Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in relat...Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.展开更多
A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These...A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.展开更多
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.展开更多
This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to im...This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.展开更多
A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic h...A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.展开更多
In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships ...In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships between concepts and taxonomy automatic construction are proposed. At the same time ontology evaluation methods are also discussed. The experimental results show that this method produces better performance and it is applicable across different domains. By integrating several machine learning algorithms, this method suffers less ambiguity and can identify domain concepts and relations more accurately. By using generalized corpus WordNet and HowNet, this method is applicable across different domains. In addition, by obtaining source documents from the web on demand, the GOLF can produce up-to-date ontologies.展开更多
The paper is devoted to the new sphere of applied process ontology. It first makes a short review of the recent investigations in that area. Then it stresses on the importance of applied process ontology. Next the mai...The paper is devoted to the new sphere of applied process ontology. It first makes a short review of the recent investigations in that area. Then it stresses on the importance of applied process ontology. Next the main methodological approaches of applied process ontology are considered： the ＂top down＂ and ＂bottom up＂ approaches. It is argued about the necessity and fruitfulness to combine both ＂top down＂ and ＂bottom up＂ approaches, and not to rely on one of them only. An example is given of the important role of process ontology as general methodological framework for the building up of regional formal ontology. Finally, the idea of variable ontological categories is stressed on and argued for its fruitfulness.展开更多
This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-speci...This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.展开更多
Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster...Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.展开更多
Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these cha...Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.展开更多
In recent years,decision support systems(DSSs)have successfully deployed ontologies in their architecture.The result of such a use is information systems that assist users and organizations in semi-structured decision...In recent years,decision support systems(DSSs)have successfully deployed ontologies in their architecture.The result of such a use is information systems that assist users and organizations in semi-structured decision-making activities.Visitors from throughout Iran travel to different cities and regions every year,and they need help making their choices.Some of these tourists are unable to visit the beautiful areas of the destination city due to a lack of awareness.In this study,we design an ontology-based spatial DSS to find entertainment and tourism centers in Arak,Iran.The objective is to provide users with recommendations appropriate for the location,time,age group,type of activity,and other factors.In this model,the demands and concerns of tourists have been managed by creating a domain Web Ontology Language(OWL)for entertainment centers as a knowledge base in the Protégéenvironment.The developed webbased DSS operates on a client-server architecture using technologies such as Werkzeug and Flask.As a result,it makes it possible to ontology reasoning based on the HermiT engine to choose the right center and conduct a semantic search on classes related to the appropriate point of interest.The main distinction between the proposed methodology and the previous studies on spatial DSS is that criteria are object properties in an ontology.Therefore,decision support relies on real-time reasoning rather than transforming criteria into geospatial layers.The evaluation results confirmed efficient interaction with this system,purposeful information retrieval,and rapid decision-making process.The results also indicated that searching for a POI(point of interest)in the study area using the developed system is at least 30%more successful than a search engine or social media.Moreover,to overcome the cold start problem,the proposed technique might be utilized in conjunction with the POI recommender systems.展开更多
In order to eliminate semantic heterogeneity and implement semantic combination in web information integration, the classification ontology is introduced into web information integration. It constructs a standard clas...In order to eliminate semantic heterogeneity and implement semantic combination in web information integration, the classification ontology is introduced into web information integration. It constructs a standard classification ontology based on web-glossary by extracting classified structures of websites and building mappings between them in order to get unified views. Mapping is defined by calculating concept subordinate matching degrees, concept associate matching degrees and concept dominate matching degrees. A web information integration system is realized, which can effectively solve the problem of classification semantic heterogeneity and implement the integration of web information source and the personal configuration of users.展开更多
In order to solve the problem of semantic heterogeneity in information integration, an ontology based semantic information integration （OSII） model and its logical framework are proposed. The OSII adopts the hybrid ...In order to solve the problem of semantic heterogeneity in information integration, an ontology based semantic information integration （OSII） model and its logical framework are proposed. The OSII adopts the hybrid ontology approach and uses OWL （web ontology language） as the ontology language. It obtains unified views from multiple sources by building mappings between local ontologies and the global ontology. A tree- based multi-strategy ontology mapping algorithm is proposed. The algorithm is achieved by the following four steps： pre-processing, name mapping, subtree mapping and remedy mapping. The advantages of this algorithm are： mapping in the compatible datatype categories and using heuristic rules can improve mapping efficiency; both linguistic and structural similarity are used to improve the accuracy of the similarity calculation; an iterative remedy is adopted to obtain correct and complete mappings. A challenging example is used to illustrate the validity of the algorithm. The OSII is realized to effectively solve the problem of semantic heterogeneity in information integration and to implement interoperability of multiple information sources.展开更多
The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov mode...The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.展开更多
A new algorithm for automated ontology mapping based on linguistic similarity and structure similarity is presented. First, the concept of WordNet is turned into a vector, then the similarity of two entities is calcul...A new algorithm for automated ontology mapping based on linguistic similarity and structure similarity is presented. First, the concept of WordNet is turned into a vector, then the similarity of two entities is calculated according to the cosine of the angle between the corresponding vectors. Secondly, based on the linguistic similarity, a weighted function and a sigmoid function can be used to combine the linguistic similarity and structure similarity to compute the similarity of an ontology. Experimental results show that the matching ratio can reach 63% to 70% and it can effectively accomplish the mapping between ontologies.展开更多
基金supported by the National Natural Science Foundation of China(61902095).
文摘Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
文摘A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.
基金supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
文摘With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.
基金supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091)Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).
文摘This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
基金supported by the National Natural Science Foundation of China(Nos.41725007,42250104,41830323,42002015,and 42302001)the Fundamental Research Funds for the Central Universities(Nos.020614380168,JZ2023HGQA0144 and JZ2023HGTA0175)。
文摘A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.
基金The National Basic Research Program of China(973Program)(No.2003CB317000),the Natural Science Foundation of Zhejiang Province (No.Y105625).
文摘In order to reduce the costs of the ontology construction, a general ontology learning framework （GOLF） is developed. The key technologies of the GOLF including domain concepts extraction and semantic relationships between concepts and taxonomy automatic construction are proposed. At the same time ontology evaluation methods are also discussed. The experimental results show that this method produces better performance and it is applicable across different domains. By integrating several machine learning algorithms, this method suffers less ambiguity and can identify domain concepts and relations more accurately. By using generalized corpus WordNet and HowNet, this method is applicable across different domains. In addition, by obtaining source documents from the web on demand, the GOLF can produce up-to-date ontologies.
文摘The paper is devoted to the new sphere of applied process ontology. It first makes a short review of the recent investigations in that area. Then it stresses on the importance of applied process ontology. Next the main methodological approaches of applied process ontology are considered： the ＂top down＂ and ＂bottom up＂ approaches. It is argued about the necessity and fruitfulness to combine both ＂top down＂ and ＂bottom up＂ approaches, and not to rely on one of them only. An example is given of the important role of process ontology as general methodological framework for the building up of regional formal ontology. Finally, the idea of variable ontological categories is stressed on and argued for its fruitfulness.
文摘This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.
基金supported by the National Key Research and Development Program of China(2020YFC1512304).
文摘Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.
基金supported by the BK21 FOUR Program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091)Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).
文摘Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.
文摘In recent years,decision support systems(DSSs)have successfully deployed ontologies in their architecture.The result of such a use is information systems that assist users and organizations in semi-structured decision-making activities.Visitors from throughout Iran travel to different cities and regions every year,and they need help making their choices.Some of these tourists are unable to visit the beautiful areas of the destination city due to a lack of awareness.In this study,we design an ontology-based spatial DSS to find entertainment and tourism centers in Arak,Iran.The objective is to provide users with recommendations appropriate for the location,time,age group,type of activity,and other factors.In this model,the demands and concerns of tourists have been managed by creating a domain Web Ontology Language(OWL)for entertainment centers as a knowledge base in the Protégéenvironment.The developed webbased DSS operates on a client-server architecture using technologies such as Werkzeug and Flask.As a result,it makes it possible to ontology reasoning based on the HermiT engine to choose the right center and conduct a semantic search on classes related to the appropriate point of interest.The main distinction between the proposed methodology and the previous studies on spatial DSS is that criteria are object properties in an ontology.Therefore,decision support relies on real-time reasoning rather than transforming criteria into geospatial layers.The evaluation results confirmed efficient interaction with this system,purposeful information retrieval,and rapid decision-making process.The results also indicated that searching for a POI(point of interest)in the study area using the developed system is at least 30%more successful than a search engine or social media.Moreover,to overcome the cold start problem,the proposed technique might be utilized in conjunction with the POI recommender systems.
基金The National Key Technologies R&D Program ofChina during the10th Five-Year Plan Period (No.2004BA721A05).
文摘In order to eliminate semantic heterogeneity and implement semantic combination in web information integration, the classification ontology is introduced into web information integration. It constructs a standard classification ontology based on web-glossary by extracting classified structures of websites and building mappings between them in order to get unified views. Mapping is defined by calculating concept subordinate matching degrees, concept associate matching degrees and concept dominate matching degrees. A web information integration system is realized, which can effectively solve the problem of classification semantic heterogeneity and implement the integration of web information source and the personal configuration of users.
文摘In order to solve the problem of semantic heterogeneity in information integration, an ontology based semantic information integration （OSII） model and its logical framework are proposed. The OSII adopts the hybrid ontology approach and uses OWL （web ontology language） as the ontology language. It obtains unified views from multiple sources by building mappings between local ontologies and the global ontology. A tree- based multi-strategy ontology mapping algorithm is proposed. The algorithm is achieved by the following four steps： pre-processing, name mapping, subtree mapping and remedy mapping. The advantages of this algorithm are： mapping in the compatible datatype categories and using heuristic rules can improve mapping efficiency; both linguistic and structural similarity are used to improve the accuracy of the similarity calculation; an iterative remedy is adopted to obtain correct and complete mappings. A challenging example is used to illustrate the validity of the algorithm. The OSII is realized to effectively solve the problem of semantic heterogeneity in information integration and to implement interoperability of multiple information sources.
基金The Weaponry Equipment Foundation of PLA Equipment Ministry (No51406020105JB8103)
文摘The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.
基金The High Technology Research and Development Program of Jiangsu Province (No.BG2004034).
文摘A new algorithm for automated ontology mapping based on linguistic similarity and structure similarity is presented. First, the concept of WordNet is turned into a vector, then the similarity of two entities is calculated according to the cosine of the angle between the corresponding vectors. Secondly, based on the linguistic similarity, a weighted function and a sigmoid function can be used to combine the linguistic similarity and structure similarity to compute the similarity of an ontology. Experimental results show that the matching ratio can reach 63% to 70% and it can effectively accomplish the mapping between ontologies.
