Search

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The knowledge graph in the recommendation system plays a vital role in the recommendation effect of the system, and the knowledge representation in the graph becomes a key factor affecting the recommendation system, which has become one of the current research hotspots. This paper proposed a node2vec-based knowledge representation node2 vec-side based on the traditional node2vec model by adding relational representation and diversifing wandering strategy to the structural characteristics of the knowledge graph in recommendation system, which combined with the knowledge graph network structure of recommendation system to explore the potential association relationship between nodes of large-scale recommendation entities, reduced the complexity of the representation and improved interpretability. After time complexity analysis, it could be seen that the proposed knowledge representation is lower than Trans series and RGCN in terms of complexity. Link prediction experiments were conducted on the traditional knowledge graph datasets FB15K, WN18, and recommendation domain datasets MovieLens-1M, Book-Crossing, Last. FM respectively. The experimental results show that on the MovieLens-1M dataset, hits @ 10 improves 5.5%12.1% and MRR improves 0. 090. 24, respectively. On the Book-Crossing dataset, hits @ 10 improves 3.5%-20.6%, and MRR improves 0.04-0.24 on average, respectively. And on the Last. FM dataset, hits@1 improves 0.3%-8.5% and MRR improves 0.04-0. 16 on average. It is better than the existing algorithms and verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Knowledge graphs, as a novel knowledge representation method, originated from the fields of artificial intelligence and the semantic web. Its development can be traced back to the early 1990s. At that time, the rapid growth of the Internet led to an influx of massive information, but there lacked structured and semantic connections, making information retrieval and comprehension challenging. To address this issue, researchers began exploring a new representation method that could express the relationships between knowledge, thus introducing the concept of knowledge graphs. With the advancement of artificial intelligence technologies, particularly in natural language processing, graph databases, and machine learning, the application of knowledge graphs has expanded significantly. They are not only used for structured knowledge expression and querying but also extensively applied in areas such as search engine optimization, intelligent recommendation systems, and natural language understanding. Moreover, knowledge graphs have achieved remarkable success in industries such as healthcare, finance, and education. In the field of fashion, knowledge graph application has also shown immense potential and promising prospects. We firstly introduced the fundamental concepts, key technologies, and construction methods of knowledge graphs, along with their extensive applications in other fields. Subsequently, we focused on elucidating the necessity of researching knowledge graphs in the domain of fashion. With the rapid development of the fashion industry, intelligence and automation have become increasingly crucial. Knowledge graphs, as a powerful knowledge management and expression tool, are expected to further drive the digitization process in fashion design, production, and marketing. By structurally integrating data and knowledge from various aspects, the fashion industry can better grasp fashion trends, optimize the supply chain, promote sustainability, and offer personalized custom products and services. Currently, there have been several examples of applying knowledge graphs in the fashion industry. In the domain of fashion design, designers can utilize knowledge graphs to organize and manage elements from different cultures, historical periods, and popular trends, drawing inspiration to create more innovative and appealing clothing designs. In terms of fashion production, knowledge graphs can optimize the supply chain, enable material traceability, expedite the production process, reduce costs, and ensure product quality. Additionally, in fashion marketing, leveraging knowledge graphs to analyze user preferences, purchasing behavior, and social media activities enables businesses to offer personalized recommendations, thus enhancing customer satisfaction and loyalty. In addition, the knowledge graph can be used for the visual analysis of literature in the field of clothing, and the use of CiteSpace software for keyword cooccurrence to analyze the research status and trend of the clothing field. The current fashion knowledge graphs still face several challenges. Data quality is a significant issue, especially when dealing with unstructured data, ensuring the accuracy and completeness of the data becomes crucial. Additionally, the integration of knowledge from different data sources is a complex and critical task, necessitating solutions for data standardization and consistency. Moreover, constructing large-scale fashion knowledge graphs demands substantial human resources and time investment. With the continuous development of technologies like artificial intelligence, the application of knowledge graphs is set to become more intelligent and automated, presenting immense opportunities and advantages for the fashion industry. They can foster the collaborative development of the entire industry chain, accelerate the digital transformation of the fashion sector, and enhance the competitiveness and market share of enterprises. Moreover, the combination of knowledge graphs and artificial intelligence holds the potential to pioneer more intelligent applications in fashion design, pattern making, production, and marketing, providing consumers with personalized and high-quality clothing products and services. In conclusion, knowledge graphs possess significant development potential in the fashion domain. Integrating them with technologies such as artificial intelligence can propel the fashion industry towards greater intelligence and automation, injecting new vitality into innovation and development. While challenges exist presently, the continuous progress of technology and deepening application efforts lead us to believe that fashion knowledge graphs will embrace even broader prospects in the future, bringing profound impact and transformation to the entire industry. [ABSTRACT FROM AUTHOR]

Knowledge graph is a large-scale semantic network that gives machine background knowledge. Using knowledge graph to organize heterogeneous medical information can effectively improve the utilization value of massive medical resources and promote the development of medical intelligence. This paper describes the research, construction and application status of knowledge graph in medical field from three dimensions: the key technology of knowledge graph, the construction of medical knowledge graph and the application of medical knowledge graph, and explores the topics worthy of research in the future. Firstly, the development of knowledge representation, knowledge extraction, knowledge fusion and knowledge inference are systematically summarized, their latest progress is discussed, and the technical difficulties in the construction of Chinese medical knowledge graph are analyzed. Secondly, the existing research on Chinese medical knowledge graph is illustrated from three perspectives of medical ontology, general practice knowledge graph and single disease medical knowledge graph. The research characteristics of Chinese medical knowledge graph are also analyzed. Finally, the application of medical know-ledge graph in semantic search, decision support and intelligent question answering are analyzed, and the new app-lication scenarios are discussed. In view of the challenges faced by Chinese medical knowledge graph, such as low standardization of terminology, lack of annotated corpus, insufficient technical research and limitations of applica-tion scenarios, the future research directions of Chinese medical knowledge graph are prospected.

Formal concept analysis is a data analysis method based on concept lattice, while decision implication and variable decision implication are two decision knowledge representations of formal concept analysis. Both decision implication and variable decision implication have complete inference rules for knowledge reasoning. In order to further clarify the differences between variable decision implication and decision implication, this paper first eliminates uncertainty from variable decision implication and introduces restricted variable decision impli-cation. Then, based on the related research on variable decision implication, this paper presents semantic and syntactic characteristics of restricted variable decision implication. On this basis, a complete set of inference rules is obtained for restricted variable decision implication, and decision implication and restricted variable decision implication are compared from the syntactic perspective. Besides, this paper further discusses the differences between the two decision types in knowledge representation and reasoning from the semantic perspective, and summarizes the characteristics of restricted variable decision implication relative to decision implication from the semantic and syntactic perspectives. These conclusions provide new ideas for comparative research of different decision types such as decision implication, variable decision implication, and fuzzy decision implication, as well as a new perspective for introducing more efficient and concise inference rules.

Existing knowledge graph completion models learn a single static feature representation for entities and relationships by integrating multi-source information.But they can't represent the subtle meaning and dynamic attributes of entities and relationships that appear in different contexts.That is,entities and relationships will show different attributes,because they have different roles and meanings when they are involved in different triples.To solve above problems,an adaptive attention network for knowledge graph completion is proposed,which uses adaptive attention to model the contribution of each task-specified feature dimension,and generates dynamic and variable embedding representations for target entities and relationships.Specifically,the proposed model defines the neighbor encoder and the path aggregator to process two structures in the entity neighborhood subgraph,adaptively learn the attention weights to capture the most logically related features of the task,and to give the entities and relationships with fine-grained semantics in line with the current task.Experimental results in link prediction task show that,the MeanRank of the proposed model on FB15K-237 dataset is 6.9% lower than PathCon,and Hits@1 is 2.3% higher than PathCon.For the sparse datasets NELL-995 and DDB14,its Hits@1 reaches 87.9% and 98% respectively.Therefore,it proves that the introduction of adaptive attention mechanism can effectively extract the dynamic attributes of entities and relationships to generate a more comprehensive embedding representation,and improves the accuracy of knowledge graph completion.

The technology about knowledge representation plays an increasingly important role in the autonomous operation of robots facing the complex and unstructured working environment.Knowledge representation focuses on the model of knowledge symbols and how to realize knowledge processing through reasoning procedures automatically.The robot knowledge representation framework and the latest application progress based on ontology representation and reasoning were reviewed.The technical background, realization methods of knowledge representation and reasoning, and recent research progress in the robotics field were summarized from deterministic knowledge and uncertain knowledge.And the future research direction of knowledge-enabled robots was predicted.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper takes smart cartography in the context of artificial intelligence as the starting point, analyzes the impact of space-time big data on cartography, points to three possible directions for the evolu‐ tion of the map, explains the connotation of smart cartography and its characteristics of crowd-sourced da‐ ta, user participation, model coupling and visual analysis, discusses in depth the explicit and implicit knowl‐ edge representation of smart cartography, proposes the idea of establishing a map knowledge graph, and outlines a platform development paradigm based on low-code technology. [ABSTRACT FROM AUTHOR]

In view of at the problem that the traditional knowledge map link prediction task ignores the possible semantic level between knowledge and the low link prediction results caused by the uncertainty of knowledge, this study proposes a Gaussian level-aware knowledge map link prediction model. In the model, the Gaussian embedding part introduces the Gaussian distribution information of entities and relationships, and the distance between the entity distribution and the relationship distribution is used to measure whether there is a link between entities. The word vector embedding part converts the word vectors of entities and relations into complex vectors. The complex vector of words is mapped to the semantic level of the modeling entities in the polar coordinate system, and the distance between the embedding vectors is used to measure whether there is a link between entities. According to the D-S evidence theory, the score function of the two parts is fused to achieve accurate knowledge map link prediction. The experimental results show that the model can effectively model the semantic level and uncertainty of entities in the knowledge graph, and is superior to other methods on the existing benchmark data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge graph embedding (KGE) is a new research hotspot in the field of knowledge graphs, which aims to apply the translation invariance of word vectors to embedding entities and relationships of the knowledge graph into a low-dimensional vector space to complete knowledge representation. In this paper, it is mainly concerned with the classification according to the types of practical problems to be solved. Firstly, it expounds four major types of embedding methods of knowledge graph, including deep learning-based methods, graphical features-based methods, translation model-based methods, and other model-based methods. The algorithm ideas of each model are elaborated, and the advantages and disadvantages of each model are concluded. Secondly, the algorithm experi-ment of knowledge graph embedding is analyzed and summarized from the four aspects of commonly used data sets, evaluation indicators, algorithms, and experiments, then a horizontal and vertical comparison of the embedding method is made. Finally, from the perspective of solving practical problems, the future direction of knowledge graph embedding technology is given. Through research, it is discovered that in the deep learning-based method, LCPE achieves the best effect; in the graphical features-based method, TCE makes the best impression; whereas in the translation model-based method, NTransGH responds most optimistically. Future researches can be expanded on the basis of LCPE, TCE, and NTransGH to continuously improve the experimental effects of link prediction and triplets classification.

Existing research methods have not effectively expressed the structural features of the heterogeneous graph of rumors and not introduced external knowledge as a means of content verification. Therefore, this paper proposed a rumor detection method with knowledge representation and graph convolutional network. It introduced the knowledge graph as additional prior knowledge to verify the authenticity of the content. After applying pre-training word embedding and knowledge graph embedding models to obtain text representation and fusing graph convolutional network simultaneously, it could extract features well in the topological graph of rumor propagation to improve the accuracy of rumor detection. Compared with the baseline methods, experimental results show that the proposed model reaches 96.1 % at precision on Weibo, 3.1 % improvement at F1 on Twitter 15 and 3.3% improvement at F1 on Twitter16 respectively. The ablation experiment also shows this method has a significant improvement on rumor detection, and simultaneously verifies the effectiveness and progressiveness of this model. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The worker’s unsafe behavior is the key cause of mine accidents.The human activity is closely related with the context.Ontology is widely used in computer science in the field of information exchange and knowledge sharing,which contains complex structures and various semantics.Firstly,the research on the description of the miners’ situational information in the mining field was summarized.It introduced the general situation in the field of the Internet of things.The importance and necessity of the description of the state information of mine personnel was presented.It introduced the research background,including semantic web,context description and knowledge representation.After that,the method of information description of mine personnel was described.The ontology model was proposed based on oneM2M international technical specifications.In order to construct this ontology,it was drawed on some of the core concepts of oneM2M Base Ontology.The concept classes,attributes and relationships that are specific to the information description of mine personnel were put forward.The context description of mine personnel proposed was aimed at achieving the purposes of specification information description,data semantic labeling,knowledge sharing and reuse.Finally,the conclusions were drawn and the prospect of using the ontology method for context perception and the miners’ unsafe behavior recognition was presented.

Aiming at the problems of process knowledge reuse and sharing led by the difficulty of unified representation of complex and diverse process knowledge, a process knowledge graph construction method for process reuse is proposed. Firstly, to ensure the accuracy and universality of data schema for process knowledge, the basic schema of process knowledge graph is constructed based on step-nc. Secondly, to improve process knowledge graph basic schema, the process knowledge graph extensive schema is established through process knowledge analysis and process knowledge combination. Meanwhile, to construct the process knowledge graph schema, the existing rules of experience are represented by SWRL language. Moreover, to instantiate the process knowledge graph schema, the process cases are analyzed under the guidance of process knowledge graph schema and the similarity between process cases is computed by latent semantic analysis technology. And then, process cases are transformed into the structured process knowledge graph representation, and process knowledge graph data is obtained. Finally, the process knowledge graph construction application platform is developed to verify the feasibility of the proposed method.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With rapid development of the Internet and social media platforms, hundreds of millions of users have created and shared information as a large quantities of text, image, audio, and video data.Given this large volume of heterogeneous, multi-source, and complex social multimedia content,the challenge of providing effective content understanding and knowledge representation to users with more efficient and high-quality service lias become the key to realizing the value of big social media data.This paper reviews research on social multimedia content analysis, knowledge extraction and representation,and on user modeling applications in recent years.In addition,research on social multi-modal feature fusion, cross-modal knowledge extraction and representation, and user modeling based on social media has been presented in detail.We then discuss development trends in social multimedia content knowledge representation,and review research into and application of user modeling.Finally,we summarize likely research directions for multimedia knowledge representation and user modeling. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Functioning is the core content which is highly regarded by patients and medical intervention in rehabilitation clinical practice. International Classification of Functioning, Disability and Health (ICF) has built a description framework of functioning, and becomes the effective basis of functioning domain knowledge representation. In this paper, we combine the necessary condition of constructing domain knowledge representation system, and analyze the key issues of functioning domain knowledge representation based on ICF including setting frame top concept, collecting terms, riching semantic relations etc., in order to provide a reference for the research of functioning domain knowledge representation.

The study focuses on a task-driven context to investigate two issues, including “informants’ information use behavior in a learning context” and “how the knowledge structure has changed after utilizing the information”. Qualitative methods were employed in this study, including observation, in-depth interview, and document analysis. One of the authors entered the setting as an observer-as-participant to observe the interaction between the lecturer and participants. Semi-structured interviews were conducted to better understand informants’ information use behavior and their cognition toward major concepts of the learning topic. Knowledge construction commenced with evaluating learning tasks assigned by lecturer, examining knowledge structure, deciding the ways of processing information, and then putting into action. During the process, the participants used information in different manners, including browsing, examining, re-examining, marking up, extracting, translating, reconstructing or setting aside. Findings indicate that information use behavior varied in different tasks, and knowledge structures were mainly expanded based on prior knowledge. Several factors influence information use behavior, such as the importance of learning tasks, different forms of knowledge representation, and the time limit, which are the key facts influencing how the information will be used.

Copyright of Journal of Shanghai Physical Education Institute / Shanghai Tiyu Xueyuan Xuebao is the property of Shanghai Physical Education Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge is the basis of the emergency decision-making. Aiming at the problem of requiring multi-disciplinary knowledge to support emergency decision-making, on the strength of the knowledge-unit model, the paper built emergency decision-making knowledge model which provide basic support for emergency knowledge acquisition and knowledge fusion accurately, and solve the problem of multi-disciplinary knowledge representation and sharing. From the perspective of system science and management science, the paper introduced a new fusion method considering the fuzziness of knowledge to solve the conflict between the reality and results produced by existing method, which improve the accuracy and scientific of fusion results. Finally, an example of subway fire event was used to verify the practicability and superiority of the proposed model in the emergency knowledge representation and fusion. [ABSTRACT FROM AUTHOR]

As a form of capital, culture affects the resource allocation to the communities. Teachers’ cultural capital has an absolute advantage and enlightenment values, which is different from ordinary people. On the realistic level, the capital forms of teachers’ professional development are mainly manifested in knowledge form. Teachers’ knowledge capital exhibits different forms in different periods. Ancient teachers’ cultural capital is the sacred knowledge; Modern teachers’ cultural capital is the rational knowledge; Contemporary teachers’ cultural capital is the professional knowledge. The social status of teachers is closely related to the knowledge capital they own, and the change of social consciousness is also influenced by the dynamic knowledge capital. [ABSTRACT FROM AUTHOR]

To overcome the instability of the diagnostic reasoning results caused by the difficulty in knowledge acquisition, the single knowledge representation, and the poor self-adaptation ability of fault diagnosis reasoning method in traditional hoist fault diagnosis systems, the hoist fault diagnosis method based on knowledge engineering is investigated. Fault diagnostic rule knowledge acquisition methods based on improved attribute importance is proposed, and it provides a data basis for hoist fault diagnosis. The mine hoist fault diagnostic ontology knowledge base is constructed and the fault diagnostic ontology knowledge representation methods based on OWL DL and fault diagnostic rule knowledge representation methods based on SWRL are proposed, and the hoist system structure and the diagnosis knowledge integration are implemented. The probability of the ontology knowledge is extended, and a new fault diagnosis uncertainty knowledge reasoning method is proposed, which are based on ontology and Bayesian. Based on the theory and method above, the fault monitoring and diagnosis system of the mine hoist is developed, and the method is proved to be feasible and reliabile. [ABSTRACT FROM AUTHOR]

Many diagnosis expert systems for equine had been developed in the past, but there are no in-depth studies for the equine disease diagnosis expert system. And also most of the traditional expert systems are used by the conventional inference approach. The domain expert confidence factor (CF) for each rule of diseases is kept unchanged with its original value in conventional inference approach. So most of the traditional expert systems have a static knowledge base with static inference, and the decision power of these systems remains same through the life cycle of the system. In fact, the progress of equine knowledge requires modification of the knowledge base and rule base in the system. In this paper, we suggested a new approach for providing the intelligence in the system for diagnosis of the equine diseases. This experiment was conducted to develop a remote auxiliary equine diseases diagnosis expert system. By collecting and analyzing the experiences of diagnosis and treatment from experts on equine disease, the numerical expression of the equine diseases diagnosis knowledge was developed. The knowledge of equine diseases was represented with the method called object-attribute-value triples act (referred as O-A-V act) that combined with the generative formula. As such, it was easy to extract knowledge rules and these rules were used for inference mechanism. Using the confidence factor, multi-valued logic was used to represent the rules of confidence level. In this paper, we suggested a new inference method which was based on use of a fuzzy rule promotion theory. This approach can enhance the intelligence of the disease diagnosis system. If a rule was repeatedly used in corrective diagnostic results, it was then promoted to a higher confidence factor by the rule promotion factor (PCF), and the PCF was the original confidence factor in the next diagnosis session. In short, the dynamic PCF which was generated in the past dialogue was used instead of static CF in the final decision making process. The dynamically promoted rules were derived from those diagnosis sessions, which resulted in successful decisions. This enabled more efficient decision making in the future sessions. With this approach, it was not only decreasing the number of interactive between the system and the users, but also leading to acceptable diagnostic results. Based on the research of knowledge representation and inference mechanism, an auxiliary diagnostic expert system of equine diseases based on Microsoft.Net and SQL Server 2008 was designed and developed. It provided online help to equine farmers and extension workers in China. For the inference engine of system, we used the fuzzy rule promotion methodology that matched facts against conditions and determined which rules were applicable. Also, it automatically revised the confidence factor of each rule. The system performance test was conducted by 804 disease test cases. The successful and unsuccessful diagnosis consultation sessions were noted each time. By thirteen iteration tests, it showed that diagnostic accuracy of the system was closed to 92.28%. It proved that the method was a new way to enhance the diagnostic intelligence. The system met basic requirement of users. Suggestion for future improvement is needed to modify the rule promotion theory by minimizing the errors in the estimation of confidence factor and then estimation of the promoted confidence factor of the rule. By constantly maintaining and updating the knowledge base, and enriching the knowledge base, the system can definitely have a wide application in the countrywide. [ABSTRACT FROM AUTHOR]

Classification is a way of seeing the world. In a classification scheme, phenomena of interest are represented in a context of relationships that provide description, explanation, prediction, heuristics, and the generation of new knowledge. Knowing that information organization isn't equal to knowledge organization, the author first defines the scope of classification and knowledge organization, and then describes the relationship between the classification and the representation and organization of knowledge. At the end, four kinds of classification structures are compared to show their abilities in representing knowledge. In order to utilize these classification structures, it is very important to understand their advantages and disadvantages. (Article content in Chinese with English abstract)

For the problem of that the various design knowledges of self-propelled artillery have not been organized and represented effectively and the existing design knowledge cannot be reused simply during the design process, a knowledge representation method is proposed based on ontology and knowledge components. The organization mode for design ontology of self-propelled artillery and the developing strategy for knowledge components are studied on the base of analyzing the representation requirement of design knowledge of self-propelled artillery, and the mapping relationships among design ontology and knowledge components are established. The representation of design knowledge of self-propelled artillery is achieved. On this basis,a special knowledge retrieval system is developed for designers to quickly reuse the existing design knowledge. The representation and reuse of design knowledge are implemented by taking large-caliber self-propelled artillery for example. The result shows that the proposed method is feasible and valid. [ABSTRACT FROM AUTHOR]

For the realization of the shearer cutting unit intelligent design, the principles of KBE was applied to the shearer cutting unit modem design. The knowledge acquisition method of a rough set extended model based on ε coherence criterion about the overall technical parameter of the shearer was presented and laied a reasoning foundation for the design of cutting unit. The hybrid knowledge expression model was put forward, which was mainly to the knowledge representation of object-oriented, as supplement to production rules and process knowledge representation method, and realized the integration of design object and knowledge. According to the process of design of shearer cutting unit, the fusion reasoning model building was accomplished based on the integration of CBR, RBR and MBR. The modem design system of shearer cutting unit was developed based on UG platform, which proved that the method is feasible and effective. [ABSTRACT FROM AUTHOR]

Semantic web is one of the main trends in current research on web application. From the point of view of Artificial Intelligence, we investigate the development of semantic web and its related domains in AI such as natural language understanding, knowledge representation and reasoning, and ontology, through which we get some important observations. Firstly, the relationship between human and machine is changing rapidly-- the network of computers becomes the basic bridge in the communication and interaction between human and machine. Secondly, human-machine interaction comes to gain a new feature -- semantic processing will take a more and more important role, while syntactic processing is dominant currently. Thirdly, the formal characteristic of machine evolves from homogeneous system to heterogeneous system. A classical logic system is a homogeneous system in the sense that it provides a unique ontology and a unique inferential mechanism so that soundness and completeness hold. The very notion of soundness and completeness has been accepted as a criterion for "correctness" of AI systems in many subfields of AI such as knowledge representation and reasoning, natural language understanding, and planning. However, this criterion is inadequate for the expected "global system" generating from semantic web in the future. Instead, "provable correctness with respect to semantic web rules" is now considered to be an adequate alternative. The reason is that the "global system" will be a heterogeneous system, one equipped with multiple ontologies and multiple inferential mechanisms, where this new criterion is adopted in order to keel) the integrality of the global system. The term "ontology" is borrowed from philosophy and assigned a new meaning, i. e., "a formal specification of a conceptualization". This notion and technique proves extremely important in AI development, especially in very large-scale knowledge systems such as Cyc, which is designed to capture all of the knowledge of an average teenage. The analysis above reveals that AI development along the line of semantic web begins to depart from its "original sketch" embodied by Leibniz's Characteristics Universalis, which can be considered as a universal homogeneous system of human knowledge and communication. The most significant evolvement of Al thought behind this department is the emergence of the possibility of "human machine semantics", a novel semantics shared by human and machine. One can characterize this semantics with concepts of heterogeneity, provable correctness with respect to semantic web rules, understandability to both human and machine. [ABSTRACT FROM AUTHOR]

Based on the analysis of engineering design process, a method of product structure modeling in design was presented. The knowledge processing technology for engineering design was researched: an engineering design knowledge can be represented with object-rule oriented method, which can better describe the tree-like product structure; using an integration of forward and backward reasoning can quick retrieve and solve the object variable; a rule using logic equation is developed to represent engineering design knowledge; a generalized table-based calculation language is developed to improve the integration between engineering theory and rule. Based on these technologies, a new knowledge system was developed which can be integrated into the design systems to improve their knowledge process ability. [ABSTRACT FROM AUTHOR]