Design Of Ontology-based Animation Material Retrieval System And Research On Retrieval Model

Currently, the text-based retrieval technology and content-based retrieval technology are widely used in the field of retrieval of multimedia resources. However, the text-based search technology uses manual annotation which leads to the low degree of automation. The quality of annotating varies from person to person, this technology is not only time-consuming and laborious but also difficult to ensure semantic consistency of the result of annotating. Although the content-based retrieval technology can improve automation degree of retrieval at a certain extent, the description of contents only directs to the physical characteristics of multimedia data, and doesn't descirbe the high-level semantic information of resources. On the other hand, traditional information retrieval models only match from the literal meaning of words, short of the capacity to deal with semantic information, and having defects such as false information retrieval and omission of undetected information. Ontology-based information retrieval and latent semantic indexing (LSI) method are two ways to realize semantic retrieval and can improve retrieval recall ratio and precision ratio.Firstly, this paper set out the ontological theory, and analyze implementation mechanism of Ontology-based information retrieval as well as the existed information retrieval techniques and models. Secondly, we propose a latent semantic indexing model based on ontology, giving the process of the combination between ontology and LSI in detail, building query expansion algorithm based on ontology to realize the semantic expansion of user query which further improves the accuracy and effectiveness of semantic retrieval. Finally, through the survey of animation production flow, we analyze and design an ontology-based retrieval system for animation materials, proposing the framework and implementation method of the semantic retrieval system, completing design and implementation of the ontology server , request pre-processor and request re-constructor in addition to system analysis and system design, and indicating through the analysis of experimental results that semantic retrieval can effectively improve retrieval performance.