پژوهشنامه پردازش و مدیریت اطلاعات
سال سی و ششم شماره 3 (پیاپی 105، بهار 1400)

Open science is the platform for the science or scientific outputs that need to be managed transparently from the beginning to the end and be accessible to the society. This concept covers many components includes open research data, open scholarly communication, and open access. Furthermore, several policies have been issued to provide a proper platform for open science in countries, due to the importance and upon their capacities. Thus, the purpose of this study is to provide a model for open science facilitator policies in Iran, conducted with a qualitative approach based on focus groups of researchers and experts in open science. The findings demonstrated that concerning the current countrychr('39')s environment, open science policies have to consider five main categories at this stage, which were identifying the nature of science, removing barriers, motivating, separating open science according to the components, and performance level. Afterward, the proposed model of the policies based on the STEEP(V) analytical model with six environmental, technological, social, economic, political, and value aspects was presented, along with the related policy recommendations. Consider openness and providing facilitators for the science ecosystem is essential, and this should be reflected in science, technology, and innovation policies.

Relevance is one of the concepts that many researchers consider it fundamental in information retrieval. Recently, relevance has also been given attention in Iran about which multiple studies have been conducted. However, there is no clear understanding of relevance-related studies in Iran. Therefore, the current research aimed at exploring related research through a systematic review. To find the publications, questions and inclusion/exclusion criteria were initially determined. Several databases were searched using related keywords. Finally, 108 publications were selected. The software Excel was used to extract and analyze the data gathered. Publications in the field of relevance could be classified into 6 groups including recall, precision, recall and precision, relevance feedback, relevance judgment, and review resources. The results showed that most of the studies are conducted in an objective way and the researchers considered search engines more than other information retrieval systems. Moreover, the results also indicate that the recent studies are related to relevance judgment and experts in Knowledge and Information Science have been unwilling to do research on relevance feedback. While the quantitative methodology is used more frequently, the qualitative approach could also be very effective, especially in the subjective relevance and user-friendly context.

Integration of existing ontologies is an approach for reusing ontology elements in construction of a new ontology. Researchers have proposed various tools and methods for ontology merging; but very few of them work in an integrated manner with a comprehensive ontology construction methodology and also none of them support Persian language. In this paper, we proposed a systematic merge-based method for reusing ontology elements with Persian support in ontology construction. Proposed method built based on the design science research method. The process and algorithms of proposed method implemented and improved based on general cycle of design science method. Proposed method used to create a reference ontology for educational and research organizations in the subset of Iranian ministry of science, research and technology. Final product evaluated and verified by a gold standard. An ontology merge method based on reuse of ontology elements and a higher education ontology are main products of this research.

This study is an applied research.The main aim of this research is to evaluate the impact of the selected Iranian Scientific Products in two areas of social science and computer science indexed in Scopus using alternative indicators (Altmetrics).  The research community consists of the articles of the Iranian researchers in the subject areas of computer science and social sciences indexed at the Scopus Database in 2014-2018. Some of the most important Altmetric service providers such as Altmetrics Explorer and Plum Analytics have been used to assess the presence of the research outputs in the social media and their impact assessment. Also, the relationship between variables such as scientific collaboration of researchers, open access journals and the quality of research journals with altmetric activity has been investigated through appropriate correlation tests. The findings of data analysis showed that in the field of social sciences, the extent of the presence of the articles of the Iranian researchers in the social media is about 12% and in the field of computer science is only about 4% of the total retrieved articles. The most important social media publishing the Iranian articles in each of the two subject areas are Mendeley, Twitter and Facebook. Due to the low level of correlation coefficient between the two variables of scientific collaboration of researchers and the altmetric activity, it seems that the increase in the number of authors in the two thematic areas does not have a significant effect on the altmetric score of these articles. The results of the correlation test showed a significant and weak statistical relation between journal openness and the altmetric scores in the social sciences. While in the field of computer science there was no significant statistical relation between these two variables. Also, due to the correlation between the two variables of the quality of the journals published the studied articles and their altmetric activity in both areas of the study, it can be concluded that the published articles in the journals with higher quality indicators have higher altmetric scores and are more likely to be present in social media.The results of these studies show that using altmetric indicators, one can evaluate the impact of research in different subject areas and compare them. Science and technology Policy makers can consider the presence of articles in social media and altmetric indices in scientific policyalong with traditional research evaluation indicators

Question answering (QA) helps one go beyond traditional keywords-based querying and retrieve information in more precise form than given by a document or a list of documents. These communities allow users to submit queries and receive answers or responses from other members. But, there is no clear way of evaluating the quality of that information. The purpose of this study was to evaluate and predict the quality of responses in the Research Gate Social Science Network. To achieve this goal, the questions and answers entered in the field from January to May 2019 surveyed in the field and the required information was collected by the site crawler. Finally, 54 questions and 443 answers were analyzed in descriptive and inferential levels by SPSS 22 software. The results show that the relevance, adequacy, and concordance variables with the odds ratios of 3.626, 3.440 and 3.148 have the most power to predict the correct or incorrect responses, respectively.

Despite abundant research on data quality, no research has so far been conducted which can provide a comprehensive view of data quality methodologies. In the present study, 3909 articles and related researches in the period before 2020 were selected from Web of Science (WOS) and Scopus citation indexes, from among which 27 articles were finally evaluated in line with the research goals, using meta-synthesis method and inclusion criteria. In this regard, while applying the system view and using the open coding method, the related codes to the three main categories of the systemic approach (input, process and output) were extracted. The similar concepts were categorized in sub-codes and then the sub-codes in main codes. The main inputs included the context and status of organization, data and information resources, and data quality dimensions. Steps of data quality methodologies were also classified into three main stages: state reconstruction, measurement/ evaluation, and improvement. In addition, data quality outputs fell into six general categories: list of activities and the related techniques for data quality improvement, controlled or redesigned processes, measured or improved flows and databases, data quality status results, revised data quality policies or rules, and costs and benefits. The results of this study can provide an appropriate instrument for identifying the existing data quality methodologies as well as evaluating the strengths and weaknesses of data quality methodologies.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other NLP[1] tasks. There are several ways to use such information, one of which is text summarization Summarizing textual information can extract the main content of text within a short time. In this paper, we propose an approach for extractive summarization on Persian texts by using sentences embedding and a sparse coding framework. Most previous works focuses on text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole text with least reconstruction error.  The proposed approach is evaluated on Persian dataset Pasokh and achieved 10.02% and 8.65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the text’s sentences can lead us to better sentence summarization.   

In the recent years, with the increasing volume of articles and the use of Internet and search engine services, the author name disambiguation problem has received a lot of attention. Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and also when there are multiple other authors with the same name. So far, various methods have been proposed to solve this problem, each of which has its own advantages and disadvantages. Despite years of research, the name disambiguation problem remains largely unresolved. In this study, we propose an algorithm to identify several records that belong to one author. For this purpose, a new criterion has been proposed to determine the similarity between the two records. Since this study addresses the approximate matching of authorschr('39') records, the importance of the fields in each record is determined by the coefficients. In order to get the optimal coefficients, we propose a genetic algorithm to learn from the available samples. The proposed method has been evaluated with two fitness functions on experimental data and the results are promising.

Evaluation of interactive question answering systems plays an important role to determine the effectiveness of these questions is of great importance. In the process evaluation of an interactive question answering system, several features can be considered. One of the most effective criteria in the assessment of these systems is reliability. Few attempts have been made to measure the reliability and accurate determination of reliability criteria in interactive question answering systems by applying different models, and most tasks have been used only or qualitative evaluation. In this paper, a new method for the reliability of reliability is presented using the set of criteria affecting this feature, including qualitative and quantitative criteria. In this method, first a list of qualitative and quantitative characteristics is collected and then according to the criteria set, 8 quantitative characteristics are selected and then based on the prepared questionnaire, the coefficients of impact of each question are determined and by combining these characteristics according to the proposed relationship of reliability was measured. The evaluation of the performance of the selected criteria on four interactive question answering indicates that by applying this set of criteria, the reliability of an interactive question answering system can be used to automate the calculation of this feature in the process evaluation process.

In this digital age, companies figured out that without using digital technologies and disruptive mindsets, they cannot survive. Digital transformation is the solution to many of organizational strategic problems in this age. Studying digital transformation elements has shown that governing this tremendous change is one of the most important factors for succeeding in this transformation. But there is a lack of conceptualizing for digital transformation governance. Role of the Chief Digital Officer is the most referenced organizational position for managing and governing the digital age disruption. But there wasnchr('39')t a competency model for recognizing a good CDO in academic literature. In this paper, we used two steps research method for development of a competency model for CDO. At first a meta-synthesis method was used to develop the first version of the competency model. After that, through expert interviews, this model was checked and improved so, at the end of two phases, a competency model for CDOs was introduced. It has 71 competencies in 4 categories. "Personal traits", "professional skills", "business knowledge" and "technical knowledge". The first two categories have many similarities with other c-suits, but the knowledge of digital technologies and understanding the use of them in business functions is the point of differentiation with other senior managers.

In contemporary world where information is all about human beings, what matters most is the accurate retrieval of information. Information retrieval has always been a human concern, which is why it is constantly undergoing many changes. One of the issues that information retrieval experts have always been thinking about is designing an efficient information retrieval system. Therefore, by identifying the key factors affecting the future of information retrieval, we can be more successful in designing such a system and have a greater share in the future of information retrieval. The present study resource review and cross-impact analysis methods, and MicMac software was used to analyze interactions and identify key factors. The results of the present study lead to the identification of 13 key factors: 1. Conversion of traditional libraries to digital, 2. Development and upgrading of search engines, 3. New content formats, 4. Intelligence of data collection methods, 5. Convergence media, 6. Increasing content production, 7. The new generation of the Web, 8. Automating information retrieval processes, 9. The emergence of hybrid resources, 10. Big data, 11. Cloud processing, 12. Increasing Internet resources, and 13. Use of artificial intelligence and natural language processing in effective information retrieval; on the future of information retrieval. Therefore, in the era of the fifth industrial revolution, it is necessary for information science specialists to be equipped with technological tool more than before.

In the current era, the growing growth of information technology and the need for organizations to survive and compete, has caused any organization that ignores this issue to decline and be eliminated from the competition cycle. Accordingly, the aim of the present study is to investigate the effect of strategic learning on strengthening information capital by analyzing the mediating role of organizational knowledge architecture. The present study is descriptive-correlational in terms of practical purpose and in terms of data collection method. The statistical population includes 600 knowledge-based companies located in Tehran Science and Technology Park. According to Morganchr('39')s table, 235 employees of these companies were selected using haphazard random sampling method. The main tools of data collection, questionnaire and data analysis were also performed using structural equation modeling. The research findings show that strategic learning has a direct and significant effect on strengthening the information capital of knowledge-based companies and also indirectly affects the strengthening of information capital through the intermediate variable of organizational knowledge architecture. The results show that strategic learning is one of the important factors influencing the strengthening of information capital of knowledge-based companies, which affects the strengthening of information capital both directly and through organizational knowledge architecture. Also, the results of strategic learning topics, organizational knowledge architecture and information capital, bring theoretical innovations and help managers and stakeholders of knowledge-based companies in understanding and how to influence strategic learning to strengthen information capital by emphasizing organizational knowledge architecture.