Capacity Set Optimization of Knowledge-Based Enterprise and Its Organizational IQ

In this paper, the knowledge based enterprise is considered as an organism, which possesses a set of capabilities. The organizational structure model of knowledge based enterprise organism is described in order to possess the essential capacity set. A dynamic capacity set is defined and analyzed based on the definition of the growth and development for knowledge based enterprise organism. The structure of the capacity base, a subset of the capacity set, is optimized for different periods of the organism ...

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

A Knowledge-based Stampability Evaluation System

The aim, characteristics and requirements of stampability evaluation are identified. As sampability evaluation is highly skill intensive and requires a wide variety of design expertise and knowledge, a knowledge based system is proposed for implementing the stampability evaluation. The stampability evaluation knowledge representation, and processing phases are illustrated. A case study demonstrates the feasibility of the knowledge based approach to stampability evaluation.

KNOWLEDGE AND XML BASED CAPP SYSTEM

In order to enhance the intelligent level of system and improve the interaetivity with other systems, a knowledge and XML based computer aided process planning （CAPP） system is implemented. It includes user management, bill of materials（BOM） management, knowledge based process planning, knowledge management and database maintaining sub-systems. This kind of nesting knowledge representation method the system provided can represent complicated arithmetic and logical relationship to deal with process planning tasks. With the representation and manipulation of XML based technological file, the system solves some important problems in web environment such as information interactive efficiency and refreshing of web page. The CAPP system is written in ASP VBScript, JavaScript, Visual C＋＋ languages and Oracle database. At present, the CAPP system is running in Shenyang Machine Tools. The functions of it meet the requirements of enterprise production.

Design of Multi-attribute Knowledge Base Based on Hybrid Knowledge Representation

Based on the knowledge representation and knowledge reasoning, this paper addresses the creation of the multi-attribute knowledge base on the basis of hybrid knowledge representation, with the help of object-oriented programming language and relational database. Compared with general knowledge base, multi-attribute knowledge base can enhance the ability of knowledge processing and application; integrate the heterogeneous knowledge, such as model, symbol, case-based sample knowledge; and support the whole decision process by integrated reasoning.

A multi-attention RNN-based relation linking approach for question answering over knowledge base

Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.

Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.

The Research of Independent Knowledge Based Mechanical Design

Most of KBE systems applied by previous researchers are dependent on some CAD software, which makes knowledge hard to be reused to other CAD software. Independent knowledge based system is independent of CAD software; therefore knowledge can be reused freely. This paper describes independent knowledge based system for mechanical design. A detailed discussion about typical design is put forward including design process implementation based on knowledge engineering, independent knowledge based design architecture. The main principal of knowledge driven engineering is explained. The implementation of KBE on the design of worm reducer is studied as a case. Independent knowledge based reducer design system is realized. The usage of independent knowledge based system makes KBE system work independent of CAD software, which enhances their portability and fertilizes the collaborative work of heterogeneous CAD systems.

Integrated Digital Design for Radar Typical Structure Using Knowledge Based Engineering

With the deep research of knowledge engineering and the widespread applications of CAD technology, the joining of knowledge engineering with CAD is the focus of advanced manufacturing. An intelligent approach is presented for configurating the typical structural components of radar. Case based reasoning, rule based reasoning, geometric, constraint solving and domain ontology are merged into a compound knowledge model. The main frame and workflow of radar typical structural component design system are illustrated. Experiments show this approach is efficient and effective.

A dynamic knowledge base based search engine

Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.

ESKD-A New Structure of Expert System Based on Knowledge Discovery

A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.

Using Icons in a Knowledge Based System for Traditional Medicine

MedTrad＋ is an expert system for African Traditional Medicine where actors are often illiterate. Its originality lies in the use of icons instead of textual content of facts. It has been created by GExpert＋ which is An Expert system Generator based on the use of icons in a Web environment. With its iconic interface GExpert＋ allows any user to overcome any intermediary for the management and the use of its knowledge base. Traditional healers from Yamoussoukro region are working toward its validation.

Study on the Interior Market Network Structure and Its Efficiency in a Knowledge Based Company

A knowledge based company is the microcosmic foundation of the knowledge economy, the design of its organization structure should amplify the company competence to be agile to the knowledge elements. This paper expounds an interior market network structure which is fit for the company intellectual capital operation, and analyses this organization pattern about the reasons of existence, the effectiveness of growing up in scale, the economies of knowledge distribution and the efficiency of operation, and it will provide some beneficial theoretical guidance about how can a company improve its competition competence in the knowledge environment through organization innovation.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing,mutual transformation of data-knowledge,application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base(GKB)tailored to fracture-cavity reservoir outcrops within the North Tarim Basin.The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations,thin section studies,unmanned aerial vehicle scanning,and high-resolution cameras.Subsequently,a three-dimensional digital outcrop model was generated,and the parameters were standardized.An assessment of traditional geological knowledge was conducted to delineate the knowledge framework,content,and system of the GKB.The basic parameter knowledge was extracted using multiscale fine characterization techniques,including core statistics,field observations,and microscopic thin section analysis.Key mechanism knowledge was identified by integrating trace elements from filling,isotope geochemical tests,and water-rock simulation experiments.Significant representational knowledge was then extracted by employing various methods such as multiple linear regression,neural network technology,and discriminant classification.Subsequently,an analogy study was performed on the karst fracture-cavity system(KFCS)in both outcrop and underground reservoir settings.The results underscored several key findings:(1)Utilization of a diverse range of techniques,including outcrop observations,core statistics,unmanned aerial vehicle scanning,high-resolution cameras,thin section analysis,and electron scanning imaging,enabled the acquisition and standardization of data.This facilitated effective management and integration of geological parameter data from multiple sources and scales.(2)The GKB for fracture-cavity reservoir outcrops,encompassing basic parameter knowledge,key mechanism knowledge,and significant representational knowledge,provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs.(3)The developmental characteristics of fracturecavities in karst outcrops offer effective,efficient,and accurate guidance for fracture-cavity research in underground karst reservoirs.The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Construction and realization of the knowledge base and inference engine of an IDSS model for air-conditioning cooling/heating sources selection

The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.

Criteria Selecting Knowledge Base in the FuzzyController of the Electrohydraulic Position Control System

Aim To analyse the influence of knowledge base on the performance of the fuzzy controller of the electrohydraulic position control system,and to determine their selection cri- teria. Methods Experiments based on different membership functions,scaling factors and con-trol rules were done separately.The experiment results and the influence of different know- ledge base on the control performance were analysed in theory so that criteria of selcting knowledge base can be summarized correctly.Results Knowledge base,including membershipfunctions, scaling factors and control rules,has a crucial effect on the fuzzy control system.Suitably selected knowledge base can lead to good control performance of fuzzy control sys-tem. Conclusion Being symmetric,having an intersection ratio of 1 and satisfying width con- dition are three necessities for selecting membership functions.Selecting scaling factors dependson both the system requirement and a comprehensive analysis in the overshoot,oscillation, rising time and stability. Integrity and continuity must be guaranteed when determining control rules.

Intelligent search on integrated knowledge base of traditional Chinese medicine

To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.