construction of a Theoretical Resource Base for Teaching English at the University Level

Abstract In the evolving field of educational technology, knowledge mapping has emerged as a pivotal tool for university English education. This study examines its role in building a theoretical resource base to enhance the efficiency of organizing, accessing, and utilizing teaching materials. This research constructs a knowledge graph for English teaching resources by employing a seven-step process to create a domain ontology and an enhanced TF-IDF algorithm for keyword extraction. Results show the knowledge graph significantly boosts information retrieval accuracy and user satisfaction. The experimental group reported a 25% increase in learning efficiency, a 30% enhancement in the quality of learning outcomes, and a 12.63% rise in average grades, outperforming the control group. The findings suggest knowledge mapping substantially benefits the development of a theoretical resource base for university English teaching, improving teaching and learning quality.

Graph Convolutional Networks for Cross-Modal Information Retrieval

In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

IMPLEMENTATION OF THE APPROACH TO ONTOLOGICAL MODELING OF THE MUSICAL SUPERGENRE “METAL”

Background. The relevance of the article is due to the development of modern ontological methods of structuring information and the need to systematize data in many new specific subject areas. Such subject areas include the musical art of the "metal" variety, which is quite common today, but insufficiently studied within the terminology. The subject of the article are ontological models and tools for creating ontological knowledge bases.
 Objective. The purpose of the paper is to increase the correctness of the semantic search in the knowledge base of the musical supergenre "metal". The scientific problem is the need to improve the terminology in this subject area and build an ontological knowledge model that increases the accuracy of information retrieval for the target audience, compared to the existing relational model implemented on one of the known web resources. 
 Methods. Classification method, generalization method, software optimization methods, analytical method. The way to solve the problem: selection based on the comparative characteristics of the best web resource of the subject area and identifying the shortcomings of its model of knowledge representation, designing an ontological knowledge model and testing its effectiveness. 
 Results. The average SUM for all users is 83.85%, which is a good indicator for ontological knowledge bases. At the same time, a similar method of checking the database of the supergenre "metal" on the basis of the site "Encyclopedia Metallum", which used the classical relational model of database organization, showed much lower results. Thus, the average SUM for 10 users was 75.32%, respectively. 
 Conclusions. The scientific novelty of the obtained results is as follows: For the first time an ontological model (ontology) of the subject area was created: musical supergenre "metal", which showed much higher efficiency of semantic search than the best relational model of this subject area, implemented as a web resource. The developed structure can be used to create ontologies of related musical supergenres with similar terminology. Future research also plans to integrate this ontological knowledge model with applied web-based and desktop applications.

Human-centered development of an electronic health record-embedded, interactive information visualization in the emergency department using fast healthcare interoperability resources.

Develop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards. We applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient's asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users. Usability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction. Following HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented. Compared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.

Performance analysis of shape, color and texture features on tracking information face based on CBIR

The image has the features of shape, color and texture that are vary. Each feature has a different performance in supporting the accuracy of information retrieval using a process approach to CBIR (Content-Based Image Retrieval). On the image with different objects different performance will be generated on each feature. For example, that the performance features of the form of the more dominant compared features color and texture on the image with the face, while the object on the image with the object of interest feature is more dominant than the features of texture and shape. In this research was conducted on the analysis of the performance features of the shape, color and texture in supporting the accuracy of a search using the approach of CBIR (Content Based Image Retrieval). The method used are invariant moment, color moment and GLCM (Grey Level Co-occurrence Matrix). The results showed that the best search accuracy is 95%, where the features of shape has a performance by 50%, 30% color feature and texture feature by 20% with 600test image with object database face.

Application of Quicksort Algorithm in Information Retrieval

With the development and progress of today’s network information technology, a variety of large-scale network databases have emerged with the situation, such as Baidu Library and Weipu Database, the number of documents in the inventory has reached nearly one million. So how do you quickly and effectively retrieve the information you want in such a huge database? This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval, improve retrieval efficiency, and adapt to the rapid expansion of document data. The Quicksort Algorithm gives different weights to each position of the document, and multiplies the weight of each position with the number of matches of that position, and then adds all the multiplied sums to set a feature value for Quicksort, which can achieve the full accuracy of Information Retrieval. Therefore, the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval, and to use the position weighting algorithm to improve the matching quality of Information Retrieval, so as to achieve the overall effect of improving the efficiency of Information Retrieval.

Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network

In recent studies, Ontology construction plays an important role in translating raw text into useful knowledge. The proposed methodology supports efficient retrieval using multidimensional theory and implements integrated data training techniques before enter the trial process. The proposed approach has used the Semantic and Thematic Graph Generation Process to extract useful knowledge, and uses data mining techniques and web solutions to present knowledge as well as improve search speed and information retrieval accuracy. Established ontology can help clarify what it means for different ideas and relationships. Due to the rise of the ontology repository, the process of matching can take a long time. To avoid this, the method produces a hierarchical structure with in-depth interpretation of the data. A system is designed to remove domain dependencies using a dynamic labeling scheme using basic theorem, and the results show that it is possible to automatically and independently construct an independent domain.

Semantic Search Tools Based on Ontological Representations of Documentary Information

Ontological means of identification and representation of text documents semantics in relation to the problems of interactive information retrieval are considered. The implementations of ontologies operations are presented, which allow forming images of new meanings in the subject area. Taxonomies of relations (paradigmatic and syntagmatic) and entities (polythematic thesaurus of concepts) are used to perform operations, as well as to identify fuzzy relationships (in the case when entities and relationships are specified at different levels of generality and/or expressed by different linguistic constructions). Interactive tools are proposed that use the operation of constructing aspect projections for graph representations of ontologies, which make it possible to reduce the dimension of the graph to a level acceptable from the point of view of display and perception. The possibilities of context-based use of entities and relationships for the development of the search process are considered. This paper discuses the use of ontology graphs of organizational processes as a “navigation map” that provides new entry points in the graphical interface, that is, objects that will be contextually defined patterns of search queries in similarity selection tasks or expert analysis tasks. This allows one not only to increase the completeness and accuracy of information retrieval, but also to make the scheme of search navigation more natural, bringing it closer to the schemes of understanding and synthesis of knowledge.

An automated data verification approach for improving data quality in a clinical registry

Background and ObjectiveThe quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (CRFs) and further transcribing them onto registry databases. To ensure the quality of data, verifying data in the registry is necessary. However, traditional manual data verification methods are time-consuming, labor-intensive and of limited-effect. As paper-based CRFs and electronic medical records (EMRs) are two sources for verification, we propose an automated data verification approach based on the techniques of optical character recognition (OCR) and information retrieval to identify data errors in a registry more efficiently. MethodsThree steps are involved to develop the automated verification approach. First, we analyze the scanned images of paper-based CRFs with machine learning enhanced OCR to recognize the checkbox marks and hand-writing. Then, we retrieve the related patient information from the EMRs using natural language processing (NLP) techniques. Finally, we compare the retrieved information in the previous two steps with the data in the registry, and synthesize the results accordingly. The proposed automated method has been applied in a Chinese registry study and the difference between automated and manual approach has been evaluated. ResultsThe automated approach has been implemented in The Chinese Coronary Artery Disease Registry. For CRF data recognition, the accuracy of recognition for checkboxes marks and hand-writing are 0.93 and 0.74, respectively. For EMR data extraction, the accuracy of information retrieval from textual electronic medical records is 0.97. The accuracy, recall and time consumption of the automated approach are 0.93, 0.96 and 0.5 h, better than the corresponding values of the manual approach, which are 0.92, 0.71 and 7.5 h. ConclusionsCompared to the manual data verification approach, the automated approach enhances the recall of identify data errors and has a higher accuracy. The time consumed is far less. The results show that the automated approach is more effective and efficient for identifying incomplete data and incorrect data in a registry. The proposed approach has potential to improve the quality of registry data.

Risk markers identification in EHR using natural language processing: hemorrhagic and ischemic stroke cases

This article describes the study results in the development of the method of analysis of semi-structured data from electronic health records to improve the quality of data describing patients’ treatment processes. Improving the accuracy of information retrieval from electronic medical records was achieved by using developed problem-solving oriented library. Moreover, the latent-semantic analysis of the electronic health records of chronic patients with chronic heart failure, diabetes mellitus, hypertension was performed. The main tokens characterizing different groups of patients were revealed. The developed library and semantic analysis based on it can be used to accurately automatic extraction of information from semi-structured electronic medical records. Automated markup of medical texts on the Russian language is also possible for the development of artificial intelligence systems and new generation clinical decision support systems.

Exploiting Parts-of-Speech for effective automated requirements traceability

ContextRequirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. ObjectiveFirst, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. MethodWe developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. ResultsThe results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. ConclusionWe showed that ConPos outperforms existing IR-based RT approaches that discard some POS tags from the input documents.

The extraction method of new logining word/term for social media based on statistics and N-increment

New logining word/terms are reasonably extracted to improve the accuracy of information retrieval and to provide the theoretical support for tracking network topics and social public opinion. To efficiently mine new logining word/terms from micro-blog, this paper presents an extraction method of new logining word/term for social media based on statistics and N-increment. First, some words with very high frequency occurring in micro-blog text, called as similar stop-words, are collected into a list according to abuttal entropy. Second, micro-blog text is preprocessed by deleting similar stop-words and non-text elements. And then the preprocessed micro-blog text is scanned to extract new logining word/term according to N-increment and internal statistics. The experimental results show that the proposed algorithm can accurately and quickly extract micro-blog new logining word/term.

Implicit Negative Feedback in Clinical Information Retrieval

In this paper, we reflect on ways to improve medical information retrieval accuracy by drawing implicit negative feedback from negated information in noisy natural language search queries. We begin by studying the extent to which negations occur in clinical texts and quantifying their detrimental effect on retrieval performance. Subsequently, we present approaches to query reformulation and ranking that remedy these shortcomings by resolving natural language negations. Our experimental results are based on data collected in the course of the TREC Clinical Decision Support Track and show consistent improvements compared to state-of-the-art methods. For queries that make excessive use of negations, we were able to achieve up to 300% relative improvement in early precision.

Ontology representation and mapping of common fuzzy knowledge

Fuzzy knowledge is prevalent in real life, and ontology is the main way of knowledge representation in semantic web. In this paper, two main kinds of common fuzzy knowledge are sorted out, and there is a great significance for ontology representation of common fuzzy knowledge in semantic web. In addition, searching knowledge in ontology is the most common operation of semantic web, but heterogeneous ontologies seriously affect accuracy of information retrieval, and ontology mapping is the key to solve the problem. Therefore, in this paper, firstly an ontology representation method of common fuzzy knowledge is presented. Common fuzzy knowledge is polytypic and includes most commonly used fuzzy knowledge in reality. So fuzzy sets and Cloud Model are used to reflect and represent these 2 types of common fuzzy knowledge in ontology. Then, based on the ontology representation method of common fuzzy knowledge mentioned above, a corresponding algorithm of ontology mapping is presented, which is based on similarity calculation of concepts and Support Vector Machine. The proposed methods extend the scope of ontology application and are significant for information retrieval of fuzzy knowledge in the semantic web. The experiments show that the proposed methods are practicable and effective.

AUTOMATIC TAGGING OF PERSIAN WEB PAGES BASED ON N-GRAM LANGUAGE MODELS USING MAPREDUCE

Page tagging is one of the most important facilities for increasing the accuracy of information retrieval in the web. Tags are simple pieces of data that usually consist of one or several words, and briefly describe a page. Tags provide useful information about a page and can be used for boosting the accuracy of searching, document clustering, and result grouping. The most accurate solution to page tagging is using human experts. However, when the number of pages is large, humans cannot be used, and some automatic solutions should be used instead. We propose a solution called PerTag which can automatically tag a set of Persian web pages. PerTag is based on n-gram models and uses the tf-idf method plus some effective Persian language rules to select proper tags for each web page. Since our target is huge sets of web pages, PerTag is built on top of the MapReduce distributed computing framework. We used a set of more than 500 million Persian web pages during our experiments, and extracted tags for each page using a cluster of 40 machines. The experimental results show that PerTag is both fast and accurate.

A Domain Information Retrieval Method based on Pension Insurance Ontology Concept Similarity

Matching key terms is the main method for most engines for information retrieval, but this method cannot meet the needs of people who want to retrieve useful domain information. This is because that it doesn’t consider the semantic relation of resources in the context. Ontology is an effective method that can solve the problem of domain knowledge sharing and semantic understanding. In this paper, an optimization strategy is presented for information retrieval domain, and an algorithm is also proposed for ontology concept similarity calculation. Experiments are carried out on the audit guidance system, and the results show that this method significantly improves the accuracy of information retrieval based on domain ontology

Effective ranking and search techniques for Web resources considering semantic relationships

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved.In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

The Research of a Multi-Language Supporting Description-Oriented Clustering Algorithm on Meta-Search Engine Result

Search engine has adopted a variety of techniques to improve the accuracy of information retrieval, but the way of a linear list of search engine results, which mixes unrelated documents with relevant documents, has brought user great burden. This article commits to build clustering of search results, which is based on meta search engine techniques. We use all the popular search engine as a data source, then after a certain pre-processing of the source search engine, hierarchical clustering results is formed and returned to the query users. we propose a multi-language supporting, label first clustering algorithm, which we named DCFC algorithm. This algorithm supports both Chinese and English query, focuses on generating human readable labels, shows search results in hierarchical structure.