现实世界的实体间往往存在着复杂的语义关系,而且实体间的关系往往是相互的。现有数据库无论是扩展了对象关系模型的Oracle,还是首次提出逆向关系定义的ODMG(object data management group),它们对现实世界中实体之间复杂语义关系及其...现实世界的实体间往往存在着复杂的语义关系,而且实体间的关系往往是相互的。现有数据库无论是扩展了对象关系模型的Oracle,还是首次提出逆向关系定义的ODMG(object data management group),它们对现实世界中实体之间复杂语义关系及其逆向关系的描述和处理依然存在着很大的不足。为了更好地表述和处理现实世界中实体间复杂的语义关系,提出了一种能够简易描述实体间复杂语义的新方法。介绍了该方法如何针对复杂语义关系建立模型和插入对象,指出了该方法相对于传统模型的优越性,设计实现了实体间逆向关系和目标对象类的自动创建和生成机制,分析了数据库一致性维护问题。比较了基于新方法实现的DBMS(data base management system)和Oracle的性能,指出了基于新方法的DBMS存在的问题。展开更多
Global semantic structures of two large semantic networks, HowNet and WordNet, are analyzed. It is found that they are both complex networks with features of small-world and scale-free, but with special properties. Ex...Global semantic structures of two large semantic networks, HowNet and WordNet, are analyzed. It is found that they are both complex networks with features of small-world and scale-free, but with special properties. Exponents of power law degree distribution of these two networks are between 1.0 and 2. 0, different from most scale-free networks which have exponents near 3.0. Coefficients of degree correlation are lower than 0, similar to biological networks. The BA （Barabasi-Albert） model and other similar models cannot explain their dynamics. Relations between clustering coefficient and node degree obey scaling law, which suggests that there exist self-similar hierarchical structures in networks. The results suggest that structures of semantic networks are influenced by the ways we learn semantic knowledge such as aggregation and metaphor.展开更多
文摘现实世界的实体间往往存在着复杂的语义关系,而且实体间的关系往往是相互的。现有数据库无论是扩展了对象关系模型的Oracle,还是首次提出逆向关系定义的ODMG(object data management group),它们对现实世界中实体之间复杂语义关系及其逆向关系的描述和处理依然存在着很大的不足。为了更好地表述和处理现实世界中实体间复杂的语义关系,提出了一种能够简易描述实体间复杂语义的新方法。介绍了该方法如何针对复杂语义关系建立模型和插入对象,指出了该方法相对于传统模型的优越性,设计实现了实体间逆向关系和目标对象类的自动创建和生成机制,分析了数据库一致性维护问题。比较了基于新方法实现的DBMS(data base management system)和Oracle的性能,指出了基于新方法的DBMS存在的问题。
基金The National Natural Science Foundation of China(No.60275016).
文摘Global semantic structures of two large semantic networks, HowNet and WordNet, are analyzed. It is found that they are both complex networks with features of small-world and scale-free, but with special properties. Exponents of power law degree distribution of these two networks are between 1.0 and 2. 0, different from most scale-free networks which have exponents near 3.0. Coefficients of degree correlation are lower than 0, similar to biological networks. The BA （Barabasi-Albert） model and other similar models cannot explain their dynamics. Relations between clustering coefficient and node degree obey scaling law, which suggests that there exist self-similar hierarchical structures in networks. The results suggest that structures of semantic networks are influenced by the ways we learn semantic knowledge such as aggregation and metaphor.
