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Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.
Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.
As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.
Yapılan çalışma, yapay zekâ teknolojilerinin bilgi yönetimi işlevlerini etkileyebilecek yeniliklerini ortaya çıkarmayı amaçlamaktadır. Bilgi yönetimi ve yapay zekâ süreçleri üzerine giderek artan sayıda çalışmalar olmakla beraber, yapay zekânın BY ile uyumlandırılmasını sistematik ve yapılandırılmış olarak inceleyen Türkçe bir çalışmaya ihtiyaç olduğu değerlendirilmektedir. Bu kapsamda yapay zekânın bilgi yönetimi alanında yeniliklerini, süreçlerdeki rolünü, benimsenmesinin avantajlarını ve etkili kullanıma olanak tanıyacak faktörleri ortaya çıkarmayı amaçlamaktadır. Konu ile ilgili alanların başlıklarını ortaya koyarak incelemek amacıyla sistematik yazın araştırması yöntemi benimsenmiştir. İncelemeye başlarken zaman aralığı, veri tabanı seçimi yapılmış ve belirtilen sınırlar içerisinde makale seçimi ve sınıflandırılması gerçekleştirilmiştir. Bu bağlamda 1990 ile 2022 yılları arasında “Web of Science” ve “Scopus” veri tabanlarında yayınlanmış 84 adet makale belirlenmiştir. Bulgulara göre yapay zekânın bilgi yönetiminde benimsendiği ve daha etkili bir hale gelmesine yönelik bir kuvvet çarpanı olduğu görülmüştür. Çalışmanın sistematik bir yazın incelemesi olması nedeniyle alanda araştırmaya yönelik faydalı bilgiler içerdiği değerlendirilmektedir.
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