Study On E-Learning Service Discovery Algorithm Based On Ontology

With the development of network and educational technology, the educational pattern of modern distance education is changing gradually. Individualization, autonomous and coordination is becoming the goal of the tutor and the students under e-Learning environment. The variations of e-Learning educational goals include automatic push of learning resource, automatic generation of learning scheme and the discovery of learning software. So how to find the e-Learning service quickly and correctly is becoming the key point that influences education effect.The traditional e-Learning service discovery mechanism is based on UDDI. It is limited in keywords matchmaking, and its matchmaking mode is static. Although its searching rate is rather rapid. It cannot ensure finding the required e-Learning service which satisfies the student's needs very correctly, and its automation degree is not very high.Because Ontology owns the characteristic of share and reuse, and it has good concept structure, and it support logic inference. From 1990s, Ontology was applied on many fields such as knowledge engineering, information retrieval, information integration and knowledge management, and was become one of the important technologies in semantic web.Ontology applies on e-Learning service description, making service description information own the semantic information. The e-Learning service discovery model based on OWL-S can overcome some of the weakness of the discovery model based on UDDI, and can improve the precision and recall of e-Learning service discovery. But this method still has the some problems: its precision is not very high and efficiency is low. According to ontology, bipartite graph, rough sets theory and user satisfaction, the dissertation proposed 3 algorithms.The first algorithm is e-Learning service discovery algorithm based on bipartite graph which called eLSDA-BG. In this algorithm, the property sets of the required e-Learning service and the advertised e-Learning service are becoming vertex sets of a bipartite graph. The edges of this bipartite graph are the line between the matchmaking properties. The weight of the edges is property matchmaking degree. So the problem of e-Learning service matchmaking becomes the optimal complete matching of bipartite graph. Because Rough Sets Theory can find implicit knowledge, reveal the law from inaccuracy inconsistency imperfect information. I applied rough sets theory on e-Learning service discovery, and developed an e-Learning service discovery algorithm named eLSDA-RS. Algorithm eLSDA-RS combined Ontology technology and rough sets theory. There are 3 operations before e-Learning service matching. The first is to normalize the required e-Learning service. The second is to reduce uncorrelated attributes of advertised e-Learning service according to required e-Learning service. The third is to reduce dependency attributes of advertised e-Learning service according to required e-Learning service. The last 2 steps can decrease the number of advertise e-Learning service which should be match made, and improve the efficiency of e-Learning service discovery.Although many algorithms can improve the precision, recall and efficiency of e-Learning service discovery. The students are uncertain satisfied with the results. One of the reasons is that all of the algorithms regard properties matchmaking as the only indicatrix without considering the students' feelings. So I led-in a new factor -- User Satisfaction which is the user's feeling to the result of e-Learning service discovery. And I proposed a new e-Learning service discovery algorithm based user satisfaction called eLSDA-US. This algorithm allows the students to take part in the process of e-Learning service discovery, and also allows them to evaluate the result of service discovery. The students' evaluation in the form of User Satisfaction is fed back to the system. Adopting an amendatory function which takes the User Satisfaction as input, the system modifies the weights of each property of the advertise service, and then the total match degree of service discovery will up to best. I adopt two methods to encourage users to use the e-Learning service discovery system.At the end of the dissertation, a prototype system of CSCL is proposed.