Intelligent Search System Research Articles

A novel customizing knowledge graph evaluation method for incorporating user needs

Knowledge graphs are now widely used in various domains, including Question-and-answer systems, intelligent search and recommendation systems, and intelligent decision-making systems. However, knowledge graphs inevitably contain inaccurate and incomplete knowledge during the creation process, which leads to a reduction in the usefulness of knowledge graphs. Therefore, to assess the usefulness of knowledge graphs based on specific application requirements, quality assessment is particularly important. Among them, accuracy assessment, as a necessary dimension, reflects the degree of correctness of the triples. However, in the actual assessment process, the existing assessment methods do not consider the user’s needs and do not implement the concept of “Fitness for Use”. Meanwhile, it takes a lot of labor cost to evaluate the accuracy of large-scale knowledge graphs. Therefore, to ensure the accuracy of the assessment in a cost-saving way while meeting the needs of users, we propose and implement a novel accuracy assessment method that focuses on the requirements of users by designing an effective sampling method to obtain accurate assessment results that are more practical and instructive for users. Finally, the performance of our proposed method is evaluated by comparing it with the real accuracy rate, and the experimental results show that the accuracy rate obtained by the proposed method is very close to the real accuracy rate, and the sample size is minimized.

Determining the ability of artificial intelligence to establish authorship of artistic ukrainian texts using significant fragments

Artificial intelligence is becoming an integral part of everyday life and profes-sional activity of a person. Bing, as an intelligent search system, can serve as a tool for determining the authorship of artistic text in Ukrainian. Bing helps to uncover in-formation about a text fragment and its author, although the search results may be inaccurate or incomplete. This work aims to explore the effectiveness of determining the authorship of ar-tistic texts using modern artificial intelligence tools based on significant fragments of works. Ten Ukrainian authors with a rich body of artistic works, reflecting various as-pects of Ukrainian culture and history, were selected for the experiment. Random fragments up to 500 words in length were selected from various works of these au-thors. An experiment was conducted to determine the authorship of 360 fragments. Using the Python programming language and the skpy package, software was created that sends queries and receives responses from the Bing bot embedded in Microsoft Skype. The presence of the author’s name and the corresponding title of the work were checked in the response texts. This work introduces, for the first time, a method of verifying the authorship of Ukrainian-language text fragments using the Bing bot equipped with artificial intelli-gence. A comparative analysis was conducted and experiments were carried out to identify the authorship of significant long fragments. It was found that long fragments allow the author of the artistic Ukrainian text to be determined with high accuracy. Ivan Franko has the highest percentage of re-sponses where the author’s name and the title of the work were mentioned - 87%. The proposed hypothesis regarding the effectiveness of artificial intelligence in establishing authorship of works has not been confirmed. Artificial intelligence has slightly lower efficiency than expected, which indirectly exposes its means of opera-tion. Namely, when establishing authorship, a sequential research comparison of the proposed fragment with a bank of works that are widespread in the Internet environ-ment is not performed.

Automation in Library Science in AI Era

The Auromation of library science has become a revolutionary force in the adminstration and accessibility of information resources in the age of artifical intelliegence. The goal of this project is to investigate and apply AI -driven solution to improve resource management, user services, cataloguing and other areas of library operation. in order to streamline the organisation and retrieval of information, the automation process uses machine learning algorithms to tag and categorise library material. Intelligent search and recommendation systems are developed using Natural Language Processing(NLP ) approaches, which facilitities the effortless discovery of pertient information by users. Additionally, by integrating AI into library systems, libraries may better forecast demand using predictive analytics and adapt their resources accourding to user perfence and new trends. Robotic process automation also simplifies repetitive administrative work, freeing up human resources to concentrate on more difficult and valuable duties. The project uses strong encryption techiques and access control to address issue with data security and privacy, the creation and application of AI technologies in library science also heavily relies on ethical consideration, such as bias prevention in algorithms. Libraries may transform their services and remain ata the forefront of information management technology by adopting automation in the AI era and offering users a more efficient and customised experience. This abstract highlights the potential for major breakthroughs in the field of library science through the integration of artifical intellience , outlining the project's main goal and technique.

Transforming Libraries Sustainably: A Synergy of AI and Machine Learning

In the rapidly evolving landscape of information management, libraries are undergoing a profound digital transformation to remain vibrant knowledge centers. This article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts for sustainability within libraries. The application of advanced algorithms not only revolutionizes information retrieval with intelligent search systems but also optimizes resource management through predictive analytics. Libraries can leverage AI to create personalized learning experiences, recommending resources tailored to individual preferences and learning styles. Automation, guided by ML, streamlines processes, minimizing environmental impact by ensuring efficient use of physical resources. Beyond the digital realm, smart building systems powered by AI enhance energy efficiency, contributing to the overall sustainability of library infrastructure. As libraries embrace AI and ML, they not only adapt to the digital era but also play a crucial role in fostering a more sustainable future

Intelligent search system for resume and labor law.

Labor and employment are important issues in social life. The demand for online job searching and searching for labor regulations in legal documents, particularly regarding the policy for unemployment benefits, is essential. Nowadays, each function has some programs for its working. However, there is no program that combines both functions. In practice, when users seek a job, they may be unemployed or want to transfer to another work. Thus, they are required to search for regulations about unemployment insurance policies and related information, as well as regulations about workers working smoothly and following labor law. Ontology is a useful technique for representing areas of practical knowledge. This article proposes an ontology-based method for solving labor and employment-related problems. First, we construct an ontology of job skills to match curriculum vitae (CV) and job descriptions (JD). In addition, an ontology for representing labor law documents is proposed to aid users in their search for legal labor law regulations. These ontologies are combined to construct the knowledge base of a job-searching and labor law-searching system. In addition, this integrated ontology is used to study several issues involving the matching of CVs and JDs and the search for labor law issues. A system for intelligent resume searching in information technology is developed using the proposed method. This system also incorporates queries pertaining to Vietnamese labor law policies regarding unemployment and healthcare benefits. The experimental results demonstrate that the method designed to assist job seekers and users searching for legal labor documents is effective.

Methodology for research and data analysis to develop an intelligent drug search system

The work presents the author-developed tools for conducting medical and sociological research in the field of modern services for searching and purchasing medicines using the capabilities of the Internet. The methodology for conducting sociological research is described, and the authors’ questionnaire for surveying users is presented. The toolkit allows studying various consumer experiences of purchasing medicines in order to predict the creation of Internet services in the pharmacy segment of the pharmaceutical market.

Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study.

The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)-based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a "frequently asked questions" component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment-based system. We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question's semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model's performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry.

Theoretical and methodological review of the modern drug market

The aim of the study is to research the possibilities of developing an intelligent drug search system. In order to do this, the statutes and regulations in the field of circulation of medicines in the Russian Federation have been studied; a comparative analysis of the search capabilities of popular digital services for the sale of medicines has been carried out. The object of the study is a system of drug search through digital services. The subject of study is the features of the functioning of systems of drug search through digital services. According to the authors’ hypothesis, the introduction of an intelligent drug search system will allow improving the search for certified drugs and enhancing user experience.

Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network.

This exploration aims to study the value orientation and essence of early childhood enlightenment education based on the deep neural network (DNN). Based on the acquisition and feature learning of cross-media education big data, the DNN correlation learning of cross-media education big data, and the intelligent search of cross-media education big data based on the DNN, the intelligent search system of cross-media children's enlightenment education big data based on DNN is designed and implemented. The system includes three functional modules: the feature learning module of cross-media infant enlightenment education big data, the deep semantic correlation learning module of cross-media childhood enlightenment education big data, and the intelligent search module of cross-media childhood enlightenment education big data based on DNN. This exploration realizes the acquisition and feature learning of big data of cross-media early childhood enlightenment education, DNN learning of cross-media education big data of early childhood enlightenment, and intelligent computing of cross-media education big data based on DNN. The experimental results show that the proposed system's mean average precision (MAP) performance is improved by 15.6% on the public dataset of early childhood enlightenment education published by the Ministry of Education. Moreover, the system has also significantly improved the MAP performance of the constructed dataset in the field of early childhood enlightenment education; that is, the MAP performance has been improved by 20.6% on the dataset of Taylor's University in Malaysia (NUS-WIDE). This exploration has certain theoretical significance and empirical value for early childhood enlightenment education research.

Natural Language Processing and Computational Linguistics

Natural Language Processing and Computational Linguistics

Analysis of approaches for identification the ontological model components of the searching system

The object of research is the components of an intelligent system for searching information in electronic repositories of unstructured documents, which based on the ontologies of the subject area. One of the most problematic areas is the processing and analysis of information contained in electronic repositories of unstructured documents. There are considered the some possibilities of increasing the efficiency of information processing. In the course of the study, using the method in which ontologies comprise sets of terms presented in it. In addition, the ontological set also includes information about subject areas, areas of definitions, etc. There are obtained the sequence of defining the conceptual representation of an intelligent search system based on ontological components. There are presented the composition of the ontological system model. There are described the main functional components of the system for intelligent processing of information about electronic documents. The proposed approaches for identifying the component components of the ontological model of the search system have a lot of features. This is due to the fact that the search system model must have a set of properties: integrity, coherence, organization, integrability, mobility. Ontologies which representing the basic concepts of the domain in a format available for automated processing in the form of a hierarchy of classes and relationships between them allow automated processing. The using of ontologies in the role of an intermediary between the user and the search process, between the search process and the search system that can facilitate the solution of a number of complex and non-standard tasks of information retrieval (for example, the automation of the search process). It is possible to solve the problem of knowledge representation for displaying information relevant to user requests, as well as to solve the problems of filtering and classifying information. Compared to similar well-known search systems, this provides such advantages as creating a common terminology for software agents and users, protecting the information store from total overflow and errors, as well as solving the issue of information aging.

Framework for tasks suggestion on web search based on unsupervised learning techniques

Search systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user’s search task can be complex and may not be expressed using a single search query, requiring the user to write several queries to fulfill all the aspects of his or her needs. In such scenarios, an intelligent search system would be beneficial to identify and understand the original search task issued by a user and then suggest several search tasks (in a form of key-phrases or short topics) related to the original search task. Aiming to tackle this limitation, this paper proposes a framework for applying several unsupervised learning approaches, including topic modeling and log mining. The results of applying these approaches to large user session data show that, indeed, these approaches would be applicable in search suggestion and task recommendation, reaching a significant improvement over a strong baseline.

Applied Research of Knowledge in the Field of Artificial Intelligence in the Intelligent Retrieval of Teaching Resources

In the development process of education informatization, digital teaching resources continue to grow, and how to manage and organize massive teaching resources has become a key issue for teaching staff. An efficient and accurate search system is an important part of the teaching resource service system. The use of intelligent search engines can search teaching resources comprehensively and efficiently, and the artificial intelligence search engine provides a reliable and convenient solution for the design and development of intelligent search systems. The article analyzes the design principles and technical standards of the intelligent search system, expounds the system’s functional architecture and database design, and introduces the realization process and principles of the search system. By analyzing the characteristics of basic education resources and existing automatic abstracting methods, this paper proposes to integrate the calculated feature word weights in the field of basic education into the algorithm for calculating the weights of abstract sentences and simultaneously examine the sentence position, sentence length, and other texts. There is an automatic summarization algorithm for surface statistics. This article also introduces the search design ideas and implementation steps based on artificial intelligence, makes a scientific evaluation and summary of the actual situation of the automatic abstract system running in the basic education resource search engine, and looks forward to the next improvement work.

Research on the Construction Path of Ideological and Political Education for Postgraduates in the Era of Artificial Intelligence

Postgraduate ideological and political education is an important part of ideological and political education in colleges and universities. It is the core of implementing the party’s educational policy, comprehensively improving the quality of education, and building a modern socialist education power. In the era of artificial intelligence, innovate classroom education of ideological and political education for graduate students through intelligent network systems, natural language understanding systems, and knowledge processing systems; use intelligent search systems, symbol processing systems, and combined planning systems to deepen the communication links of graduate ideological and political education; through enhancing digital intelligence tools such as genetic algorithm, deep learning algorithm and artificial neural network algorithm to reconstruct the evaluation criteria of graduate ideological and political education is an important engine to promote the intelligent, contemporary and diversified development of graduate ideological and political education in my country.

Development of a multi-agent intelligent system for solving problems of classification and ranking of materials on the Internet

The article proposes an architectural solution for intelligent information systems based on a multi-agent approach and microservice architecture. An example of using the described solution in the development of an automated intelligent search system that solves the problem of thematic classification and ranking of various sources of information on the Internet is given. The advantages and disadvantages of the developed architectural solution are described.

SMART SEARCH IN THE DATABASE OF PHYSIC-TECHNICAL INFORMATION

The article describes the process of developing an automated intelligent search system based on physical effects. The developed system performs descriptor, full-text search, logging of search queries, displaying physical effects and other functions.

Effective scientific personnel training in the field of modern computer technologies for the implementation of advanced research projects of the Megascience class

Successful research projects of the Megascience class usually require a well-trained team of scientists from various fields of knowledge. These scientists must be high-skilled experts. Each member of a team like that must have the necessary, specialized cross-industry skills, for example, in such areas as artificial intelligence, convolutional neural networks, specialized intelligent search engines, and full-text analysis. One of the key aspects of effective personnel training for successful implementation of Megascience projects into reality is the acquisition by students professional skills, abilities, and knowledge to use tools of modern scientific technologies, containing, for example, libraries of programs (functions). In particular, convolutional neural networks and intelligent search systems can be applied in various research projects in the field of physics, chemistry, biology, and medicine, for example, in telemedicine, for effective decision-making in diagnosing a patient. Therefore, understanding the principles of neural networks and intelligent search systems is a necessary competence of researchers working in the framework of Megascience projects. Classic search engines are based on indexing the textual information of the database that is being searched. Intelligent search engines can improve the search experience through intelligent data processing, including using convolutional neural networks. This report examines practical examples and areas of the successful application of convolutional neural networks and information systems in practice.

Bi-level programming enabled design of an intelligent maritime search and rescue system

This paper studies an intelligent maritime search and rescue (SAR) system problem. According to historical accidents and available SAR equipment information, a bi-level mixed-integer programming (MIP) model is proposed to determine the type and number of SAR equipment allocated to activated stations. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) algorithm are applied to solve the proposed mathematical model. Computational experiments based on real instances in the East Sea China not only validate the effectiveness of the bi-level MIP model in balancing two objectives during decision process, but also indicate that PSO algorithm is better than GA algorithm to solve the proposed model and generate reasonable equipment allocation plans. Some managerial implications are also outlined on the basis of the numerical experiments.

Application intelligent search and recommendation system based on speech recognition technology

With the progress of science and technology, artificial intelligence as the emerging tool has been deeply into our lives. The application of modern intelligent equipment is more and more extensive, and also inseparable from our lives. The mobile intelligent terminal represented by smart phone is a powerful intelligent computing and networking device, which has more perception and interaction capabilities. Due to the emergence of a large number of application software, it is not easy to find and get the app that you really need. Therefore, this paper uses speech recognition technology to build an app intelligent search and recommendation system. We designed the system from the levels of speech information extraction, analysis and finalized recommendation. Experimental results show that the proposed method is more efficient and intelligent.

On ontology based data integration: problems and solutions

In the article essential problems of integrating heterogeneous data, arising in development of corporate databases intellectual access systems, are considered. In addition to the common structural problems, caused by variety of data organization, special attention is paid to the less obvious linguistic problems, caused by differences in data notation. A unified approach to overcoming such problems by sequential application of explicit definition of semantics, is described. This approach was tested in development of an intelligent search system for the TATNEFT oil-producing corporation; the system implementation showed high relevance of search results together with an adequate reactivity.