جستجوی مقالات مرتبط با کلیدواژه "استخراج دانش" در نشریات گروه "کتابداری و مدیریت اطلاعات"

Today, semantic technology offers a new approach in organizing Quranic knowledge with the aim of providing meaningful information and representing Quranic teachings. Ontologies are a tool to formally express concepts and relationships in a specific domain. In the same way, the development of ontology as a tool for representing the effulgence and extracting the knowledge of the Quran is not only valuable, but also necessary. Ontology learning and its methods automatically to extract concepts are important topics in the field of Semantic Web and its technologies. Recently, the development and application of ontologies learning for the extraction of Quranic concepts has been considered. Therefore, the aim of the current research is to comprehensively investigate the ontologies automatic learning in the field of extracting knowledge and Quranic concepts in order to clarify the current and future situation. The investigated criteria were data set, learning methods, evaluation methods, results and future suggestions of studies in the field of ontologies automatic learning of the Quran.

The research was conducted by the scoping review method in accordance with PRISMA guidelines and based on Arksey & O’Malley procedure. This process describes a protocol for matching the results of existing studies with research questions and criteria. The five steps suggested by Arksey & O’Malley are as follows: 1. Identify and design the research question(s), 2. Conduct search strategies advocate for relevant studies through the selection of appropriate keywords and Boolean operators, 3. Final selection of relevant studies, considering the inclusion and exclusion criteria, 4. Tabulating the data, and finally, 5. Reporting its results. Sources were searched in seven scientific databases including Emerald, Science Direct, IEEE Xplore Digital Library, Google Scholar, Web of Science, and Scopus. The search process has been done in April 2023. A number of 811 articles, regardless of the time limit, were evaluated and selected. In order to organize the retrieved articles, EndNote resource management software was used and after matching the titles in different databases, 317 duplicate articles were removed. After reviewing the abstracts, the entry and exit criteria and the quality of the articles were applied. Also, in order to avoid bias in the selection of articles, during a random review, two independent researchers in the field of ontology automatic learning were evaluated and finally 25 articles were selected as review criteria.

Most of the study in the field of Quranic data set were in English and Arabic languages, and most of them used the English translation of Al-Hilali and Khan's Quran. The use of a limited data set was the most important limitation of the research conducted in the field of automatic learning of Quranic ontologies. Most of the studies have used normalization methods, text clustering and categorization, text summarization, information extraction, similarity and finding famous entities. Of course, in some studies, artificial intelligence methods such as neural network have also been used. In addition, the findings showed that data mining algorithms based on statistics and probability methods for learning and constructing automatic ontologies was apparently surging in popularity among researchers. Evaluation methods includes calculating accuracy, recall and F criteria in the application of automatic learning algorithms in Quranic ontologies. The studies that have used artificial intelligence techniques, by Semantic analysis, inference, modeling and validation of inferred data have achieved results such as sound recognition for teaching Quran reading, recognition of literary arrays and creating thematic connections in Quranic concepts as well as creating connections between these concepts and concepts in other religions. The evaluation of the presented methods for ontology automatic learning shows that the combined use of data mining methods and artificial intelligence brings better results. Most of the results of this field are in two general categories. The first category was based on the use of data mining, text mining and machine learning methods to automatically extract three concepts and dimensions (subject-predicate-object) along with Semantic relationships from the text of the Quran. The other category compares the performance of methods and algorithms based on statistics and similarity, such as TF, TF-IDF, AVE-TF, Ridf, TIM, N-gram, FREyA, Pos Taggin, Levenshtein, Log Likelihod, Herset, etc. in extracting concepts for the construction of the Quranic ontologies. The findings of the future studies review show the researchers' interest in artificial intelligence algorithms and their use in ontology learning and the automatic and semi-automatic development of Quranic ontologies. The lack of correct data sets is the reason for the inability of the world's advanced artificial intelligence systems such as GPT 4, which must be addressed in the future.Discussion and

The results of this study can help to direct future research about the best practices in the automatic development of Quranic ontologies. This issue can be taken into consideration by designing a comprehensive Quranic ontology that covers all topics and concepts according to the context of the Quran, and by creating a comprehensive ontology of the Quranic concepts, it will guide users towards the retrieval of Quranic knowledge. Also, more use of artificial intelligence and natural language processing methods, such as GPT as a machine learning model for natural language text generation by deep neural network, it seems essential in the development of automatic learning of Quranic ontologies. Machine learning requires the existence of big data in the field of the Qur'an, hence the creation of standard data sets is one of the future studies.

Service industries are recognized as one of the largest sectors of the economy globally, and it has the most prominent role in the countries' economic growth. To create an essential change that represents a revolutionary change in the technology of a product or service, there is a need to acquire, extract and develop knowledge to achieve a competitive advantage. Therefore, this study aims to analyze the knowledge development clusters in the service industry's knowledge extraction field. In the knowledge management process, knowledge extraction is the main phase of knowledge acquisition. Knowledge acquisition is one of the important aspects of knowledge discovery in databases to help managers make timely decisions by extracting correct knowledge.

Bibliometrics and scientific mapping techniques have been used in this applied research. Research data were collected from the Scopus database from 1986 to 2022. VOSviewer and Bibliometrix R were used to analyze and visualize data and scientific maps. Furthermore, to ensure the accuracy and validity of the results, Bibliometrix and Excel tools have been used to integrate data and remove duplicate data.

The research findings show the knowledge extraction application among 434 documents in 5 clusters of knowledge extraction, artificial intelligence, information retrieval, semantics, and forecasting. In the research, knowledge extraction and data mining are the most widely used words in a single cluster and have the most centrality and betweenness. Likewise, the bibliometric analysis of the data in The Multiple Correspondence Analysis (MCA) shows that the Internet, natural language processing, and machine learning are among the topics that are important next to the healthcare sector. This shows the importance of natural language and machine learning in extracting knowledge in healthcare services. Since 2006, the importance of knowledge extraction has received more attention. The co-occurrence of keywords shows that knowledge extraction is widely used with data mining, extraction, and artificial intelligence. The keywords of knowledge extraction and data mining in cluster 1, semantics, knowledge management, and information services in cluster 2, and information retrieval, internet, and human in cluster 3 have the highest centrality. The theme mapping shows that forecasting, multi-agent systems, and planning are themes with high density and low centrality, which are called niche themes. Semantics, web services, and knowledge-based systems are the main themes with low density and high centrality. Also, artificial intelligence, information management, and decision support systems are themes with low density and centrality, which are also known as emerging or declining themes. The forecasting cluster is located in the strategic knowledge cluster group. Information retrieval, knowledge extraction, and artificial intelligence are included in the cluster of practical knowledge. Semantics as a cluster including various experts and specialists such as domain experts, knowledge engineers, and programmers is in the collaborative cluster.

Knowledge extraction is an emerging interdisciplinary field in knowledge management and has a direct and significant impact on the country's economy. Knowledge development and integration of key issues in knowledge extraction are essential. According to the findings of this study, for the promotion and advancement of this process in the service industry, it is suggested to provide a strategic view in the use of metadata analysis of the context of activity and success of the service industry in knowledge extraction. Moreover, knowledge management as the primary discipline and domain can guarantee success in this process. The clusters identified in this study are also divided into three practical, strategic, and collaborative knowledge clusters. Moreover, the results of this research can help managers of organizations, especially their knowledge managers, to plan and make decisions in the field of service industries to facilitate optimal knowledge extraction and maintain competitive advantage.

The importance of smartening in industrial business processes has increased significantly in recent years. On the other hand, the importance of knowledge as a competitive advantage continues growing. The purpose of this article is to identify the essential components affecting the intelligent extraction of knowledge in organizations.
This Meta-synthesis study was performed by Sandelowski & Barroso seven-step method. 280 research articles were retrieved from the database, of which 32 articles were used for research purposes. Each selected research paper reported one or more components that were analyzed separately.
48 codes were classified into 6 main topics ("Individual factors", "Education and learning", "Technology and intelligent technology factors", "Knowledge", "Dynamics and agility", "Organizational factors"). The results show that "empowering people in business" is one ofthe most important components of knowledge acquisition, the strengthening of which can leadto intelligent knowledge extraction. By empowering employees and studying the way theythink and work in the organization, intelligent models for performing tasks can be defined, andthis can lead to the extraction of useful knowledge. In other words, the greater the ability of employees, the study of human behavior in the workplace leads to the discovery of stronger intelligent patterns.
There is no organized study on the components affecting intelligent extraction of knowledge, and this is the first study in this field to classify topics into an organized framework for intelligent extraction of knowledge and find appropriate solutions for businesses.

The development of innovation in the service sector is a complex process that is affected by various factors, and the service sector due to its commercial nature must be updated in its innovative processes. Since knowledge innovation is required, knowledge extraction is essentially required to achieve goals and innovation in the industry. Appropriate and timely knowledge leads the industry to increase its productivity and level of innovation. This article is intended to provide a model for knowledge extraction in the service industry. For this purpose, the grounded theory model has been used, which is applied in terms of purpose and qualitative research in terms of collecting information. The population includes experts and managers in the service industry and the sample is due to the theoretical saturation of 14 people who were selected in a purposive sampling. The data collection tool, in the qualitative section, was a semi-structured interview. To confirm the accuracy of the data, the validity of the study was used by the research members. In this study, to calculate the reliability, the method of intercoder agreement has been used. The percentage of thematic agreement confirms encodings with 80% reliability. ATLAS.TI software was used for coding. After analyzing the data, a knowledge extraction model was extracted for the service industry. Based on this, 122 concepts and 18 sub-categories have been categorized using the Strauss and Corbin paradigm model. These factors were divided into 18 categories in the common model of grounded theory, which are: causal conditions (infrastructure and technological factors, structural factors, human resources, organizational culture, support of senior managers), contextual conditions (organizational strategic knowledge, management Organizational communication, support for intelligent knowledge extraction system), strategic (knowledge commercialization strategies, creativity strategies, innovative strategies), interventionist conditions (personal characteristics, financial factors, managerial factors), consequences (value creation, advantage) Competitive, globalization, growth, and maturity). Therefore, according to the results of this study, vital resources in the service industry can be identified to extract knowledge in a timely and appropriate manner, and the knowledge obtained from this extraction can be used to create a competitive advantage in domestic and foreign markets.

Knowledge problems in business processes refer to those problems result from the lack of proper implementation of knowledge management processes, such as creation, deployment, sharing and storage. The existence of knowledge problems during process execution will lead to a deviation in the process path. Identifying and detecting these deviations requires a precise tool that can detect the actual process from event logs. process mining is a new smart approach that examines business processes from different aspects, using data mining techniques, social network analysis, and some of its own techniques. In this study, by selecting a contact center of Water and Wastewater Organization have compared the design process and the extracted process resulting from the discovery phase of the process mining, which has led to the identification of the deviations in the process pathway and the bottlenecks in the process. For this purpose, the three months data were gathered and after the preprocessing, each of the three views of the process, organization and case was implemented on the data. The results of the research indicate that there is a lack of conformity of the process in some cases with the main process and the presence of key people in the center.