Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

Semantic Knowledge Acquisition from Blogs with Tag-Topic Model

This paper focuses on semantic knowl- edge acquisition from blogs with the proposed tag- topic model. The model extends the Latent Dirichlet Allocation （LDA） model by adding a tag layer be- tween the document and the topic. Each document is represented by a mixture of tags; each tag is as- sociated with a multinomial distribution over topics and each topic is associated with a multinomial dis- trNution over words. After parameter estimation, the tags are used to descrNe the underlying topics. Thus the latent semantic knowledge within the top- ics could be represented explicitly. The tags are treated as concepts, and the top-N words from the top topics are selected as related words of the con- cepts. Then PMI-IR is employed to compute the re- latedness between each tag-word pair and noisy words with low correlation removed to improve the quality of the semantic knowledge. Experiment re- sults show that the proposed method can effectively capture semantic knowledge, especially the polyse- me and synonym.

Intelligent Multilevel Knowledge Acquisition System for Product Design and Its Implementation

The characteristics of design process, design object and domain knowledge of complex product are analyzed. A kind of knowledge representation schema based on integrated generalized rule is stated. An AND-OR tree based model of concept for domain knowledge is set up. The strategy of multilevel domain knowledge acquisition based on the model is presented. The intelligent multilevel knowledge acquisition system (IMKAS) for product design is developed, and it is applied in the intelligent decision support system of concept design of complex product.

Knowledge acquisition method for case-knowledge-based product family design

Knowledge acquisition has always been the bottleneck of artificial intelligence. It is the critical point in product family design. Here a knowledge acquisition method was introduced based on scenario model and repository grid and attribute ordering table technology. This method acquired knowledge through providing product design cases to expert, and recording the means and knowledge used by the expert to describe and resolve problems. It used object to express design entity, used scenario to describe the design process, used Event-Condition-Action（ECA） nile to drive design process, and with the help of repository grid and attribute ordering table technology to acquire design knowledge. It＇ s a good way to capture explicit and implicit knowledge. And its validity is proved with respective examples.

Knowledge Acquisition in Small and Medium Businesses

This article applies to the process of organizational knowledge acquisition by managers and specialists with the possession of manager license. The theoretical part explains： concepts of knowledge, knowledge management, knowledge sources, and the step of creating and acquiring knowledge. The research part focuses on the presentation and analysis of obtained results of research performed by the author.

Product Evaluation Knowledge Acquisition Based on Rough Sets

The method and steps of acquiring evaluation rules based on the knowledge reduction theory of rough sets is discussed, and the distilling process and approach for the evaluation rules of mechanical product structure design is described by using hydraulic torque converter as an example. Practice shows that this approach to a certain extent simplifies the knowledge base structure and reasoning process in comparison with the case-based reasoning method in the aspect of setting up evaluation rule base and carrying out reasoning to realize the mechanical product evaluation.

Domain-Specific Formal Ontology of Archaeology and Its Application in Knowledge Acquisition and Analysis

Inherent heterogeneity and distribution of knowledge strongly prevent knowledge from sharing and reusing among different agents and software entities, and a formal ontology has been viewed as a promising means to tackle this problem. In this paper, a domain-specific formal ontology of archaeology is presented. The ontology mainly consists of three parts: archaeological categories, their relationships and axioms. The ontology not only captures the semantics of archaeological knowledge, but also provides archaeology with an explicit and formal specification of a shared conceptualization, thus making archaeological knowledge shareable and reusable across humans and machines in a structured fashion. Further, we propose a method to verify ontology. correctness based on the individuals of categories. As applications of the ontology,we have developed an ontology-driven approach to knowledge acquisition from archaeological text and a question answering system for archaeological knowledge.

A Survey of Commonsense Knowledge Acquisition

Collecting massive commonsense knowledge （CSK） for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects： CSK acquisition task （what CSK is acquired from where）, technique used （how can CSK be acquired）, and CSK evaluation methods （how to evaluate the acquired CSK）. In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

A Three-Stage Knowledge Acquisition Method

This paper presents and analyzes three fundamental problems in knowledgeacquisition, and proposes a general method for tackling them. The methoddivides the whole process of knowledge acquisition into a set of almost indepen-dent pieces, each of which can be finished by knowledge engineers, experts andassistants, respectively.

A Study of the Techniques of Automatic Abstracting and Knowledge Acquisition Systems

ing; automatic knowledge acquisition; machine learning; natural language processing Abstract One of the most important signs of the information society is the explosion of information. The information in Internet is out of order and is mostly written in natural languages which need to be processed by the technology of natural language processing. When you search for some certain information on Internet through a search engine, you might be confused by the huge amount of results which the search engine provides. However, if a search engine is embedded with Automatic Abstracting (AA) processing systems, you could locate the information quickly or you could get more information within a limited time. So, the AA technology is valuable both in science and application. The work of this thesis was begun when we took over a project that is called 'The Key Technology Research of Computer Networks Providing Intelligent Information Services' which belongs to the national 863 plan. One of the tasks is 'The Key Technology Research of Automatic Abstracting Systems of Chinese Text'. As a member of this research group, I took part in designing and implementing an AA system called Literature Abstract and Digest Information Extract System(LADIES). From then on, I have been working in this field and this paper is the conclusion of my work. The main topic of the thesis is AA technology. There are two parts of it. One is about the research of understanding based AA systems, and the other is about the invcestigation of Automatic Knowledge Acquistion(AKA) in AA systems. In the first part, the contents of AA technology are introduced and an understanding based AA model is put forward. Based on this model, LADIES is implemented. There are two major features of LADIES: (1) it understands text with the grammar, semantic and pragmatic information of words; (2) it chunks words into a relatively independent entity with chunking rules which are substitutes of syntactic analyzing rules. The results demonstrate that it performs better than those statistical based AA systems. However, the application of LADIES is limited for its knowledge bases. And it is difficult to use in other fields because the knowledge bases are setup manually. So we investigate the techniques of automatic knowledge acquisition in order to solve the above problems to some extent. In the second part, we introduce the basic ideas of AKA and some Machine Learning (ML) methods which AKA applies. Then we propose a comprehensive dictionary model that contains grammar, semantic and pragmatic information of words. And we investigate a strategy of automatic learning pragmatic information for words. Also we put forward another strategy of automatic learning rule of salience sentences in texts and based on it, we establish an AA system LADIES NEW. Eventually, we suggest a AKA based AA system model called hierarchical feature extracting AA system model.

Acquisition and Active Navigation of Knowledge Particles throughout Product Variation Design Process

The variation design of complex products has such features as multivariate association, weak theory coupling and implicit knowledge iteration. However, present CAD soft wares are still restricted to making decisions only according to current design status in dynamic navigation which leads to the huge drain of the knowledge hidden in design process. In this paper, a method of acquisition and active navigation of knowledge particles throughout product variation design process is put forward. The multi-objective decision information model of the variation design is established via the definition of condition attribute set and decision attribute set in finite universe. The addition and retrieval of the variation semantics is achieved through bidirectional association between the transplantable structures and variation design semantics. The mapping relationships between the topology lapping geometry elements set and constraint relations set family is built by means of geometry feature analysis. The acquisition of knowledge particles is implemented by attribute reduction based on rough set theory to make multi-objective decision of variation design. The topology lapping status of transplantable substructures is known from DOF reduction. The active navigation of knowledge particles is realized through embedded event-condition-action（ECA） rules. The independent prototype system taking Alan, Charles, Ian＇s system（ACIS） as kernel has been developed to verify the proposed method by applying variation design of complex mechanical products. The test results demonstrate that the navigation decision basis can be successfully extended from static isolated design status to dynamic continuous design process so that it more flexibly adapts to the different designers and various variation design steps. It is of profound significance for enhancing system intelligence as well as improving design quality and efficiency.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Leadership,Knowledge Management,and Human Capital Development

This research highlights the need to develop a framework for leadership,human capital development,and knowledge management by reviewing existing literature in the field of research.The main aim of this research is to propose a model which supports the relationship between leadership(servant leadership,transformational leadership)and human capital development.The study also proposes that knowledge management(knowledge sharing,knowledge acquisition)will moderate the relationship between leadership(servant leadership,transformational leadership)and human capital development.A set of propositions that represent an empirically-driven research agenda,and also describe the relationships between the focal variables are presented to enhance audience’s understanding within a business context.

The Life Cycle of Knowledge in Big Language Models:A Survey

Knowledge plays a critical role in artificial intelligence.Recently,the extensive success of pre-trained language models(PLMs)has raised significant attention about how knowledge can be acquired,maintained,updated and used by language models.Despite the enormous amount of related studies,there is still a lack of a unified view of how knowledge circulates within language models throughout the learning,tuning,and application processes,which may prevent us from further understanding the connections between current progress or realizing existing limitations.In this survey,we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods,and investigating how knowledge circulates when it is built,maintained and used.To this end,we systematically review existing studies of each period of the knowledge life cycle,summarize the main challenges and current limitations,and discuss future directions.

Fault Diagnosis Method Based on System-phenomenon-fault Tree

The historical records of mechanical fault contain great amount of important information which is useful to identify the similar fault.The current fault diagnosis methods using historical records are inefficient to deal with intuitive application and multicomponent multiphase fault diagnosis.Towards the problem,the rapid and intelligent fault diagnosis method based on system-phenomenon-fault （SPF） tree is proposed.The method begins with the physical system of the fault system,conceives the fault causes as leaves,the fault causes as leaves and the frequentness of fault as the interrelationship,and finally forms the fault tree with structural relationship of administrative subordination and flexible multi-granularity components.Firstly,the forming method of SPF tree is discussed;Secondly some basic definitions as synonymous branch,the tough degree of the branch,the dominant leaf,and the virtual branch are defined;and then,the performances including the merger of the dominant branches keeping dominant,the merger of the synonymous branches keeping dominant were proved.Furthermore,the merging,optimizing and calculating of virtual branch of SPF tree are proposed,the self-learning mechanism including the procedure and the related parameter calculation is presented,and the fault searching method and main fault statistics calculation are also presented based on SPF tree.Finally,the method is applied in the fault diagnosis of the certain type of embedded terminal to demonstrate fault information searching in the condition of the synonymous branch,the virtual branch merging and visual presentation of search results.The application shows that the proposed method is effective to narrow down the scope of searching fault and reduce the difficulty of computing.The proposed method is a new way to resolve the intelligent fault diagnosis problem of complex systems by organizing the disordering fault records and providing intuitive expression and intelligent computing capabilities.

Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert System

Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.

Domain-Oriented Data-Driven Data Mining Based on Rough Sets

Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.

Research on a New Kind of ES Based on XML/Web

A new knowledge acquisition methodology for mechani ca l knowledge Web was put forward in this paper after discussed the backwards of k nowledge acquisition method for technical document: natural language understandi ng and ES. A kind of united knowledge representation model, which can include ma ny sorts of knowledge carriers and knowledge representation methods, was advance d firstly, and then mechanical knowledge XML tag was designed based on it. Thus, knowledge still stay in primary electronic document, and knowledge document bec ome document knowledge base based on Web with hierarchy. A corresponding method was advanced and a knowledge acquisition system was developed. It can understand user’s questions in natural language and then find the relevant knowledge and execute reasoning and answer the user’s questions according the result of reaso ning and realized the automatic knowledge acquisition from Web knowledge documen t.

Trend prediction of chaotic time series

To predict the trend of chaotic time series in time series analysis and time series data mining fields,a novel predicting algorithm of chaotic time series trend is presented,and an on-line segmenting algorithm is proposed to convert a time series into a binary string according to ascending or descending trend of each subsequence.The on-line segmenting algorithm is independent of the prior knowledge about time series.The naive Bayesian algorithm is then employed to predict the trend of chaotic time series according to the binary string.The experimental results of three chaotic time series demonstrate that the proposed method predicts the ascending or descending trend of chaotic time series with few error.

The algorithm AE_(11) of learning from examples

We first put forward the idea of a positive extension matrix (PEM) on paper. Then, an algorithm, AE_ 11, was built with the aid of the PEM. Finally, we made the comparisons of our experimental results and the final result was fairly satisfying.