Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the metho...Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the method of similarity computation is imperfect. And the computation quantity is high. To solve these problems, an ontology-mapping framework with a kind of hybrid architecture is put forward, with an improvement in the method of similarity computation. Different areas have different local ontologies. Two ontologies are taken as examples, to explain the specific mapping framework and improved method of similarity computation. These two ontologies are about classes and teachers in a university. The experimental results show that using this framework and improved method can increase the accuracy of computation to a certain extent. Otherwise, the quantity of computation can be decreased.展开更多
Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The m...Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The mapping discovery between two ontologies is achieved by computing the similarity between the concepts of two ontologies.Every concept consists of four features of concept name,property,instance and structure.First,the algorithms of calculating four individual similarities corresponding to the four features are given.Secondly,the similarity vectors consisting of four weighted individual similarities are built,and the weights are the linear function of harmony and reliability.The similarity vector is used to represent the similarity relation between two concepts which belong to different fuzzy ontolgoies.Lastly,Support Vector Machine(SVM) is used to get the mapping concept pairs by the similarity vectors.Experiment results are satisfactory.展开更多
Ontology mapping is a critical problem for integrating the heterogeneous information sources. It can identify the elements corresponding to each other. At present, there are many ontology mapping algorithms, but most ...Ontology mapping is a critical problem for integrating the heterogeneous information sources. It can identify the elements corresponding to each other. At present, there are many ontology mapping algorithms, but most of them are based on database schema. After analyzing the similarity and difference of ontology and schema, we propose a parsing graph-based algorithm for ontology mapping. The ontology parsing graph (OP-graph) extends the general concept of graph, encodes logic relationship, and semantic information which the ontology contains into vertices and edges of the graph. Thus, the problem of ontology mapping is translated into a problem of finding the optimal match between the two OP-graphs. With the definition of a universal measure for comparing the entities of two ontoiogies, we calculate the whole similarity between the two OP-graphs iteratively, until the optimal match is found. The results of experiments show that our algorithm is promising.展开更多
A new ontology-based question expansion (OBQE) method is proposed for question similarity calculation in a frequently asked question (FAQ) answering system. Traditional question similarity calculation methods use ...A new ontology-based question expansion (OBQE) method is proposed for question similarity calculation in a frequently asked question (FAQ) answering system. Traditional question similarity calculation methods use "word" to compose question vector, that the semantic relations between words are ignored. OBQE takes the relation as an important part. The process of the new system is:① to build two-layered domain ontology referring to WordNet and domain corpse;② to expand question trunks into domain cases;③ to use domain case composed vector to calculate question similarity. The experimental result shows that the performance of question similarity calculation with OBQE is being improved.展开更多
The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a ...The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.展开更多
O n to logy映射是实现异构O n to logy互操作的有效方法,目前的O n to logy映射主要采用句法方法,而很少采用语义方法。本文提出了一种基于语义的O n to logy映射方法,该方法考虑了O n to logy中的概念属性,采用概念名称相似性、概念...O n to logy映射是实现异构O n to logy互操作的有效方法,目前的O n to logy映射主要采用句法方法,而很少采用语义方法。本文提出了一种基于语义的O n to logy映射方法,该方法考虑了O n to logy中的概念属性,采用概念名称相似性、概念属性集合相似性、相关概念集合相似性等确定O n to logy之间的语义映射关系;采用语义半径提高了语义映射方法的灵活性。试验分析表明:该方法得到的映射的准确率和查全率在90%以上,准确率和查全率均优于S-M atch方法。该方法已在基于知识需求的主动式知识系统原型中实现,并在某研究所得到了应用。展开更多
There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which a...There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.展开更多
Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The ...Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.展开更多
The similarity between biomedical terms/concepts is a very important task for biomedical information extraction and knowledge discovery. The measures and tests are tools used to define how to measure the goodness of o...The similarity between biomedical terms/concepts is a very important task for biomedical information extraction and knowledge discovery. The measures and tests are tools used to define how to measure the goodness of ontology or its resources. The semantic similarity measuring techniques can be classified into three classes: first, measuring semantic similarity using ontology/ taxonomy;second, using training corpora and information content and third, combination between them. Some of the semantic similarity measures are based on the path length between the concept nodes as well as the depth of the LCS node in the ontology tree or hierarchy, and these measures assign high similarity when the two concepts are in the lower level of the hierarchy. However, most of the semantic similarity measures can be adopted to be used in health domain (Biomedical Domain). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two concepts within single ontology or multiple ontologies in UMLS Metathesaurus (MeSH, SNOMED-CT, ICD), and compare my results to human experts score by correlation coefficient.展开更多
基金the National Natural Science Foundation of China (70371052).
文摘Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the method of similarity computation is imperfect. And the computation quantity is high. To solve these problems, an ontology-mapping framework with a kind of hybrid architecture is put forward, with an improvement in the method of similarity computation. Different areas have different local ontologies. Two ontologies are taken as examples, to explain the specific mapping framework and improved method of similarity computation. These two ontologies are about classes and teachers in a university. The experimental results show that using this framework and improved method can increase the accuracy of computation to a certain extent. Otherwise, the quantity of computation can be decreased.
基金supported by the Natural Science Foundation of Beijing City under Grant No.4123094the Science and Technology Project of Beijing Municipal Commission of Education under Grants No.KM201110028020,No.KM201010028019+1 种基金the National Nature Science Foundation under Grants No.61100205,No.60873001,No.60863011,No.61175068the Fundamental Research Funds for the Central Universities under Grant No.2009RC0212
文摘Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present,a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed.The mapping discovery between two ontologies is achieved by computing the similarity between the concepts of two ontologies.Every concept consists of four features of concept name,property,instance and structure.First,the algorithms of calculating four individual similarities corresponding to the four features are given.Secondly,the similarity vectors consisting of four weighted individual similarities are built,and the weights are the linear function of harmony and reliability.The similarity vector is used to represent the similarity relation between two concepts which belong to different fuzzy ontolgoies.Lastly,Support Vector Machine(SVM) is used to get the mapping concept pairs by the similarity vectors.Experiment results are satisfactory.
基金National Natural Science Fundation of China (No.60374071)National Basic Research Program of China( No.2003CB316905)
文摘Ontology mapping is a critical problem for integrating the heterogeneous information sources. It can identify the elements corresponding to each other. At present, there are many ontology mapping algorithms, but most of them are based on database schema. After analyzing the similarity and difference of ontology and schema, we propose a parsing graph-based algorithm for ontology mapping. The ontology parsing graph (OP-graph) extends the general concept of graph, encodes logic relationship, and semantic information which the ontology contains into vertices and edges of the graph. Thus, the problem of ontology mapping is translated into a problem of finding the optimal match between the two OP-graphs. With the definition of a universal measure for comparing the entities of two ontoiogies, we calculate the whole similarity between the two OP-graphs iteratively, until the optimal match is found. The results of experiments show that our algorithm is promising.
文摘A new ontology-based question expansion (OBQE) method is proposed for question similarity calculation in a frequently asked question (FAQ) answering system. Traditional question similarity calculation methods use "word" to compose question vector, that the semantic relations between words are ignored. OBQE takes the relation as an important part. The process of the new system is:① to build two-layered domain ontology referring to WordNet and domain corpse;② to expand question trunks into domain cases;③ to use domain case composed vector to calculate question similarity. The experimental result shows that the performance of question similarity calculation with OBQE is being improved.
基金the US National Science Foundation, Grant No. CMS-0601167
文摘The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.
文摘O n to logy映射是实现异构O n to logy互操作的有效方法,目前的O n to logy映射主要采用句法方法,而很少采用语义方法。本文提出了一种基于语义的O n to logy映射方法,该方法考虑了O n to logy中的概念属性,采用概念名称相似性、概念属性集合相似性、相关概念集合相似性等确定O n to logy之间的语义映射关系;采用语义半径提高了语义映射方法的灵活性。试验分析表明:该方法得到的映射的准确率和查全率在90%以上,准确率和查全率均优于S-M atch方法。该方法已在基于知识需求的主动式知识系统原型中实现,并在某研究所得到了应用。
基金supported by National Natural Science Foundation of China (No. 60873044)Science and Technology Research of the Department of Jilin Education (Nos. 2009498, 2011394)Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University of China(No. ZSDZZZZXK11)
文摘There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.
文摘Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.
文摘The similarity between biomedical terms/concepts is a very important task for biomedical information extraction and knowledge discovery. The measures and tests are tools used to define how to measure the goodness of ontology or its resources. The semantic similarity measuring techniques can be classified into three classes: first, measuring semantic similarity using ontology/ taxonomy;second, using training corpora and information content and third, combination between them. Some of the semantic similarity measures are based on the path length between the concept nodes as well as the depth of the LCS node in the ontology tree or hierarchy, and these measures assign high similarity when the two concepts are in the lower level of the hierarchy. However, most of the semantic similarity measures can be adopted to be used in health domain (Biomedical Domain). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two concepts within single ontology or multiple ontologies in UMLS Metathesaurus (MeSH, SNOMED-CT, ICD), and compare my results to human experts score by correlation coefficient.