Knowledge Acquisition Procedure Research Articles

A Novel Quantum Neural Network Approach to Combating Fake Reviews

AbstractAs e-commerce has grown gradually online item assessments have emerged as a key source of consumer data. That stated, there are problems with the consistency and fictitiousness of the evaluations because there are numerous fake or fraudulent ones. These misleading assessments are generated during the investigation in an attempt to mislead customers about the nature of a real advantage, compromising their ability to make a predetermined decision and damaging the reputations of businesses. A cutting-edge interrogation department revealed that quantum machine learning (QML) could manage a huge amount of machine-trained data and could convey almost emotional choices in the context of inaccurate checks. It is truly beneficial in obtaining reviews for things that are incorrect. Opinion, generating trends, interpersonal relationships, and assessing fatigue is merely a few of the attributes. Tests conducted utilizing the Amazon fraudulent review. The dataset demonstrates that QML tactics outperform conventional knowledge acquisition procedures in errands, including the place of fraudulent reviews. The delicacy and tolerance of incorrect review distinguishing evidence can be significantly advanced, although QML is still in its early stages of development. Both our proposed model and model pass rigorous conventional machine learning algorithms testing with a remarkable level of accuracy. An article introduces a unique approach to fraudulent review detection and brings in the QNN algorithm as a solution. A deep learning model, such as this one, has an 86% accuracy rate in quantum computer implementation, which is an impressive level of innovation, especially if it comes with successful results. Involvement in these cutting-edge technologies promises significant benefits in battling the problem of false identities on the Web. In our case, the experimental results demonstrate that our QNN algorithm, which can accurately identify fake reviews, will become a key weapon for suppressing various forms of fraudulence on emerging digital technology platforms.

Knowledge acquisition and representation for intelligent operation support in offshore fields

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems’ instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support.

ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients\u2019 disease history

Given the rapid rate at which text data are being digitally gathered in the medical domain, there is growing need for automated tools that can analyze clinical notes and classify their sentences in electronic health records (EHRs). This study uses EHR texts to detect patients’ disease history from clinical sentences. However, in EHRs, sentences are less topic-focused and shorter than that in general domain, which leads to the sparsity of co-occurrence patterns and the lack of semantic features. To tackle this challenge, current approaches for clinical sentence classification are dependent on external information to improve classification performance. However, this is implausible owing to a lack of universal medical dictionaries. This study proposes the ETM (enrichment by topic modeling) algorithm, based on latent Dirichlet allocation, to smoothen the semantic representations of short sentences. The ETM enriches text representation by incorporating probability distributions generated by an unsupervised algorithm into it. It considers the length of the original texts to enhance representation by using an internal knowledge acquisition procedure. When it comes to clinical predictive modeling, interpretability improves the acceptance of the model. Thus, for clinical sentence classification, the ETM approach employs an initial TFiDF (term frequency inverse document frequency) representation, where we use the support vector machine and neural network algorithms for the classification task. We conducted three sets of experiments on a data set consisting of clinical cardiovascular notes from the Netherlands to test the sentence classification performance of the proposed method in comparison with prevalent approaches. The results show that the proposed ETM approach outperformed state-of-the-art baselines.

Automatic knowledge extraction of any Chatbot from conversation

Acquiring knowledge for conversation modeling is an important task in the process of building a Conversational Agent (Chatbot). However, it is a quite difficult process that requires too much time and efforts. To overcome these difficulties, in this paper, we demonstrate a novel methodology for the automatic conversational knowledge extraction from an existing Chatbot. Extracted knowledge will be used as training dataset for building a Neural Network Conversational Agent. The experiments in the paper show that our proposed approach can be used for the automatic knowledge extraction from any type of Chatbot on the Internet. The experiment that is presented in this paper has two phases. In the first phase, we present a methodology for the conversational knowledge extraction. In the second phase of the experiment, we introduce a methodology for building a new Neural Conversational Agent using a deep learning Neural Network framework. The key novelty of our proposed approach is the automated machine-machine conversational knowledge sharing and reuse. This is an important step towards building the new conversational agents skipping the difficult and time-consuming procedure of knowledge acquisition.

Dialéctica y ciencia en Aristóteles

The dialectical method of reasoning, constituted by opposed opinions, is the procedure of knowledge acquisition and of foundation of principles for practical and theoretical sciences. The demonstration from the principles of science, which classically is considered the proper procedure of science, does not have more committing than to systematize pedagogically what previously has been obtained by the dialectic method.

Knowledge acquisition for diagnosis model in wireless networks

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G) and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e. those that self-configure, self-protect, self-heal and self-optimize) are the solution to tackle the high complexity inherent to these networks. This paper proposes a probabilistic model for self-healing in the radio access network (RAN) of wireless systems. The main difficulty in model construction is that, contrary to other application domains, in wireless networks there are no databases of previously classified cases from which to learn the model parameters. Due to this reason, in this paper, a knowledge acquisition procedure is proposed to build the model from the knowledge of troubleshooting experts. In order to support the theoretical concepts, a model has been built and it has been tested in a live network, proving the feasibility of the proposed system. Additionally, a knowledge-based model has been compared to a data-based model, showing the benefits of the former when the number of training cases is scarce.

Attribute exploration with background knowledge

We give the mathematical theory of a simple knowledge acquisition procedure based on implications and counter examples, and show examples of application.

Cold forging process design based on the induction of analytical knowledge

In this paper, a cold forging process design method based on the induction of analytical knowledge is proposed. The analysis engine, which is a finite-element-based program, is used to analyze various multi-stage cold forging processes based on pre-defined process condition parameters and tooling geometry. According to the simulation results, a knowledge-acquisition procedure is instituted, i.e. a neural network model is constructed, in which the multi-layer network and the back-propagation algorithm are utilized to learn the training examples from the simulation results. In the last part of this paper, an industrial case study for the multi-stage cold forging process design of a low-carbon steel speaker tip is discussed. The optimal process condition parameters, such as the preform punch geometry and the preform punch stroke are decided, based on the requirement of homogeneous plastic deformation of the cold-forged product. The proposed method is useful for the shop floor to decide the cold forging process parameters for producing a sound product within the required minimum quantity of the die set.

Are artificial neural networks black boxes?

Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

Managing context in enterprise knowledge processes

Because competitive advantage accrues to those enterprises that effectively manage their ‘knowledge’, researchers are seeking a viable organizational epistemology. This paper addresses one epistemological challenge - the slippery but critical notion of ‘context’ - by presenting and substantiating four claims. First, as ‘that which imparts meaning’, context is inherently important to epistemological enquiry generally and enterprise knowledge processes specifically. Second, context is a key issue in three areas of current enterprise (re-) engineering and ‘organizational learning’ research: (a) systemic theories; (b) knowledge acquisition tools; and (c) conversation management. Third, a systemic perspective requires redefining context as a process ( contexture) embedded in a system's intrinsic operational ‘situatedness’. Finally, this shift of perspective can be practically implemented through innovative enterprise knowledge acquisition procedures. One such innovative procedure ( nichepicking) is illustrated by a case study.

The Construction of Work in Artificial Intelligence

Although technology is often viewed as value-free, an anthropological perspective suggests that technological tools embody values and assumptions of their builders. Drawing upon extended field research, this article investigates the construction of work in the expert systems community of artificial intelligence (AI). Describing systematic deletions in practitioners' representations of their own work, the article relates these to both the selectivity of conventional knowledge acquisition procedures and the tendency of expert systems to (in the practitioners' words) "fall off the knowledge cliff." Although system builders see the latter problem as purely technical, this article suggests that it is also the result of nontechnical factors, including the system builders' own tacit assump tions. This article supports the view that technology has a cultural dimension.

VIDES: an expert system for visually identifying microfossils

The design of a visual identification expert system (VIDES) that uses a visual approach to help identify microfossils down to the species level is discussed. VIDES combines a graphical expert-system shell with a set of knowledge bases to help identify microfossils, reducing the time and cost associated with petroleum exploration. VIDES's initial knowledge-acquisition procedure, automated knowledge acquisition, textual and visual knowledge representation, automatic knowledge-base generation, inference mechanism and graphical interface are described. >

Knowledge acquisition and information searching strategies in diagnostic decision making

The effect of there learning procedures on diagnostic information searching strategies was investigated. Undergraduate students acquired an artificial knowledge domain through either a Taxonomic-, Inductive-, or Case-oriented knowledge acquisition procedure. The use of the competing-hypotheses heuristic as a practical strategy to guide decision making in the face of uncertainty, was compared among the three learning conditions and between each condition and a non-learning control group using the method developed by Kern and Doherty (1982) and Wolf et al. (1985). The small instruction intervention had an effect on the diagnostic nature of subjects' information-searching strategies. Subjects in the Inductive-learning condition exhibited a stronger tendency to seek diagnostically worthless information than the other learning conditions. The outcomes are considered relevant to the nature of expertise in decision making, and to the effect of instruction methodologies on the knowledge representations available to diagnostic classification and decision-making processes.

Knowledge acquisition for effective and efficient use of engineering software

The problem of effective and efficient use of engineering software can be thought of as a Pareto optimal problem. However, the complexity of modern engineering software precludes the possibility of acquiring complete knowledge of the software's Pareto optimal set. Instead, heuristic knowledge must be acquired. To this end, the paper proposes a knowledge acquisition procedure. The use of a knowledge acquisition system, which may be computerized, forms an integral part of this procedure. An illustrative example is provided.

The value of cognitive foundations for dynamic social theory

It is possible to develop models of social behavior that are predicated on detailed mechanical models of cognition. Cognitively based social models are potentially unified theoretical frameworks that can be used to explain a wide variety of social phenomena. Moreover, if a knowledge representation scheme and a knowledge acquisition scheme are specified in the underlying cognitive model then it is possible to produce a dynamic social model. The resulting social model can thus be used to predict and explain not only conditions for specific behaviors but changes in those behaviors over time. Constructuralism is a theory of social behavior that rests on a cognitive model. The cognitive model specified has a knowledge representation scheme, knowledge acquisition procedures, and control procedures for shifting cognitive attention. The resulting social model is a dynamic model that can be used to explain both conditions for the occurrence of a behavior and social and individual changes that accrue do to a series of behaviors. The explanatory breadth of the model is illustrated by looking at predictions about a variety of social phenomena including: development of shared knowledge, identical behavior by members of the society, foreign language acquisition, clique formation, civil disobedience, and diffusion of innovative information.

Some problems on uncertain knowledge acquisition for rule based systems

The problem of uncertainty management in systems based on rules can be dealt with using several methodologies. A particularly significant and well-founded one is based on Dempster-Shafer's Theory of Evidence. This methodology is based on the hypothesis that the values of an attribute for a certain object are mutually exclusive. This hypothesis is often too restrictive and the present work proposes a procedure of knowledge acquisition which allows for the application of the D-S theory even in cases where the objects are described, for example, in a liguistically ‘conventional’ manner, where such a constraint would not be satisfied. This paper introduces the concept of knoxel and relates also to other problems which are of particular interest for the use of complex networks based on knoxels.

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.

The use of alternative knowledge-acquisition procedures in the development of a knowledge-based media planning system

The knowledge-acquisition procedures used in developing a knowledge-based media planning system are discussed. The approach used in developing the system and the resulting system will have a number of unique characteristics. First, in developing the system, we first constructed a system which we call a decision frame. This system structures the problem for the media planner and contains little expertise. We are currently adding expertise to the system so that our final system will be able to operate both as a decision frame and as an expert system. A number of different knowledge-acquisition procedures are currently being used to obtain the requisite knowledge from media planners. These include: (1), elicitation procedures; (2), problem-sorting techniques; (3), protocol analysis; and (4), having experts use the decision frame system to acquire knowledge and to determine the validity of the system.

A Methodology for Knowledge Acquisition and Reasoning in Failure Analysis of Systems

The problem of modeling knowledge about the fault behavior of a system and utilizing this model for reasoning about and diagnosing failures is addressed. A solution that merges graph and fault-tree-based failure analysis with rule-oriented reasoning is presented. Failure analysis is divided into two phases, a failure source location phase and a failure cause identification phase. Each phase consists of a failure model and a process that operates on it. The failure models for the first and second phases are based on lesel-structured fault propagation digraphs and augmented fault trees, respectively. The augmented fault tree (AFT) is a conceptual structure that encodes probabilistic, temporal, and heuristic information in addition to the causal aspects of failures modeled by conventional fault trees. The two models are combined to form a novel hierarchical failure knowledge representation scheme. Upper levels of this hierarchy are made up of the fault propagation digraphs. Each level represents a view of the system under a particular granularity, and the granularity increases with levels. This feature permits control over the resolution of fault diagnosis. The lowest level consists of a set of cause-consequence knowledge bases containing production rules. These production rules are derived from augmented fault trees and represent the cause-effect relations among failure events that lead to the corresponding subsystem's failure. A knowledge acquisition procedure to generate these failure models and failure analysis processes that operate on them are described. The methodology proposed is inherently parallel as the processes may operate on different levels independently.

Relationships Between Knowledge and Learning Environments in Teacher Education

The education of teachers, asserts the author, involves three categories of knowledge: particularistic, technical and liberal. Particularistic knowledge, a prod uct of direct experience, is what pros pective teachers understand as a result of their multiple exposures to class rooms and teachers. Technical knowl edge, the body of skills and understand ings that constitute the knowledge base undergirding teaching, results from laboratory instruction in which preser vice teachers practice and reflect on methods and skills. Liberal knowledge, more universal in character, is composed of the concepts and generalizations as sociated with the disciplines. Liberal knowledge is applicable to the broadest social contexts and contains the most objective truth value. The three knowledge areas are differ entially associated with field, laboratory and scholarly/scientific learning environ ments. Effective teacher training matches knowledge acquisition proce dures with appropriate learning environ ments.