In the past years, there has been increasing concern on ontology for its ability to explain data semantics in the usual manner independent of the data source characteristics, providing a schema that allows interchanging data between heterogeneous information systems and users. The ontology development in some areas is not expected due to a large amount of information, particularly in history, leading its semantic impossible. Several works have been designed to improve the technological aspects of ontology, such as the representation of language and inference mechanisms, and less attention has been paid to practical results development of application methods. This paper presents a discussion on the experience and processes during ontology building in history: historical documents retrieval based on the event.

Abstract-This article presents an ontology-based approach to designing and developing new representation IR system instead of conventional keyword-based approach. Such representation improves the precision and recall of document retrieval. Experiments carried out on the ontology-based approach and keyword-based approach demonstrates the effectiveness of the proposed approach.

Searching and retrieving documents from large historical archives prove to be challenging for the information retrieval (IR) field as historians typically employ their knowledge, experience, and intuition. There are several works done on the application of IR in historical documents. As such, the conventional IR model is mostly used a simple Bag-of-Word (BOW) approach and usually unable to support precise document retrieval for the domain of history. We proposed an ontology-based approach to semantically index and ranked rich historical documents. The historical documents relating to the Vietnam War were chosen for this study. Several existing ontologies have been reviewed to identify the most suitable concepts and properties which contain rich information pertaining to relevant entities such as an event, time, and people. The domain ontology was developed by utilizing the existing Simple News and Press (SNaP) ontology and extended with concepts related to the Vietnam War. The ontology was then semantically mapped with concepts found in a collection of 133 documents relating to the Vietnam war. In this paper, we also proposed a simple ontology-based weighting mechanism derived from the classic tf-idf scoring scheme. Finally, 20 SPARQL queries are implemented to do the evaluation. The evaluation shows that the proposed ontological-based approach achieved better results as compared to the base-line BM-25 probabilistic retrieval model in terms of precision and recall metrics. The use of the ontology-based approach in document retrieval can compete with the keyword-based approach.

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The COVID19 pandemic has caused a paradigm shift in teaching and learning (T&L) methods in higher learning institutions to online medium. The practice of online T&L increases the use of computer for a substantial amount of time to ensure that T&L activities are conducted successfully This is seen as causing a direct impact on the extensive use of energy and indirectly contribute to environmental pollution. To minimize the impact information technology on the environment, one of the solutions that receives much attention is green computing. Therefore, this study attempts to identify the knowledge and attitudes towards green computing among Malaysian students with survey method. A questionnaire with eleven items was developed to collect data, which was then distributed online among students in entire Malaysian higher learning institutions. A total of 192 respondents participated in this study. However, 182 questionnaires are used for analysis. This study reveals that most students are aware of the concept of green computing in general but do not properly understand how to apply green computing practices in daily life. In conclusion, several suggestions are proposed for developing an appropriate strategy to enhance green computing knowledge and practices among Malaysian students.