This paper presents a new method based on ontology formarion and fuzzy recognition of digital pictures. Ontology creation and doormat indexing are well-kown bottlenecks for integrating semantic services and for the Se...This paper presents a new method based on ontology formarion and fuzzy recognition of digital pictures. Ontology creation and doormat indexing are well-kown bottlenecks for integrating semantic services and for the Semantic Web, and thus the new method will be able to make automatic creation of the fish geometric ontology and automatic indexing to existing Semantic Web. Fuzzy set and fuzzy recognition are used to decide wheter a new fish picture belongs to an existing training set, here with the carp as an example. Training ing samples are used to set up fuzzy set and membership functions. The existing way of fish ontology formation can be integrated with the new method and the existing work for fish web can be used.展开更多
Since the publication of the indexation documents and queries are represented by keywords from their content. The use of words to represent the document content and query generates several problems, the ambiguity of w...Since the publication of the indexation documents and queries are represented by keywords from their content. The use of words to represent the document content and query generates several problems, the ambiguity of words and their disparity. The semantic indexing is as a solution that answers these problems. The goal context where the ambiguity is present, the semantic indexing is meant is to index by the meaning of words rather than words. In a to improve the performance of IRS （Information Retrieval System）. In this sense we will soon overcome the problems of traditional indexing approaches. What we propose is a new approach that will allow semantically indexing algorithms courses written in French language, based on a new application ontology. The aim of our approach is to adjust a semantic annotation tool with the reference ontology. The semantic annotation tool we generate an index that will be used in e-Learning as needed （question answering performance on the field. systems, information retrieval systems ） while improving展开更多
基金supported by the Independent Innovation Foundation of Shandong University(No.2009JC004)the Natural Science Foundation of Shandong Province(No.Y2007G31)
文摘This paper presents a new method based on ontology formarion and fuzzy recognition of digital pictures. Ontology creation and doormat indexing are well-kown bottlenecks for integrating semantic services and for the Semantic Web, and thus the new method will be able to make automatic creation of the fish geometric ontology and automatic indexing to existing Semantic Web. Fuzzy set and fuzzy recognition are used to decide wheter a new fish picture belongs to an existing training set, here with the carp as an example. Training ing samples are used to set up fuzzy set and membership functions. The existing way of fish ontology formation can be integrated with the new method and the existing work for fish web can be used.
文摘Since the publication of the indexation documents and queries are represented by keywords from their content. The use of words to represent the document content and query generates several problems, the ambiguity of words and their disparity. The semantic indexing is as a solution that answers these problems. The goal context where the ambiguity is present, the semantic indexing is meant is to index by the meaning of words rather than words. In a to improve the performance of IRS （Information Retrieval System）. In this sense we will soon overcome the problems of traditional indexing approaches. What we propose is a new approach that will allow semantically indexing algorithms courses written in French language, based on a new application ontology. The aim of our approach is to adjust a semantic annotation tool with the reference ontology. The semantic annotation tool we generate an index that will be used in e-Learning as needed （question answering performance on the field. systems, information retrieval systems ） while improving
