Intelligent Knowledge Research Articles

Intelligent knowledge management in operations research

Scientists employ sophisticated programs to mine and extract interesting information and knowledge from enormous databases. Intelligent knowledge, extracted from large data sets, reveals useful insights and hidden patterns. Intelligent knowledge management investigates the theoretical framework of data science and intelligent knowledge to bridge the gap between mining and knowledge management and further develops the techniques of how to identify and obtain the intelligent knowledge for decision-making. Operations research (OR) and management science (MS) have strong influences on the development of intelligent knowledge management. Many ORmethodologies and tools have been applied to knowledge sharing, knowledge organization, knowledge acquisition, and knowledge usage. It is important to synthesize the research findings of intelligent knowledge and OR with a wide range of real-life applications to support sustainable economic development. This special volume includes both high-quality academic (theoretical or empirical) and practical papers that present current state-of-the-art results from researchers and practitioners from all related disciplines. All papers have some numerical or experimental illustrations of scientific value. The guest editors hope that the papers published in this special volume will be of interest to academic and industrial communities by promoting high quality, novel and daring research findings. The volume provides solutions and tools to solve challenging problems faced by fellow researchers, practitioners, and students, as well as research challenges and initiatives for further research.

Commonsense reasoning and commonsense knowledge in artificial intelligence

AI has seen great advances of many kinds recently, but there is one critical area where progress has been extremely slow: ordinary commonsense.

The hybrid intelligent systems design using grey systems theory

Purpose– The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.Design/methodology/approach– Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.Findings– Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.Originality/value– The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

A Study of Future Internet Applications based on Semantic Web Technology Configuration Model

The need for future Internet applications based on semantic web technology should ensure the growth of industrial technologies by developing practical ones. The design direction for a next generation web based industrial application systems may be a system design that can solve the problems of semantic web, especially intelligent system, knowledge acquisition and engineering, and knowledge representation. The proposed framework aims to be used for technical engineering for industries and it could be a system design for manufacturing products, developing services, and analysis and sales. A system design should develop a framework available for real industrial fields, not just for academic research purposes. The range of an integrated design for related technologies includes the integration and redesign of existing technologies for web utilizing and the new technology design. This research proposes a configuration model of future Internet applications based on semantic web technology for logical system and adaptation to standardization theory as the conceptual model of neutral theory.

Frontiers for propositional reasoning about fragments of probabilistic conditional independence and hierarchical database decompositions

Conditional independence provides an essential framework to deal with knowledge and uncertainty in Artificial Intelligence, and is fundamental in probability and multivariate statistics. Its associated implication problem is paramount for building Bayesian networks. Unfortunately, the problem does not enjoy an axiomatization, by a finite set of Horn rules, and is already coNP-complete to decide for some fragments of conditional independencies. Saturated conditional independencies form an important fragment whose implication problem is decidable in almost linear time. We establish an axiomatization, by a finite set of Horn rules, for the fragment of generalized saturated conditional independencies. These state the conditional independence between finitely many sets of random variables. The special case of two sets captures Geiger and Pearl's finite axiomatization for saturated conditional independencies. Even for this special case, our completeness proof is new. The proof argument utilizes special probability models where two events have probability one half. Special probability models allow us to establish an equivalence between the implication of generalized saturated conditional independencies and that of formulae in a Boolean propositional fragment. This duality is then extended to a trinity including the implication problem of Delobel's full first-order hierarchical database decompositions. Already for the independence between two sets of random variables, the dualities cannot be extended to cover conditional independencies in general, or first-order hierarchical decompositions.

Intelligent Knowledge Assessment for CAD/CAM Systems

Intelligent tutoring system (ITS) with the possibility to track a user’s / student’s operations into CAD/CAM system would allow gaining a wide range of knowledge about successful or non-successful completion of the task and precision of the result – drawing and/or design. It would be possible to diagnose each stage of a students’ work in any CAD/CAM system. Each action would be traceable and ITS would gain information about the quality of these actions (speed, precision, tools used, coherence and sequence). In this paper for implementation of such systems the intelligent agent paradigm is used and multi agent system is proposed.

English

Image mining is defined as the discovery of image patterns in a given collection of images. It is an effort that fundamentally draws upon knowledge in computer vision, image processing, data mining, machine learning, database, and artificial intelligence. Facial recognition helps to analyze and compare the patterns from the facial images. Facial feature extraction is an automatic recognition of human faces by detecting its features i.e. eyes, eyebrows and lips. In this research work, features are extracted from the human facial images by using the existing Face Part Detection (FPD) algorithm and the newly proposed Gray Level Co-occurrence Matrix (GLCM) algorithm. FPD uses bounding box method and GLCM uses affine moment invariants method. Performance factors applied here are feature extraction accuracy and execution time. The implementation of this work is performed in MATLAB 7.0. Based on the experimental results, it is observed that the proposed GLCM algorithm extracted the features more accurately with minimum execution time than FPD algorithm.

Da paneleira à bonequeira: vida econômica, espaço doméstico e técnica da cerâmica em transformação no Jequitinhonha

Comunidades rurais caboclas conquistaram enormes extensões de terra no Brasil, preservando-se em algumas regiões, extinguindo-se em outras, seja em decorrência do avanço da grande economia envolvente, seja em virtude de dinâmicas internas. De certo, na constituição da técnica da cerâmica mais rudimentar existe um longo caminho de conhecimento inteligente e um conhecimento em transformação, na atualidade, em face dos novos desafios da arte figurativa. As comunidades de camponeses-ceramistas do vale do Jequitinhonha [Minas Gerais] sofrem com o progressivo abandono dos fogões a lenha e do vasilhame de barro, e sua gradual substituição, na cozinha sertaneja, por fôrmas, baixelas, panelas e fogões em metal, o que enseja a falência do ofício tradicional das paneleiras como também das bonequeiras da região, ou, quando não, sua transformação, concomitante à dos objetos que produz. É o que se pretende discutir neste Artigo.

Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization

This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.

A Research and Implementation of Internal Medicine Diagnosis Assisted by Intelligence Knowledge Base

Intelligent knowledge system is an important knowledge base for internal medicine diagnosis. Intelligent diagnosis of the knowledge base can be realized by establishing appropriate expert models to assist diagnosis and treatment. By building the hierarchical model of internal diseases, this paper established an internal medicine diagnostic system assisted by intelligence knowledge base with the mathematical model of analytic hierarchy. The hierarchical model is able to summarize characteristics of diseases and quantize the determinant criterion of diseases. The weighted value of a possible disease can be obtained through the judgment of physicians on the weight of factors of the criterion layer and the compared calculation of database. It is concluded that the analytic hierarchy model can realize the auxiliary diagnosis function of intelligence knowledge base and the weight of a disease providing diagnostic reference for physicians.

Challenges to Engineering Management in the Big Data Era

This paper presents a review of the challenges to engineering management in the Big Data Era as well as the Big Data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into structured format and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

Design and Research of Intelligent Knowledge Push Model

In viewing of information overload environment,traditional search engine has inherent insufficiency about retrieval accuracy and query mode,together with the characteristics of decision tree algorithm and knowledge push,this paper presets an intelligent knowledge push model,and discuss the mechanism of this model.The model strive to improve the retrieval efficiency and realize the intelligent service.

IngeniousTRIZ: An automatic ontology-based system for solving inventive problems

With the development of the Theory of Inventive Problem Solving (TRIZ), different models and knowledge sources were established in order to solve different types of inventive problems. These knowledge sources with different levels of abstraction are all built independently of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the use of these TRIZ models and knowledge sources, an intelligent knowledge management system – IngeniousTRIZ was developed in this research. On the one hand, according to the TRIZ knowledge sources ontologies, this system offers to the users the relevant knowledge sources of the model they are building. On the other hand, the system has the ability to fill “automatically” the models of the other knowledge sources. Firstly, the TRIZ user chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other concept solutions are generated through ontology inference. In order to show this whole process, the resolution of the case of the “diving fin” in IngeniousTRIZ system is elaborated in detail.

Semantic Similarity Measurement Methods: The State-of-the-art

With increasing importance of estimating the semantic similarity between concepts this study tries to highlight some methods used in this area. Similarity measurement between concepts has become a significant component in most intelligent knowledge management applications, especially in fields of Information Extraction (IE) and Information Retrieval (IR). Measuring similarity among concepts has been considered as a quantitative measure of the information; computation of similarity relies on the relations and the properties linked between the concepts in ontology. In this study we have briefly reviewed the main categories of semantic similarity.

Efficient web service QoS prediction using local neighborhood matrix factorization

In the era of Big Data, companies worldwide are actively deploying web services in both intranet and internet environments. Quality-of-Service (QoS), the fundamental aspect of web service has thus attracted numerous attention in industry and academia. The study on sufficient QoS data keeps advancing the state in Service-Oriented Computing (SOC) area. To collect a large amount of resource in practice, QoS prediction applications are designed and built. Nevertheless, how to generate accurate results in high productivity is still a main challenge to existing frameworks. In this paper, we propose LoNMF, a Local Neighborhood Matrix Factorization application that incorporates domain knowledge in modern Artificial Intelligence (AI) technique to tackle this challenge. LoNMF first proposes a two-level selection mechanism that can identify a set of highly relevant local neighbors for target user. And then, it integrates the geographical information to build up an extended Matrix Factorization (MF) approach for personalized QoS prediction. Finally, it iteratively generates results by utilizing hints from previous round computations, a gradient boosting strategy that directly accelerates solving process. Experimental evidence on large-scale real-world QoS data shows that LoNMF is scalable, and consistently outperforming other state-of-the-art applications in prediction accuracy and efficiency.

Intelligent Subject and Knowledge Retrieval Algorithm Based on Ontology

For the problems of obscure demand of users, insufficiency and difficulty in correct acquisition in the process of knowledge retrieval, this paper combines with basic theory of interactive genetic algorithm to discuss its application model in the process of requirement and acquisition of knowledge retrieval. And then, it uses the interactive information produced in the retrieval process to make improvement on matching method based on concept map. It uses the interactive information produced in the retrieval process to calculate weight of users to search concept, which enhances the knowledge matching efficiency of concept map. Through test, it compares that intelligent knowledge retrieval system based on ontology has double advantages with high intelligence and efficiency.

Role of Emotional Intelligence on Job Satisfaction in University Teacher

Emotional intelligence and job satisfaction are two concepts of high importance in the modern work environment. They serve as a competitive verge in personal and organizational life. The teaching profession is one the most important profession. A teacher can mold the future every day in the class. A teacher can make the world a better place, giving young people the best start in life and can use his own education to the fullest and best extent. The knowledge of Emotional Intelligence could help teachers to harvest great advantages from their profession. Teachers in today’s education system face excessive expectations and demands such as increased accountability, negative student attitudes, low status of the profession and low paying salaries. As a result of these demands many teachers experience job dissatisfaction. The present research identified the role of Emotional Intelligence on Job Satisfaction and also assesses the level of job satisfaction in male and female teachers. Teachers were asked to fill, the Self Report Measure of Emotional Intelligence, and the Job Descriptive Index. It was hypothesized that there is a significant relationship between Emotional Intelligence and Job Satisfaction. The teacher of higher education institutes were the respondents. The collected data was analyzed by chi square and gamma test. Results demonstrate a significant association between Emotional Intelligence and Job Satisfaction. Age differences were also found as young teachers are highly Emotionally Intelligent and Satisfied with their job than older teachers. DOI: 10.5901/ajis.2014.v3n6p327

I/E Selective Activation based Knowledge Reconfiguration mechanism and Reasoning

As the role of information collection becomes increasingly important in the enormous data environment, there is growing demand for more intelligent information technologies for managing complex data. On the other hand, it is difficult to find a solution because of the data complexity and big scaled amount. Accordingly, there is a need for a special intelligent knowledge base frame that can be operated by itself flexibly. In this paper, by adopting switching function for signal transmission in the synapse of the human brain, I/E selective activation based knowledge reconfiguring mechanism is proposed for building more intelligent information management system. In particular, knowledge network design, a special knowledge node structure, Type definition, I/E gauge definition and I/E matching scheme are provided. Using these concepts, the proposed system makes the functions of activation by I/E Gauge, selection and reconfiguration. In a more efficient manner, the routing and reasoning process was performed based on the knowledge reconfiguration network. In the experiments, the process of selection by I/E matching, knowledge reconfiguration and routing & reasoning results are described.

Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate bond within the framework of intelligent knowledge management

This paper exploits the implied information of data collected from credit spreads of Chinese corporate bonds and systemic and idiosyncratic risk factors. We compute contribution of risk factors to credit spreads of Chinese corporate bonds by establishing the unbalanced panel data model, identify the key factors impacting the size of credit spreads of corporate bonds. Knowledge extracted by data mining is helpful to investors for reasonable pricing of bonds and making rational investment decisions. When selecting variables, the unbalanced panel data model is used to calculate the Zero-volatility credit spreads, which are more accurate. We use term structure adjusted return of bond index as the systemic risk factor of corporate bond market, the three Fama/French systemic factors as the systemic risk factors of stock markets and idiosyncratic stock/bond volatility and idiosyncratic bond value-at-risk as the idiosyncratic risk factors. Empirical analysis of corporate bonds sampling China’s listing Corporation issued and traded on Shanghai Stock Exchange from 2008 to 2011 shows that the size of credit spreads is mainly determined by the systemic risk factors of bond market, i.e. risk factors of stock market make very little contribution to the spread; the idiosyncratic risk factors also contribute. An interesting phenomenon is that we find that the relationship between idiosyncratic stock volatility and credit spread is negative, which is contrary to extant research while the relationship is positive and mainly focuses on impact of risk factors on credit spread of corporate bond.

On the Analysis of Web-Based Intelligent English Self-Learning System

With the development of Web-based Intelligent Learning System in the information society, we should pay attention to its usage in autonomous English learning. At first, this thesis shows the basic knowledge of Artificial Intelligence and discusses the feasibility of combining Artificial Intelligence with Web-based Intelligent English self-learning system. Second, it analyzes the combination of Expert system with Web-based Intelligent English self-learning system. Finally, it probes into that this new system will promote the revolutionary of the English self-learning mode.