Khaled Shaalan

Knowledge Management (KM) and knowledge sharing (KS) have become crucial tasks for both Middle Managers and Top Managers of many organizations, especially those who highly rely on the type of knowledge which is difficult to transfer from one person to another, or what is called “Tacit Knowledge”. The objective of this case study is to review the practical knowledge transfer techniques, the main motivators and demotivators of a tacit knowledge transfer process, and the measures that can be taken to overcome the demotivation factors. Siemens is chosen for this case study because it has been recently rated as one of the top Knowledge-Management-driven companies. We present here our own observations on some of the KM practices that Siemens strives to implement in its branches in the Middle East countries and the barriers which are challenging such practices.

In this study the researcher is focusing on the performance of the project. During the life cycle of projects managers are striving to forecast the future performance of the project according to the historical patterns of performance. Irregular performance and randomness are representing chaotic performance periods in project’s life. The study proposes five propositions. The first proposition is that Manager have different conscious perceptions about accepted levels of order and chaos in their project performance. The second proposition is that Knowledge Creation Process has different characteristics in chaotic perceived projects and order perceived projects. The third proposition is that managers have different prioritization of knowledge assets from which they create new knowledge for chaotic performance projects. The fourth proposition is that Sources of knowledge creation differ according to the context of project performance. The fifth proposition is that Assets used for knowledge creation during chaotic performance periods differ according to managers’ tendency to innovate. This study is a qualitative analysis of secondary data that comes from literature review and primary data that comes from interviews with experts and project managers. The researcher investigated four styles of knowledge creation during chaotic performance periods; experiential knowledge creation style, innovative knowledge creation style, precautions and risk minimization knowledge creation style, and specialty based knowledge creation style. Conclusions and recommendations are provided to project managers on which knowledge assets are leading to innovative knowledge creation.

The advent of technology has changed the way of government to communicate with its stakeholders by incorporating the notion of e-government based on web-portal. These web-portals whereas identified successful in communicating with the stakeholders also played a vital role in sharing and managing knowledge. Based on this notion, this article is concerned with the role of e-government portal in sharing knowledge and the way through which this portal increases the efficiency of entire public sector. In the same instance, the K-ACT model of internet portal is analyzed to identify the features that make a web-portal best and perfect. In this article, we conducted a literature review and focused our attention on analyzing the e-government portals of four countries i.e. China, Hong Kong, Beijing and Turkish Municipalities. For the analysis and comparison purposes, the checklist of K-ACT model is used. The findings revealed that the e-government portal of these countries is not fully matched with the features as described in K-ACT, which ultimately affect the knowledge management and sharing practices in the region. Therefore, recommendations are made to improve the existing portal for knowledge sharing so that citizens can take more knowledge benefits from the e-government portals.

The increasing trends of technological immersion into the businesses lead towards many form of indecisions. The business world required certain planning to overcome the problems and recovery from many disasters accommodating to Information Technology. Businesses required running their tasks with the inclusion of the knowledge and IT within the organization. The respective report is concerned to approach the Information Technology meditation to examine the business continuity in the certain IT disasters. A mixed methodology approach for collection of the data is used in the study in which quantitative and qualitative methods are considered. The outcomes of the study will assist the IT-enabled organization to plan their business activities to abide the misery from disasters.

Mobile learning (M-learning) has become an important educational technology component in higher education. M-learning makes it possible for students to learn, collaborate, and share ideas among each other with the aid of internet and technology development. However, M-learning acceptance by learners and educators is critical to the employments of M-learning systems. Attitudes towards M-learning technology is an important factor that helps in determining whether or not learners and educators are ready to use M-learning. Such attitudes will serve to identify strengths and weaknesses and facilitate the development of the technology infrastructure. This paper aims at exploring students and educators' attitudes towards the use of M-learning in higher educational universities within Oman and UAE; two neighboring countries in the Arab Gulf region. To serve this purpose, two survey questionnaires were conducted: one for students and another for educators. The participants of this study are 383 students and 54 instructors from five universities. Different factors have been examined to test where there is a significant difference among students and educators' attitudes towards the use of M-learning, such as gender, age, country, level of study, smartphone ownership, major in terms of students and age, country, academic rank, academic experience and smartphone ownership in terms of educators. Findings revealed significant differences among the students’ attitudes towards M-learning with regard to their smartphone ownership, country and age. Furthermore, results indicated that M-learning can be one of the promising pedagogical technologies to be employed in the higher educational environments within the Arab Gulf countries.

Keyword identification is an important task that provides useful information for NLP applications including: document retrieval, clustering, and categorization, among others. State-of-the-art methods rely on local features of words (e.g. lexical, syntactic, and presentation features) to assess their candidacy as keywords. In this paper, we propose a novel keyword identification method that relies on representation of text abstracts as word graphs. The significance of the proposed method stems from a flexible data representation that expands the context of words to span multiple sentences and thus can enable capturing
of important non-local graph topological features. Specifically, graphlets (small subgraph patterns) were efficiently extracted and scored to reflect the statistical dependency between these graphlet patterns and words labeled as keywords. Experimental results demonstrate the capability of the graphlet patterns in a keyword identification task when applied to MEDLINE, a standard research abstract dataset.

The study tries to find out the most important features in building games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the availability of (Acquisition, Retention and Monetization) elements on every successful game and if any is missed, will lead to the failure of the game.

The tasks that falls under the errands that takes after Natural Language Processing approaches includes Named Entity Recognition, Information Retrieval, Machine Translation, and so on. Wherein Sentiment Analysis utilizes Natural Language Processing as one of the way to locate the subjective content showing negative, positive or impartial (neutral) extremity (polarity). Due to the expanded utilization of online networking sites like Facebook, Instagram, Twitter, Sentiment Analysis has increased colossal statures. Examination of sentiments helps organizations, government and other association to extemporize their items and administration in view of the audits or remarks. This paper introduces an Innovative methodology that investigates the part of lexicalization for Arabic Sentiment examination. The system was put in place with two principles rules– “equivalent to” and “within the text” rules. The outcomes subsequently accomplished with these rules methodology gave 89.6 % accuracy when tried on baseline dataset, and 50.1 % exactness on OCA, the second dataset. A further examination shows 19.5 % in system1 increase in accuracy when compared with baseline dataset.

Most Named Entity Recognition (NER) systems follow either a rule-based approach or machine learning approach. In this paper, we introduce out attempt at developing a hybrid NER system, which combines the rule-based approach with a machine learning approach in order to obtain the advantages of both approaches and
overcomes their problems. The system is able to recognize eight types of named entities including Location,
Person, Organization, Date, Time, Price, Measurement and Percent. Experimental results on ANERcorp dataset indicated that our hybrid approach outperforms the rule-based approach and the machine learning approach when
they are processed separately. Moreover, our hybrid approach outperforms the state-of-the-art of Arabic NER.

There is a growing interest in Arabic web content worldwide due to its importance for culture, religion, and economics. In the literature, researches that address searching Arabic web content using semantic web technology are still insufficient compared to Arabic’s actual importance as a language. In this research, we propose an Arabic semantic search approach that is applied on Arabic web content. This approach is based on the Vector Space Model (VSM). It uses the Universal WordNet ontology to build a rich concept-space index instead of the traditional term-space index. The proposed index is used for enhancing the capability of the semantic-based VSM. Moreover, the approach introduces a new incidence measurement to calculate the semantic significance degree of the document's concepts which is more suitable than the traditional term frequency measure. Furthermore, a novel method for calculating the semantic weight of the concept is introduced in order to determine the semantic similarity of two vectors. As a proof of concept, a system is applied on a full dump of the Arabic Wikipedia. The experimental results in terms of Precision, Recall and F-measure have showed improvement in performance from 77%, 56%, and 63% to 71%, 96%, and 81%, respectively.

A Treebank is a linguistic resource that is composed of a large collection of manually annotated and verified syntactically analyzed sentences. Statistical Natural Language Processing (NLP) approaches have been successful in using these annotations for developing basic NLP tasks such as tokenization, diacritization, part-of-speech tagging, parsing, among others. In this paper, we address the problem of exploiting treebank resources for statistical parsing of Modern Standard Arabic (MSA) sentences. Statistical parsing is significant for NLP tasks that use parsed text as an input such as Information Retrieval, and Machine Translation. We conducted an experiment on 2000 sentences from the Pen Arabic Treebank (PATB) and the parsing performance obtained in terms of Precision, Recall, and F-measure was 82.4%, 86.6%, 84.4%, respectively.

Opinion Mining is a raising research field of interest, with its different applications derived by market needs
to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.

Due to very fast growth of information in the last few decades, getting precise information in real time is becoming increasingly difficult. Search engines such as Google and Yahoo are helping in finding the information but the information provided by them are in the form of documents which consumes a lot of time of the user. Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. This saves a lot of time for the user. There has been a lot of research in the field of English and some European language Question Answering Systems. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. Question classification is a very important module of Question Answering Systems. In this paper, we are presenting a method to accurately classify the Arabic questions in order to retrieve precise answers. The proposed method gives promising results.

Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. However, one serious concern with the Question Answering system is that despite having answers of the questions in the knowledge base, they are not able to retrieve the answer due to mismatch between the words used by users and content creators. There has been a
lot of research in the field of English and some European language Question Answering Systems to handle this issue. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. In this paper, we are
presenting a method to add semantically equivalent keywords in the questions by using semantic resources. The experiments suggest that the proposed research can deliver highly accurate
answers for Arabic questions.

To institute the characteristics of an effective Information Sharing for sustainability, the study mined the different aspects of information sharing under different statuses. The developed understandings became the provision from which the characteristics of effective Information Sharing were drawn. Using Mixed approach Phenomenology as a method of research, the lived experiences and information sharing approaches used into the organization served as pragmatic data which were reflected upon until the devising of understandings about impact on performance of the organizations. The result for study revealed that Information Sharing abilities are distinctive and are practiced by destitution. These practices and styles standout as good instructors, goal-oriented, support society and are obedient to their seniors. Thus the reader will have a wide knowledge about information sharing itself and the theories and thoughts behind it, which covers the different definitions and the aims of information sharing.

In the last few years, the way we learn has been significantly changed from traditional classrooms that depend on printed papers into e-learning relying on electronic teaching material. Contemporary educational technologies attempt to facilitate the delivery of learning from instructors to students in a more flexible and comfortable way. Mobile learning (M-learning) is one of such pervasive technologies that has been evolved rapidly to deliver e-learning using personal electronic devices without posing any restrictions on time and location. Literature that sheds light on using M-learning in various institutions of learning is beginning to emerge. The work in this paper demonstrates the state of the art of the M-learning. It discusses learners’ and educators’ attitudes towards the use and adoption of M-learning. Advantages and disadvantages of M-learning were also presented. The integration and implementation of M-learning with other technological resources has been described. Factors affecting the students’ and faculty members’ attitudes towards the use of M-learning have been demonstrated. Moreover, the new trends and challenges, which are evolved while conducting this survey, are explained.

Arabic language is strongly structured and considered as one of the most highly inflected and
derivational languages. Learning Arabic morphology is a basic step for language learners to
develop language skills such as listening, speaking, reading, and writing. Arabic morphology
is non-concatenative and provides the ability to attach a large number of affixes to each
root or stem that makes combinatorial increment of possible inflected words. As such, Arabic
lexical (morphological and phonological) rules may be confusing for second language learners.
Our study indicates that research and development endeavors on spelling, and checking of
grammatical errors does not provide adequate interpretations to second language learners’
errors. In this paper we address issues related to error diagnosis and feedback for second
language learners of Arabic verbs and how they impact the development of a web-based
intelligent language tutoring system. The major aim is to develop an Arabic intelligent
language tutoring system that solves these issues and helps second language learners to
improve their linguistic knowledge. Learners are encouraged to produce input freely in
various situations and contexts, and are guided to recognize by themselves the erroneous
functions of their misused expressions. Moreover, we proposed a framework that allows
for the individualization of the learning process and provides the intelligent feedback that
conforms to the learner’s expertise for each class of error. Error diagnosis is not possible with
current Arabic morphological analyzers. So constraint relaxation and edit distance techniques
are successfully employed to provide error-specific diagnosis and adaptive feedback to learners.
We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic
weak verbs formed using complex morphological rules. As a proof of concept, we have
implemented the components that diagnose learner’s errors and generate feedback which
have been effectively evaluated against test data acquired from real teaching environment.
The experimental results were satisfactory, and the performance achieved was 74.34 percent
in terms of recall rate.

In the last few years, the way we learn has been shifted dramatically from traditional classrooms depending
on printed papers into E-learning depending on digital pages. Mobile learning (M-learning) is a recent technology that has been developed rapidly to deliver E-learning using personal mobile devices without posing any restrictions on time and location. In this work, we investigate students and faculty members’ attitudes towards the use of M-learning in higher educational institutions within two countries in the Gulf Region (Oman & UAE). Two questionnaire surveys have been conducted: one for students and another for faculty members. In these surveys, 383 students and 54 instructors have taken part within the study. An independent sample t-test was performed to examine whether there exist a significant difference among the students’ attitudes and the faculty members’ attitudes towards the use of M-learning with regard to gender and country. Results indicated that students in the UAE were more positive towards the use of M-learning than those in Oman. Moreover, results revealed that 99% of the students own mobile devices, in particular smartphones and tablets, while only 1% has not. Results of this study could help policy makers for better decision making in building the M-learning infrastructure in the higher educational institutions in general and specifically within the Arab Gulf region.

The main reason of adopting Semantic Web technology in information retrieval is to improve the retrieval performance. A semantic search-based system is characterized by locating web contents that are semantically related to the query's concepts rather than relying on the exact matching with keywords in queries. There is a growing interest in Arabic web content worldwide due to its importance for culture, political aspect, strategic location, and economics. Arabic is linguistically rich across all levels which makes the effective search of Arabic text a challenge. In the literature, researches that address searching the Arabic web content using semantic web technology are still insufficient compared to Arabic’s actual importance as a language. In this research, we propose an Arabic semantic search approach that is applied on Arabic web content. This approach is based on the Vector Space Model (VSM), which has proved its success and many researches have been focused on improving its traditional version. Our approach uses the Universal WordNet to build a rich concept-space index instead of the traditional term-space index. This index is used for enabling a Semantic VSM capabilities. Moreover, we introduced a new incidence measurement to calculate the semantic significance degree of the concept in a document which fits with our model rather than the traditional term frequency. Furthermore, for the purpose of determining the semantic similarity of two vectors, we introduced a new formula for calculating the semantic weight of the concept. Because documents are indexed by their topics and classified semantically, we were able to search Arabic documents effectively. The experimental results in terms of Precision, Recall and F-measure have showed improvement in performance from 77%, 56%, and 63% to 71%, 96%, and 81%, respectively.