Data related to research assets are dispersed in various places and heterogeneous formats which may be structured or semi-structured in nature. This scattering of data causes a lot of repetition and inconsistencies. In this paper, we propose an Ontology-Based Research Asset Management Model (ORAM) useful for academic institutions. For this model, we created academic ontology. Data from different academic institutions is mapped to produce a single knowledge base. This mapping is performed by writing mapping rules. This unified knowledge base is integrated with web application to provide a single platform for decision-makers to retrieve information. The ORAM model is tested by developing a prototype with research assets data of various academic institutions available in structured and semi-structured formats. It is concluded that ontology plays a very important role in managing research assets effectively and efficiently.

Multiagent Systems are autonomous intelligent systems. In many academic institutions student admissions are performed after generating merit lists. Generation of merit lists is preceded by manual scrutiny of admission forms. This manual scrutiny is a knowledge-intensive, tedious and error-prone task. In this paper the design, implementation and testing of Multiagent System for Scrutiny of Admission Forms (MASAF) using