Problem Of Knowledge Acquisition Research Articles

Is there a place for logic in recognizing textual entailment?

From a purely theoretical point of view, it makes sense to approach recognizing textual entailment (RTE) with the help of logic. After all, entailment matters are all about logic. In practice, only few RTE systems follow the bumpy road from words to logic. This is probably because it requires a combination of robust, deep semantic analysis and logical inference—and why develop something with this complexity if you perhaps can get away with something simpler? In this article, with the help of an RTE system based on Combinatory Categorial Grammar, Discourse Representation Theory, and first-order theorem proving, we make an empirical assessment of the logic-based approach. High precision paired with low recall is a key characteristic of this system. The bottleneck in achieving high recall is the lack of a systematic way to produce relevant background knowledge. There is a place for logic in RTE, but it is (still) overshadowed by the knowledge acquisition problem.

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System

In this paper, a fault diagnosis method for spacecraft based on telemetry data mining and fault tree analysis was proposed. Decision trees are constructed from the history telemetry data of the spacecraft, and are used to classify the current data which is unknown whether it is fault. If there is a fault, the fault tree method will be used to analyze the fault reason and the impact on the spacecraft system. This method can effectively solve the problem of diagnostic knowledge acquisition. We design and construct a fault diagnosis expert system for spacecraft based on this diagnosis method. An experiment is presented to prove the effectiveness and practicality of the expert system.

Research on Knowledge Acquisition Based on Improved Particle Swarm Optimization

In order to solve the problem of knowledge acquisition in equipment fault diagnosis,the paper introduces a method based on improved particle swarm optimization. Firstly the paper transforms the nonlinear equations which describe the system into optimization problem with constriction. Since the equation is nonlinear and multidimensional ,standard particle swarm cant solve the problem due to the weakness of premature. So one improved particle swarm optimization is proposed. During the evolution, density evaluation, clone and mutation operator is proposed under the thought of immunity. The results of simulation show that the immune particle swarm optimization can simulate effectively and acquire the system knowledge.

U Nong Shu Cai Tong System - The Agricultural Expert System Based on U Disk

Chinese rural areas’ Internet coverage is relatively low based on the investigation of Beijing suburbs. It will take a long time to achieve that Internet enters households of farmers. In response to this reality, the paper puts forward the new idea that use U disk to lead agricultural information resources into rural areas, so agricultural expert system based on U disk-U Nong Shu Cai Tong System is developed. It can effectively solve the problem of farmers' knowledge acquisition. The paper discusses the background, architecture, functions, system development’s difficulties and application of U Nong Shu Cai Tong System.

Spatial Structure Analysis and Identification of Candidate Hyponymy Relations

Automatic acquisition of semantic relations is an important problem in knowledge acquisition. We present a method of spatial structure analysis of candidate Chinese hyponymy based on concept space and use them to identify candidate hyponymy. Firstly a group of candidate hyponymy relations is imported into concept space. Secondly we analyze the basic spatial structure of concept space including concept nodes and relation edges. More identified features based on the spatial structure analysis are given and used to identify hyponymy relations. Experimental results show that the spatial structure is very useful to the identification of hyponymy.

Knowledge Flow in Supply Chain Manufacturing: Case Study in Food Manufacturing Firm

Abstract This paper discusses the knowledge acquisition problems faced in the Supply Chain Management (SCM) when acquiring knowledge among Supply Chain (SC) members, and it subsequently shows how modelling of knowledge types can be used as knowledge base to assist in solving SCM problems. This paper justifies the types of knowledge are useful in order to build knowledge bases to help decision makers and more importantly to help making decisions at the right time. The aim of this research is to explore knowledge types in the supply chain of food manufacturing firms. Our research method emphasizes content analysis and case study based on food manufacturing firm. As a result, we developed a model for the knowledge flow in SCM that advocates the knowledge that are relevant among SCM members. This model can dramatically save knowledge and, more fundamentally, allows for the knowledge engineers to build a knowledge based system based on this model.

Research on Fault Knowledge Acquisition Base of Circuit Simulation

To resolve bottle-net problem of knowledge acquisition of fault diagnoses expert system. This paper make use of circuit simulation software Multisim2001module modeling and simulating, acquiring fault data of electric control circuit of missile launching vehicle, acquiring correspondence relation of function-fault, establishing knowledge base of intelligent diagnostic expert system, affording a new thought to resolve knowledge acquisition of expert system. The fault diagnosis expert system is based on the knowledge base, but many fault diagnosis expert systems face the same problem: the knowledge of fault and fault maintenance is very poor. This causes many of intelligent fault diagnosis systems are still in the theoretical research or prototype design stage, and its use value is low, therefore, how to gain more significant and enough knowledge has turned to be the bottleneck which confine the engineering operational capability of fault diagnosis system[1] . This article will build an electrical system dynamic operational model of some equipment by circuit simulation. And it will do the direct current analysis, transient analysis etc. for the circuit by the software’s powerful analyze ability, include the setting of some devices’ voltage value, current value and test point, and gain the simulation parameters of normal conditions and fault conditions. Then lead these data into the database so as to the system to call and search, and confirm the fault node finally [2, 3]. The function simulation which is mentioned in this article is an ingredient of the equipment system simulation; structure simulation and movement simulation are also like it. And it contains system-level simulation, part-level simulation and component-level simulation in the hierarchical division. Because of the limited space, this article just considerate the electricity circuit simulation’s application in the fault knowledge acquisition, which is belong to the function simulation.

Knowledge Acquisition from In-Operation Data for Water Supply System by Using SVM and PCA

This study aims to replicate the operations performed by an experienced operator of a water supply system. Steering groups of water supply systems face problems because of the decreasing number of experienced operators. Without the skill of experienced operators, it is difficult to carry out safe and stable operations. Regression analysis was adapted in this study to replicate the operations performed by an experienced operator. To resolve the nonlinear regression problems of knowledge acquisition and decreasing number of experienced operators, a support vector machine (SVM) was used. For knowledge acquisition from sensor data, data mining and principal component analysis (PCA) were used. Experimental results showed that when the proposed method was used, the average of the root mean square values of the water level improved by 29.8% as compared to that obtained by the conventional method. Thus, we confirmed that the proposed method can acquire operation knowledge from experienced operators and use it to prepare an operation plan. This method is expected to compensate for the decrease in the number of experienced operators.

Concept Optimization Model with Multilevel Hierarchy Based on Fuzzy Neural Network

To implement optimization for mechanical concepts acquired by function analysis more effectively, BP neural network is adopted to structure multilevel evaluation model, which capitalizes on the features of nonlinearity, self-organization, and fault tolerance of neural network. By using appropriate data sets to train the neural network, expertise is acquired and expressed using a trained weight and threshold matrix. Once evaluation objectives of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model, the optimal concept can be obtained. During the process, neural network is used to solve the bottle-neck problem of knowledge acquisition and expression and can be viewed as knowledge base and reasoning engine for the optimization. Hence the proposed evaluation model can effectively deal with concept evaluation and optimization problem with multilevel objective system.

Special Issue on Structured Knowledge Transfer

This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.

Research on the Method of Style Knowledge Acquisition Based on the Kansei Engineering

To solve the problem of the uncertain style knowledge acquisition during the product designing, the information model oft style with Kansei Engineering and a reduct approach to style information based on rough sets is introduced. On the basis of the attribute analysis of form features and the image semantic extractions by the web-based style evaluation system. In the system the evaluate information of style is obtained and the decision-making table is constructed. And then the valuable knowledge rules represented the typical product style are acquired through the attribute reduction algorithm. Finally, the attribute feature sets related to the different style image could be presented. Typical Ming-style chairs as an example to show that the method is effective and feasible.

Experts' knowledge acquisition for differential diagnostics of bronchial asthma in children in STEPCLASS environment

The problem of experts' knowledge acquisition for diagnostic decision support systems (DDSS) is considered as a multi-attribute classification problem (MACP), where a class is the subset of objects with the same diagnosis. Method STEPCLASS has proved to be an effective tool for such problem solving in various applications due to its ability to construct complete (up to the experts' knowledge) and contradiction-free knowledge bases. The main techniques of STEPCLASS – application domain structuring, large-size problem decomposition, classification rules eliciting, and rules' inconsistency control – are considered and illustrated with its application for constructing knowledge base of DDSS for differential diagnostics of bronchial asthma in children (DDBAC).

The Model of Mobile Knowledge Services System in the Management of Complex Cluster Project

Cluster projects are projects of large-scale or with huge investment, such as Beijing 2008 Olympic Games and Shanghai 2010 Expo. A lot of on-site management works are involved in the process of cluster project management, especially the treatment of sudden accidents which would produce plenty of knowledge and experience. As cluster project on-site management spots change frequently, knowledge produced by mobile workers in the process of on-site management is hard to be saved into the knowledge repository on time. It may cause a knowledge distortion or even missing. On the other hand, in the process of cluster project management, a lot of knowledge is required by on-site workers to deal with on-site activities. However, due to lack of effective mechanisms for knowledge communion, they often face the risk of making wrong decisions. To solve the problems of knowledge management and acquisition in the process of cluster project on-site management, this paper presents a system model for mobile knowledge service.

Mechanisms of Cognitive Development: Domain‐General Learning or Domain‐Specific Constraints?

The issue of how people acquire knowledge in the course of individual development has fascinated researchers for thousands of years. Perhaps the earliest recorded effort to put forth a theoretical account belongs to Plato, who famously advocated the idea that knowledge of many abstract categories (e.g., ‘‘equivalence’’) is innate. Although Plato argued with his contemporaries who advocated the empirical basis of knowledge, it was the British empiricists who most forcefully put forth the idea of the empirical basis of knowledge, with John Locke offering the famous ‘‘tabula rasa’’ argument. The first comprehensive psychological treatment of the problem of knowledge acquisition was offered by Piaget (1954), who suggested that knowledge emerges as a result of interactions between individuals and their environments. This was a radical departure from both extreme nativism and extreme empiricism. However, these ideas, as well those of empiricist-minded behaviorists, fell short of providing a viable account of many human abilities, most notably, language acquisition. This inability prompted Chomsky to propose an argument that language cannot be acquired from the available linguistic input because it does not contain enough information to enable the learner to recover a particular grammar, while ruling out alternatives (Chomsky, 1980). Therefore, some knowledge of language must be innate to enable fast, efficient, and invariable language learning under the conditions of the impoverished linguistic input. This argument (i.e., known as the Poverty of the Stimulus argument) has been subsequently generalized to perceptual, lexical, and conceptual development. The 1990 Special Issue of Cognitive Science is an example of such generalization. The current Special Issue on the mechanisms of cognitive development has arrived exactly 20 years after the first Special Issue. In the introduction to the 1990 Special Issue of Cognitive Science, Rochel Gelman stated:

Beyond personality: the impact of GMA on performance for entry‐level service employees

PurposeWhile personality is often deemed important, few studies have examined the relationship between general mental ability (GMA) and job performance for entry‐level service employees. As such, the present study aims to examine the impact of GMA, along with the Big Five personality dimensions, on performance in this context.Design/methodology/approachData were collected on servers from multiple units of two large restaurant chains. The employees completed GMA and personality assessments. Managers in Sample 1 provided ratings of service performance and two dimensions of contextual performance – interpersonal facilitation and job dedication. In Sample 2, sales performance data were obtained from company records.FindingsUnlike the Big Five taxonomy, GMA was the only variable to significantly predict all performance criteria.Research limitations/implicationsSuccessful job performance requires more than habits and motivation rooted in personality; it also requires knowledge acquisition and ongoing problem solving facilitated by GMA.Practical implicationsManagers should consider GMA along with personality in hiring decisions to maximize the prediction of employee performance.Originality/valueThe paper demonstrates that GMA impacts multiple dimensions of server performance, including task and contextual performance.

Research on the Intelligent Quality Management System of Supply Chains Based on Six Sigma Theory

Aiming at the features of the common Supply Chain quality management, an intelligent Supply Chain quality management system based on Six Sigma theory was proposed. The Six Sigma workflow, organizational framework and system goal were adopted in the system overall design, and some traditional Six Sigma tools were integrated into the system. A sub-system for expansive intelligent Six Sigma expert model was designed, which solved the problem of knowledge acquisition in the general expert system and endowed the system with the characteristic of local intelligence.

Unsupervised Translation Disambiguation Based on Web Indirect Association of Bilingual Word

To solve the problems of data sparseness and knowledge acquisition in translation disambiguation and WSD (word sense disambiguation), this paper introduces a fully unsupervised method, which is based on Web mining and Web indirect association of bilingual words. It provides new knowledge of translation disambiguation. It assumes that word sense can be determined by indirect association of bilingual words. Based on Web, this paper revises four common methods of indirect association, and designs three decision methods. These methods are evaluated on a gold standard Multilingual Chinese English Lexical Sample Task dataset of SemEval- 2007. The experimental results show that the model gets the state-of-the-art results (Pmar=44.4%) and outperforms the best system in SemEval-2007.

Promoting college students’ knowledge acquisition and ill-structured problem solving: Web-based integration and procedure prompts

This study examined how web-based integration and procedure question prompts differentially affected students’ knowledge acquisition and ill-structured problem solving skills, particularly in representing problem(s), developing solutions, and monitoring and evaluating a plan of action within the social science context. Eighty-four undergraduate pre-service teachers were recruited and randomly assigned to one of the four conditions: (1) an IP condition that required students to complete integration prompts, (2) a PP condition that required students to complete procedure prompts, (3) an IPP condition that required students to complete both integration and procedure prompts, or (4) a control condition that did not provide access to any prompts. The findings show that students who received integration prompts outperformed those who did not receive any in knowledge acquisition and problem representation for solving an ill-structured problem. Integration prompts also helped the development and integration of cognitive schema, whereas procedure prompts helped direct students’ attention to specific features of the problem in order to arrive at the solution(s). In fact, the presence of an integration prompt alone is not sufficient to support successful ill-structured problem solving unless a procedure prompt is provided. Based on these findings, this study offers implications for designing Web-based learning environments, engineered to promote integrative knowledge and ill-structured problem solving skills.

An Iterative Method of Extracting Chinese ISA Relations for Ontology Learning

Automatic acquisition of ISA relations is a basic problem in knowled ge acquisition from text. We present a n iterative method extracting ISA relations from large Chinese free text for ontology learning . Firstly, i t initially discovers a set of sentences using several special Chinese lexico-syntactic patterns from free text corpus . Secondly we combine outside layer removal and inside layer gathering for acquiring concepts of constituting ISA relation. Finally, ISA relations are verified with multiple features. Extracted ISA relations will be selected for new relation extracting cycle. Experimental results demonstrate good performance of the method for extracting ISA relation from large Chinese corpus.

Automating dimensional tolerancing using Ripple down Rules (RDR)

We propose to use a knowledge based approach to assist in mechanical design focusing on dimensional tolerancing. To illustrate our approach, we capture the knowledge which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. The software system we developed would help mechanical designers become more effective in the time-consuming dimensioning and tolerancing process of their designs in the future. In doing this, the paper makes a twofold contribution to the field of knowledge acquisition: firstly, interface was adjusted to receive mathematical functions with their specifications prior and during the KA process to propose an approach to exploit relationships among several classes with respect to certain numerical features of the cases in order to accelerate the convergence of the RDR knowledge acquisition process by generating artificial cases which are likely to trigger the addition of exception rules. Secondly, it introduces the above problem domain of determining suitable tolerances for mechanical parts in a design as a knowledge acquisition problem.