پژوهشنامه پردازش و مدیریت اطلاعات
سال سی و پنجم شماره 2 (پیاپی 100، زمستان 1398)

Reviewing the recently published and authentic texts in the field of Library & Information Science’s education indicates some fundamental problems in this pedagogic process. According to different factors dealing with the process such as professors, learners, educational department and learning resources, confronting the challenges is considered as complex issues. Therefore, in this research Soft Systems Methodolgy (SSM) was chosen to propose a comprehensive model to solve the mentioned problems. Soft Systems Methodolgy (SSM) is one of the Action Research’s methods. In view of research approach, SSM’s considered qualitative. Based on the Checkland seven proposed stages, problem situation was identified, and then it was expressed in the form of rich picture. Driving root definitions and CATWOE model were cleared to accomplish the conceptual model. Comparison of the conceptual model to real world, also proposing feasible and desired changes are the fifth and sixth stages of the reserach. Finally, taking action to improve the current situation in the field of Library & Information Science’s education finished the procedure.  Utilizing the seven steps of SSM, this resrarch draws the rich picture illustrating the process of Library & Information Science’s education and its issues dealing with the related ecosystem. Accordingly, the final model consisting of three ontologies (learners, professors, and educational content) was attained. To validate the semantic model, Cohen’s kappa coefficient was calculated. Pertaining to the high level of agreement coefficient, 9203/., the achieved conceptual model was approved. To utilize the comprehensive model, it was implemented by Protégé4.3 software and OWL2 language.  The obtained model, approved by high level of expert agreement, not only can be an appropriate solution for the problems involved in Library & Information Science’s education in Iran, but also can be considered as a pattern for future researches in designation and implementation of semantic model of education in the other disciplines. The findings indicate that the semantic model is a complex concept including different aspects and indices which can be easily implemented and noticed by policy-makers, designers and other players in designation and assessment of teaching-learning environments.

This study is to explore the cognitive components of therapists' information seeking behavior in the diagnosis and treatment of mental disorders. According to the high power of neuroscience tools in the analysis of immediate cognitive components, Quantitative Electroencephalography (QEEG) was used. Following, five of the therapists were selected by random sampling from a variety of researchers and entered in the research process. To gather data, some tools such as QEEG and Morae software were used. The findings showed that the brain waves of the therapists in the early stages of Kuhlthau's ISP revealed extensive cognitive activities. As the therapists moved on to the final stages of Kuhlthau's ISP, these cognitive activities diminished and the brain emotions overcame. Based on the findings, the best step for librarian intervention to guide and assist clinicians was the stage 3 of Kuhlthau’s ISP. Also, the findings indicated that the therapists’ information seeking behavior in the cognitive actions section was similar to the Kuhlthau's ISP Model. Finally, it can be concluded that based on modern neuroscience and information technology tools, one can provide a better understanding of the up-to-the-minute process of therapists' information seeking behavior, and due to smart and personalized systems , provide at the right moment, the appropriate information to the users. This evolution can be a major step in promoting information seeking behavior and advancing in the user information needs momently.

The compatibility of classification codes and indexing terms done from a codified thesaurus with words and phrases that are automatically extracted using machine indexing. In designing an outo-indexing system, the computer completely replaces humans. The purpose of this research was to identify and extracting keywords and the subject trends of articles in the field of information retrieval and the subject's specificity of the author of each article by using the text mining and categorizing (classifying) with the help of concurrence vocabularies.The method of this research is applied and based on the CRISP model of data mining and text mining algorithms are used. The research community has 313 articles has in the field of information retrieval indexed at Noormags database. After normalizing the text of with the Virastyar software, during the text mining of the articles with the 7.1 version of the RapidMiner software, the keywords are extracted by their weight and the data are categorized using two classical classification algorithms, namely, KNN and Naïve Bayse were analyzed. In this study, the computer automatically indexed the readable machine text by using the frequency of the words with the help of the text mining tools of RapidMiner software. For this purpose, we use N-gram operators and calculate the weight of the words according to tf-idf method, Terms and key concepts and subject and specialization of author of each article is extracted in the form of 16 categories. Finally, the superiority of the KNN model In the categorization of the core subjects of the papers, this study is proving to be 85% more accurate than the Naïve bayse model. Finding the results of calculating the accuracy of the models indicate the acceptable performance of the RapidMiner software in machine indexing of texts. Indexing texts using this method can help improve the results of information retrieval and prevent false dropping of information in databases.

The present research aimed to find out the usability of non-topical terms used in indexing of Islamic sciences resources in information retrieval process. The research population, was carried out with analytical survey method, was the users of the Islamic Sciences Information Management Database, designed by Institute of Islamic Information and Document Management, a sample of 50 researchers, familiar with the Islamic Sciences Information Management Database, were selected by purposive convenience sampling method. Data gathering tools used in this study were three questionnaires developed by the researchers. These questionnaires included first, questionnaire evaluated the end users’ experience of the database, the second one was an open-ended questionnaire to get feedback from users about the retrieval process and the third one was a close-ended questionnaire related to the research hypotheses. The questionnaires validity was determined by information retrieval and storage specialists, and their reliability was confirmed by Cronbach's alpha test. Inferential statistics was applied to test the research hypotheses. Research findings indicate that using non-topical terms for expanding search query in Islamic Sciences Information Management Database led to desirable results; however, developing non-topical terms based on suggestions from end users is necessary. Findings also showed that there was a proper equivalence relationship among non-topical terms. Findings of present research indicated that paying attention to non-topical terms and using equivalence relationship between them along with applying topical terms can lead to relevant and desired results in information search and retrieval.

The increasing growth of web usage has made the importance of search engines and their quality assessment more responsive to users' needs. One of the approaches to quality assessment is user satisfaction. The use of a search engine depends on user satisfaction, which is a measure of the quality of the search engine. Many search engines have been built in Iran, as well. Some of these engines are widely used to search for information on the web, and some are found in a database Iran scientific information database (GANJ) is one of the databases that have its search engine. It contains nearly 530,000 dissertations and thesis and is one of the key scientific databases in the country with many users. Therefore, assessing the satisfaction of users of this system with its broad audience is crucial. In this research, indicators based on the EQOAL model were developed to evaluate users' satisfaction with the GANJ System. Indices developed based on the Delphi method were screened and localized to assess users' satisfaction with GANJ System. User satisfaction was also assessed using an EQual questionnaire. The results of the data analysis of the 156 questionnaires collected show that usability, information, and service interaction are the significant determinants of users' satisfaction with the treasure system, respectively. In terms of usability, the ease of working with "GANJ" is a top priority. Likewise, the ease of interaction with the system and the apparent attractiveness of the system were introduced as the main factors.

The knowledge graph plays an important role in the Semantic Web and Natural Language Processing (NLP) tools. There are many knowledge bases in different languages, however lack of Farsi-specific knowledge base appears some defects in research and industrial applications. In this study, the most comprehensive knowledge base in Farsi language is presented, which consists of more than 500K of entities and 7 million relations, which is accessible open source. Data is supplied 3 sources: Farsi Wikipedia and its structured data such as infobox, Web tables, relation extraction module. According to the semantic web, RDF data model and OWL2 ontology employed to implement the Farsi Knowledge Graph (FKG). Resources and their relations are stored in triple format, therefor access to the knowledge graph is provided by a SPARQL endpoint. An ontology, retrieved from DBpedia ontology, was developed and improved Based on resources of Farsi Wikipedia. Also, more than 8000 templates and properties of Wikipedia were mapped to the ontology automatically and manually. Furthermore, a part of the ontology was mapped to the FarsNet, the Persian WordNet, for research purposes. In the graph, there are a large amount of information on a variety of topics including famous people, important places, organizations and companies, literary and art works, physiology, biology, events, species, astronomy, etc. According to the Linked data, most of entities in the FKG have been connected to DBpedia and Wikidata resources by owl:sameAs. In order to achieve high performance and flexible data model, a two-level architecture for storing data was designed to separate data from metadata. This design plays a key role in update operation and managing versions. For evaluation purposes, a small part of triples were randomly collected to build a test dataset for manually inspection. Experimental results demonstrate that more than 94% of triples were obtained correctly through the process of extraction, conversion, mapping, transformation and store. Future of internet according to the semantic web will be a complex and huge global knowledge base, therefor the FKG can play a significant role in defining and developing this emerging technology.

Today, due to the massive amount of information, NoSQL databases are used to compute complex data. These databases are used to store semi-structured and unstructured data for big data management. In this paper, fuzzy queries are executed by users on the information stored in the Neo4j graph database, which show results in defuzzy manner. To evaluate the efficiency of the proposed method, we considered a database of manufactured company, two price and quality fuzzy variable were considered and used to define and execute the fuzzy queries that the results can verify the performance of the proposed method. Additionally, a counselor (who has information about the data) defines fuzzy terms, membership functions, and fuzzy rules table. The most important process in this method is to find the center of gravity in order to defuzzify the final result. So, an algorithm has been implemented for this purpose by C # programming language. The greatest query time is due to find center of gravity. The evaluation results show that the query time increasing by using the proposed approach than the query time by using Cypher language, is acceptable due to the complexity of fuzzy concepts; therefore, the proposed solution will be suitable for using ambiguous and fuzzy queries in large databases.

Nowadays, one of the key issues in enterprise 2.0 is how to use the innovative knowledge of users and customers systematically and purposely, and also and benefit from the knowledge sharing into organizational processes. One effective approach is to make use of an open innovation approach, leading to improvement of knowledge flows into the organization. However, to date, specific knowledge management architecture for managing such knowledge flows based on the open innovation approach has not been studied yet at enterprise 2.0. In this research a new campestral architecture is introduced for knowledge management in enterprise 2.0 that is based on open innovation approach. For this purpose, grounded theory, a qualitative inductive research has been used with emphasis on content analysis of documents and related research studies. The results of content analysis show that the key intersection between knowledge management sharing, enterprise 2.0 and open innovation is knowledge flow management. Therefore, using the characteristics of in-flow and out-flow of knowledge, a conceptual architecture composed of five different layers with seventeen components is proposed. These layers are the logic of business, interface, process and flow of knowledge, infrastructure, and context. In the proposed architecture, each layer has some components that are connected to each other and some of them are connected to the components of other layers as well. The proposed architecture is also compared with two similar architectures in this context as well.

Strategic planning of information technology is one of the concerns for new organizations. This paper is the result of a research project in the field of IT strategic planning in Kermanshah Electric Power Distribution Company. Based on the common view of all strategic planning models, one of the important steps is to determine the status of the organization in the area in question. Given the notification of upstream documents, such as the Tavanir Information Technology perspective document and the e-government document that are necessary to implement, as well as governance approach in this documents, this approach in the service sector, has used as Kermanshah Electric Power Distribution Company vision. Based on the vision, Capability Maturity Model Integration and Information Technology Infrastructure Library best practices have been used to determine the current statues. To achieve the purpose of the article, four phases are considered: in first phase that is the same phase of recognition, business processes is recognized. The second phase is the matching of business processes with standard processes of Information Technology Infrastructure Library. For this purpose, initially, business processes were examined form the standpoint of information architecture and then the match was done by conducting specialized interview based on the process. In the next phase, organizational maturity is determined in two ways. In the first method, initially, descriptive phrases are determined by extracting the features of  ITIL best practices and delivered to process officials in the form of 27 questionnaires for 27 processes. In the second method that has the standard questionnaire, the questionnaire is completed for all of ITIL process. The results of examining the existing situation using best practices as well as standard processes of maturity model indicate the maturity level of the processes based on the percentage and provides the ability to compare the two methods. In addition, based on a comparative analysis of these two methods, the weaknesses and strengths of the organizational processes adapted to the processes of the Information Technology Infrastructure Library were determined, and some recommendations and suggestions for future research in this area were presented. The results show that service desk, event management, request fulfilment, configuration management, and contractor management processes as organizational strengths and change management, knowledge management, information security management, release and deployment management processes as organizational weaknesses issues can be investigated and were not regularly seen in other processes such as service level management, demand management, capacity management.

With the proliferation of the Internet and the rapid growth of electronic articles, text categorization has become one of the key and important tools for data organization and management. In the text categorization, a set of basic knowledge is provided to the system by learning from this set, the new input documents into one of the subject groups. In health literatures due to the wide variety of topics, preparing such a set of early education is a very time consuming and costly task. The purpose of this article is to present a hybrid model of learning (supervised and unsupervised) for the subject classification of health scientific products that performs the classification operation without the need for an initial labeled set. To extract the thematic model of health science texts from 2009 to 2019 at PubMed database, data mining and text mining were performed using machine learning. Based on Latent Dirichlet Allocation model, the data were analyzed and then the Support Vector Machine was used to classify the texts. In the findings of this study, model was introduced in three main steps. In the first step, the necessary preprocessing was done on the dataset due to the elimination of unnecessary and unnecessary words from the dataset and increasing the accuracy of the proposed model. In the second step, the themes in the texts were extracted using the Latent Dirichlet Allocation method, and as a basic training set in step 3, the data were backed up by the Support Vector Machine algorithm and the classifier learning was performed with the help of these topics. Finally, with the help of the categorization, the subject of each document was identified. The results showed that the proposed model can build a better classification by combining unsupervised clustering properties and prior knowledge of the samples. Clustering on labeled samples with a specific similarity criterion merges related texts with prior knowledge, then the learning algorithm teaches classification by supervisory method. Combining categorization and clustering can increase the accuracy of categorization of health texts.