IFO模型在数据库逻辑设计中的应用

反映信息系统的结构语义是数据库设计的主要目的之一。这一目的一般采用语义数据模型来达到。我们考察这一问题的逆问题:识别关系数据库模式中IFO结构。本文描述了将无语义的关系模式转换成IFO图的方法,同时给出了该方法的具体应用。     

汉语中"的"字短语右边界的识别

“的”字是现代汉语中的高频词．“的”字的作用主要是用来构成短语,我们称之为“的”字短语,它是现代汉语中最常见的短语．“的”字短语造成了汉语句子中丰富的层次和复杂的语义结构．汉语句子中很多的歧义结构也是由“的”字短语引起的．因此正确地分析“的”字短语的结构对于汉语句法分析和汉语理解是很重要的．要分析“的”字短语的语义结构必须首先识别出句子中的“的”字短语．当然,当“的”字以词的形式出现在句子中时,这个句子中就出现了一个“的”字短语．可是这个“的”字短语的右边界在那里呢？如何让计算机能够正确地识别出它的右边界呢？这个问题却迄今未见有文献涉及．该文对此进行了研究并提出了研究交提出了识别“的”字短语边界的算法．     

动词带宾语"被"字句的形成过程浅析

动词带宾语“被“字句是现代汉语中较为特殊的一类“被“字句式.它出现时期早于唐代,六朝以前这类现象为数尚少,唐代以后用得已经相当普遍了.     

A Framework of Semantic Information Representation in Distributed Environments

0 IntroductionOonft thhee gcloombaml uinnifcoartimoanti onentiwnofrraksst rhuacstu mread,et ahvea iglraebalte teox puasnesrisolarge number of autonomous data repositories ,however ,these repositories present different structures and semantics result fromthat distinct data sources may use different modeling methodsmaking very difficult to share and exchangeinformation.Recentlythere have been some works ,such as OntoBroker in Ref .[1] ansystemin Ref .[2] ,that focus on the umformrepresentation …     

Query through heterogeneous ontologies using association matrix

This paper introduces the definition and calculation of the association matrix between ontologies, h uses the association matrix to describe the relations between concepls ill different ontologics and uses concept vectors to represent queries; then computes the vectors with the association matrix in order to rewrite queries, This paper proposes a simple method of querying through heterogeneous Ontology using association matrix. This method is based on the correctness of approximate information filtering theory; and it is simple to be implemented and expected to run quite fast.     

Integrated semantic similarity model based on ontology

To solve the problem of the inadequacy of semantic processing in the intelligent question answering system. an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and informarion content is presented in this paper. With the help of interrelationship between concepts, the information content ofconcepts and the strength of the edges in the ontology network. we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user‘s question and answers in knowlegdge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is vety satisfied.     

A semantic retrieval method based on the fuzzy reasoning

This paper gives a semantic fuzzy retrieval method of multimedia object, discusses the principle of fuzzy semantic retrieval technique, presents a fuzzy reasoning mechanism based on the knowledge base, and designs the relevant reasoning algorithms. Researchful results have innovative significance. Foundation item: Supported by the Natural Science Foundation of Hubei Province(No. 200J157) Biography: Cao Jia-heng(1946-), male, Professor, research direction: multimedia software, artificial intelligence.     

语义结构复杂异常的语符"A and B"

"A and B"是最常见的词组形式,也是最简单的常用组词结构.对其作语法分析很容易,作正确又清晰的语义分析却不容易.     

日语被动句中助词“に”的关系语义辨析

在日语被动句中出现的助词“に”,不仅有表示被动句中施动者的关系语义,还有表示其它的关系语义,这样给翻译带来一定的难度。本文从“に”构成的词素中词的类别及谓语动词需要哪种补语入手,通过例句辨析被动句中助词“に”的种种关系语义,力求翻译上的正确性。     

Image Semantic Automatic Annotation by Relevance Feedback

A large semantic gap exists between content based index retrieval (CBIR) and high-level semantic, additional semantic information should be attached to the images, it refers in three respects including semantic representation model, semantic information building and semantic retrieval techniques. In this paper, we introduce an associated semantic network and an automatic semantic annotation system. In the system, a semantic network model is employed as the semantic representation model, it uses semantic keywords, linguistic ontology and low-level features in semantic similarity calculating. Through several times of users' relevance feedback, semantic network is enriched automatically. To speed up the growth of semantic network and get a balance annotation, semantic seeds and semantic loners are employed especially.