◇◇新语丝(www.xys.org)(xys5.dxiong.com)(www.xinyusi.info)(xys2.dropin.org)◇◇   复旦大学计算机学院臧斌宇教授拷贝抄袭他人论文   尊敬的方先生,   最近本人在阅读文献时发现复旦大学臧斌宇教授的论文存在大量拷贝的情况, 现将臧斌宇的其中一篇论文和被拷贝的两篇论文信息发给您。以资参考!   臧斌宇论文: Binyu Zang , Yinsheng Li , Wei Xie , Zhuangjian Chen , Chen-Fang Tsai ,Christopher Laing,An ontological engineering approach for automating inspection and quarantine at airports, Journal of Computer and System Sciences, Elsevier 74 (2008) 196–210   原文1: Jingshan Huang, Jiangbo Dang, Michael N. Huhns, Yongzhen Shao, "Ontology Alignment as a Basis for Mobile Service Integration and Invocation," International Journal of Pervasive Computing and Communications, vol. 3, no. 2, pp. 138-158, Emerald, 2007   原文2: Jingshan Huang, Jiangbo Dang, and Michael N. Huhns, “Reconciling Ontologies for Coordination among E-business Agents,” Proc. AAMAS Workshop on Business Agents and the Semantic Web, Hakodate, Japan, May 2006   臧斌宇论文:   GLUE [30] is schema-based and introduces well-founded notions of semantic similarity, applies multiple machine learning strategies, and can find not only one-to-one mappings, but also complex mappings. However, it depends heavily on the availability of instance data. Therefore, it is not practical for cases where there is an insignificant number of instances or no instance at all. …PROMPT [13] is a tool making use of linguistic similarity matches between concepts for initiating the merging or alignment process, and then use the underlying ontological structures of the Protege-2000 environment to inform a set of heuristics for identifying further matches between the ontologies. PROMPT has a good performance in terms of precision and recall. However, user intervention is required, which is not always available in real world application. COMA [14] provides an extensible library of matching algorithms, a framework for combining results, and evaluation platform as well. According to their evaluation, COMA is performing well in terms of precision, recall and overall measures. Although being a composite schema-matching tool, COMA does not integrate reasoning and machine learning techniques. Similarity Flooding [12] utilizes a hybrid matching technique based on the idea that similarity spreading from similar nodes to the adjacent neighbours. Before a fix-point is reached, alignments between nodes are refined iteratively. This algorithm only considers the simple linguistic similarity between node names, leaving behind the node property and inter-node relationship. Cupid [15] combines linguistic and structural schema matching techniques, as well as the help of a precompiled dictionary.   But it can only work with a tree-structured ontology instead of a more general graph-structured one. As a result, there are many limitations to its application, because a tree cannot represent multiple-inheritance, an important characteristic in ontologies. S-Match [11] is a modular system into which individual components can be plugged and unplugged. The core of the system is the computation of relations. Five possible relations are defined between nodes: equivalence, more general, less general, mismatch, and overlapping. Giunchiglia et al. claim that S-Match outperforms Cupid, COMA, and SF in measurements of precision, recall, overall, and F-measure. However, like Cupid, S-Match uses a tree-structured ontology.   抄袭原文1:   GLUE introduces well-founded notions of semantic similarity, applies multiple machine learning strategies, and can find not only one-to-one mappings, but also complex mappings. However, it depends heavily on the availability of instance data. Therefore, it is not practical for cases where there is an insignificant number of instances or no instances at all….PROMPT (Noy and Musen, 2001) is a tool making use of linguistic similarity matches between concepts for initiating the merging or alignment process, and then use the underlying ontological structures of the Prote′ge′-2000 environment to inform a set of heuristics for identifying further matches between the ontologies. PROMPT has a good performance in terms of precision and recall. However, user intervention is required, which is not always available in real world applications. COMA (Do et al., 2002) provides an extensible library of matching algorithms, a framework for combining results, and evaluation platform as well. According to their evaluation, COMA is performing well in terms of precision, recall, and overall measures. Although being a composite schema matching tool, COMA does not integrate reasoning and machine learning techniques. Similarity Flooding (Melnik et al., 2002) utilizes a hybrid matching technique based on the idea that similarity spreading from similar nodes to the adjacent neighbors. Before a fix-point is reached, alignments between nodes are refined iteratively. This algorithm only considers the simple linguistic similarity between node names, leaving behind the node property and inter-node relationship. Cupid (Madhavan et al., 2001) combines linguistic and structural schema matching techniques, as well as the help of a precompiled dictionary. But it can only work with a tree-structured ontology instead of a more general graph-structured one. As a result, there are many limitations to its application, because a tree cannot represent multipleinheritance, an important characteristic in ontologies. S-Match (Giunchiglia et al., 2004) is a modular system into which individual components can be plugged and unplugged. The core of the system is the computation of relations. Five possible relations are defined between nodes: equivalence, more general, less general, mismatch, and overlapping. Giunchiglia et al. claim that S-Match outperforms Cupid, COMA, and Similarity Flooding in measurements of precision, recall, overall, and F-measure. However, like Cupid, S-Match uses a tree-structured ontology.   臧斌宇论文:   An ontology-based information retrieval model was presented for Semantic Web in the literature [7]. The authors generate ontology through translating and integrating domain ontologies. The terms defined in ontology are used as metadata to mark up the Web content; these semantic mark-ups are semantic index terms for information retrieval. The equivalent classes of semantic index terms are obtained by using description logic reasoner. They claim that the logical views of documents and user information needs, generated in terms of the equivalent classes of semantic index terms, can represent documents and user information needs well, so the performance of information retrieval can be improved effectively when suitable ranking function is chosen.   抄袭原文2:   An ontology-based information retrieval model for the Semantic Web is presented in [13]. The authors generate an ontology through translating and integrating domain ontologies. The terms defined in the ontology are used as metadata to markup the Web's content; these semantic markups are semantic index terms for information retrieval. The equivalent classes of semantic index terms are obtained by using description logic reasoner. It is claimed that the logical views of documents and user information needs, generated in terms of the equivalent classes of semantic index terms, can represent documents and user information needs well, so the performance of information retrieval can be improved effectively when suitable ranking function is chosen.   臧斌宇论文:   Tijerino et al. introduce an approach (TANGO) to generate ontologies based on table analysis [9]. TANGO aims to understand a table’s structure and conceptual content; discover the constraints that hold between concepts extracted from the table; match the recognized concepts with ones from a more general specification of related concepts; and merge the resulting structure with other similar knowledge representations. The authors claim that TANGO is a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.   抄袭原文2:   Tijerino et al. introduce an approach (TANGO) to generate ontologies based on table analysis [14]. TANGO aims to understand a table’s structure and conceptual content; discover the constraints that hold between concepts extracted from the table; match the recognized concepts with ones from a more general specification of related concepts; and merge the resulting structure with other similar knowledge representations. The authors claim that TANGO is a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology. (XYS20101223) ◇◇新语丝(www.xys.org)(xys5.dxiong.com)(www.xinyusi.info)(xys2.dropin.org)◇◇