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
The presentation method of the mechanical motion scheme must support thewhole process of conceptual design. To meet the requirement, a semantic network method is selectedto represent process level, action level, mecha...The presentation method of the mechanical motion scheme must support thewhole process of conceptual design. To meet the requirement, a semantic network method is selectedto represent process level, action level, mechanism level and relationships among them. Computeraided motion cycle chart exploration can be realized by the representation and revision of timecoordination of mechanism actions and their effect on the design scheme. The uncertain reasoningtechnology based on semantic network is applied in the mechanism types selection of the needledriving mechanism of industrial sewing mechanism, and the application indicated it is correct,useful and advance.展开更多
Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic netw...Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.展开更多
Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a chall...Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.展开更多
The Grand Canal in China,as a UNESCO World Heritage Site,has received great attention for the revitalization and utilization of its rich cultural heritage.Previous studies have predominantly focused on the tourism pre...The Grand Canal in China,as a UNESCO World Heritage Site,has received great attention for the revitalization and utilization of its rich cultural heritage.Previous studies have predominantly focused on the tourism preferences and values of tangible and intangible resources while often neglecting a more holistic understanding of public perceptions.Analyzing public perceptions of tourist attractions helps clarify their evolving role in the cultural tourism market from the demand side.This study examined this issue based on complex network theory,social media reviews,and semantic network analysis.It identified hotspots in relevant reviews,examined the overall characteristics of the social cognitive image,and analyzed public perceptions through four types of semantic networks based on natural language processing.This study demonstrates a novel approach to policy implication in the regeneration and utilization of cultural heritage.展开更多
Keyword extraction is a branch of natural language processing,which plays an important role in many tasks,such as long text classification,automatic summary,machine translation,dialogue system,etc.All of them need to ...Keyword extraction is a branch of natural language processing,which plays an important role in many tasks,such as long text classification,automatic summary,machine translation,dialogue system,etc.All of them need to use high-quality keywords as a starting point.In this paper,we propose a deep learning network called deep neural semantic network(DNSN)to solve the problem of short text keyword extraction.It can map short text and words to the same semantic space,get the semantic vector of them at the same time,and then compute the similarity between short text and words to extract top-ranked words as keywords.The Bidirectional Encoder Representations from Transformers was first used to obtain the initial semantic feature vectors of short text and words,and then feed the initial semantic feature vectors to the residual network so as to obtain the final semantic vectors of short text and words at the same vector space.Finally,the keywords were extracted by calculating the similarity between short text and words.Compared with existed baseline models including Frequency,Term Frequency Inverse Document Frequency(TF-IDF)and Text-Rank,the model proposed is superior to the baseline models in Precision,Recall,and F-score on the same batch of test dataset.In addition,the precision,recall,and F-score are 6.79%,5.67%,and 11.08%higher than the baseline model in the best case,respectively.展开更多
In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains se...In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.展开更多
A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of ...A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of knowledge triples,it is difficult to directly display to researchers.Semantic Link Network is an attempt,and it can deal with the construction,representation and reasoning of semantics naturally.Based on the Semantic Link Network,this paper explores the representation and construction of knowledge graph,and develops an academic knowledge graph prototype system to realize the representation,construction and visualization of knowledge graph.展开更多
Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ...Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.展开更多
In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained ...In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse”, “augmented reality”, “virtual reality”, “lifelogging”, and “mixed reality”. First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog(y/ies)”, “experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious;however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.展开更多
Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information ...Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.展开更多
A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of sy...A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.展开更多
Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of ...Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of knowledge base and corpus,the Word Segmentation and POS Tagging of text,the Grammatical Analysis and Semantic Analysis of sentences etc.This thesis dissertated mainly the denotation of knowledge-information based on semantic network in QAS,the stochastic syntax-parse model named LSF of knowledge-information in QAS,the structure and constitution of QAS.And the LSF model's parameters were exercised,which proved that they were feasible.At the same time,through "the limited-domain QAS" which was exploited for banks by us,these technologies were proved effective and propagable.展开更多
Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,wa...Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.展开更多
This paper uses word frequency statistics and semantic network analysis to analyse text related to genetically modified organisms(GMOs) in microblog in China. We discuss the structure of the main discourses and change...This paper uses word frequency statistics and semantic network analysis to analyse text related to genetically modified organisms(GMOs) in microblog in China. We discuss the structure of the main discourses and changes in them over the past decade, explore the reasons for those changes and provide possible references that may be useful when related problems or situations occur in future. We have found that conspiracy theories permeated online discussions and that netizens' emotions had a nationalist tendency. The GMO issue was highly socialized. Participants in online discussions were from different backgrounds, and the topics went far beyond GMO technology. The public tended to trust the government, rather than experts, while opinion leaders also played a role in guiding public opinion. The keywords in this discussion have gradually changed in recent years from clustering around ‘harmful’ to clustering around ‘scientific’, and new participation models brought about by new media have provided new reference paths for problem solving.展开更多
This study introduces a novel framework to dissect and understand tourists'cultural perceptions within river basins.The framework consists of two complementary parts:first,it develops a multi-dimensional system to...This study introduces a novel framework to dissect and understand tourists'cultural perceptions within river basins.The framework consists of two complementary parts:first,it develops a multi-dimensional system to identify cultural perceptions through textual analysis;second,it uses advanced methods like deep learning and spatial clustering to analyze and compare these perceptions across different cities and regions.The findings from the Yellow River Basin reveal six key dimensions of cultural perception:historical,architectural,folklore,food,reli-gious,and leisure.The basin exhibits three distinct cultural patterns:an upstream polycentric network,a central‘cultural circle’around Xi’an,and a city-to-city pattern downstream.Furthermore,the basin is categorized into ten unique cultural perception regions,each highlighting diverse tourist perceptions.This framework not only offers a methodological beacon for future regional tourism studies but also equips managers with strategic insights to en-hance the quality and cooperation in river basin tourism development.展开更多
The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes a...The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.展开更多
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.
基金This Project is supported by National Natural Science Foundation of China(No.59875058).
文摘The presentation method of the mechanical motion scheme must support thewhole process of conceptual design. To meet the requirement, a semantic network method is selectedto represent process level, action level, mechanism level and relationships among them. Computeraided motion cycle chart exploration can be realized by the representation and revision of timecoordination of mechanism actions and their effect on the design scheme. The uncertain reasoningtechnology based on semantic network is applied in the mechanism types selection of the needledriving mechanism of industrial sewing mechanism, and the application indicated it is correct,useful and advance.
文摘Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.
基金supported by the STI2030-Major-Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.
基金supported by China Postdoctoral Science Foundation (2022M710669)Natural Science Foundation of Jiangsu Province,China (BK20211371)+1 种基金Jiangsu Provincial Key Research and Development Program (BE2023799)National Natural Science Foundation of China (42371191)。
文摘The Grand Canal in China,as a UNESCO World Heritage Site,has received great attention for the revitalization and utilization of its rich cultural heritage.Previous studies have predominantly focused on the tourism preferences and values of tangible and intangible resources while often neglecting a more holistic understanding of public perceptions.Analyzing public perceptions of tourist attractions helps clarify their evolving role in the cultural tourism market from the demand side.This study examined this issue based on complex network theory,social media reviews,and semantic network analysis.It identified hotspots in relevant reviews,examined the overall characteristics of the social cognitive image,and analyzed public perceptions through four types of semantic networks based on natural language processing.This study demonstrates a novel approach to policy implication in the regeneration and utilization of cultural heritage.
基金the Major Program of National Natural Science Foundation of China(Grant Nos.91938301)the National Defense Equipment Advance Research Shared Technology Program of China(41402050301-170441402065)he Sichuan Science and Technology Major Project on New Generation Artificial Intelligence(2018GZDZX0034).
文摘Keyword extraction is a branch of natural language processing,which plays an important role in many tasks,such as long text classification,automatic summary,machine translation,dialogue system,etc.All of them need to use high-quality keywords as a starting point.In this paper,we propose a deep learning network called deep neural semantic network(DNSN)to solve the problem of short text keyword extraction.It can map short text and words to the same semantic space,get the semantic vector of them at the same time,and then compute the similarity between short text and words to extract top-ranked words as keywords.The Bidirectional Encoder Representations from Transformers was first used to obtain the initial semantic feature vectors of short text and words,and then feed the initial semantic feature vectors to the residual network so as to obtain the final semantic vectors of short text and words at the same vector space.Finally,the keywords were extracted by calculating the similarity between short text and words.Compared with existed baseline models including Frequency,Term Frequency Inverse Document Frequency(TF-IDF)and Text-Rank,the model proposed is superior to the baseline models in Precision,Recall,and F-score on the same batch of test dataset.In addition,the precision,recall,and F-score are 6.79%,5.67%,and 11.08%higher than the baseline model in the best case,respectively.
基金Supported by the National Natural Science Foundation of China( 60873225, 60773191, 70771043)National High Technology Research and Development Program of China ( 2007AA01Z403)Wuhan Youth Science and Technology Chenguang Program (200950431171)
文摘In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.
文摘A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of knowledge triples,it is difficult to directly display to researchers.Semantic Link Network is an attempt,and it can deal with the construction,representation and reasoning of semantics naturally.Based on the Semantic Link Network,this paper explores the representation and construction of knowledge graph,and develops an academic knowledge graph prototype system to realize the representation,construction and visualization of knowledge graph.
文摘Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.
文摘In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse”, “augmented reality”, “virtual reality”, “lifelogging”, and “mixed reality”. First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog(y/ies)”, “experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious;however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.
基金supported in part by the Beijing Natural Science Foundation under Grants L211020 and M21032in part by the National Natural Science Foundation of China under Grants U1836106 and 62271045in part by the Scientific and Technological Innovation Foundation of Foshan under Grants BK21BF001 and BK20BF010。
文摘Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.
文摘A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60305009)the Ph.D Degree Teacher Foundation of North China Electric Power University(Grant No.H0585).
文摘Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of knowledge base and corpus,the Word Segmentation and POS Tagging of text,the Grammatical Analysis and Semantic Analysis of sentences etc.This thesis dissertated mainly the denotation of knowledge-information based on semantic network in QAS,the stochastic syntax-parse model named LSF of knowledge-information in QAS,the structure and constitution of QAS.And the LSF model's parameters were exercised,which proved that they were feasible.At the same time,through "the limited-domain QAS" which was exploited for banks by us,these technologies were proved effective and propagable.
文摘Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.
基金supported by the Science Popularization and Risk Communication of Transgenic Biotechnologies project (grant ID:2016ZX08015002)
文摘This paper uses word frequency statistics and semantic network analysis to analyse text related to genetically modified organisms(GMOs) in microblog in China. We discuss the structure of the main discourses and changes in them over the past decade, explore the reasons for those changes and provide possible references that may be useful when related problems or situations occur in future. We have found that conspiracy theories permeated online discussions and that netizens' emotions had a nationalist tendency. The GMO issue was highly socialized. Participants in online discussions were from different backgrounds, and the topics went far beyond GMO technology. The public tended to trust the government, rather than experts, while opinion leaders also played a role in guiding public opinion. The keywords in this discussion have gradually changed in recent years from clustering around ‘harmful’ to clustering around ‘scientific’, and new participation models brought about by new media have provided new reference paths for problem solving.
基金The National Social Science Fund of China(24BGL137)The Beijing Municipal Commission of Education(SM202010031004)。
文摘This study introduces a novel framework to dissect and understand tourists'cultural perceptions within river basins.The framework consists of two complementary parts:first,it develops a multi-dimensional system to identify cultural perceptions through textual analysis;second,it uses advanced methods like deep learning and spatial clustering to analyze and compare these perceptions across different cities and regions.The findings from the Yellow River Basin reveal six key dimensions of cultural perception:historical,architectural,folklore,food,reli-gious,and leisure.The basin exhibits three distinct cultural patterns:an upstream polycentric network,a central‘cultural circle’around Xi’an,and a city-to-city pattern downstream.Furthermore,the basin is categorized into ten unique cultural perception regions,each highlighting diverse tourist perceptions.This framework not only offers a methodological beacon for future regional tourism studies but also equips managers with strategic insights to en-hance the quality and cooperation in river basin tourism development.
基金Supported by the National Natural Science Foundation of China(No. 61070156)Special Youth Research and Innovation Programs (Nos.2009QNA5025 and 2010QNA5044)IBM-ZJU Joint Research Projects
文摘The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.
