Knowledge Acquisition Methodology Research Articles

Ontology-Based Methodology for Knowledge Acquisition from Groupware

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

Decision Tree Learning Approach for Knowledge Acquisition from A Medical Database.(Dept.E)

Automated knowledge acquisition (AKA) tools hold promise of preventing the process of acquiring knowledge from becoming the bottleneck in the development of knowledge base systems (KBSs). Most hospitals keep detailed records of patients containing descriptions of symptoms, diagnosis, prescribed medicine, and observed changes over time. Cumulatively, these records contain a wealth knowledge about medical diagnosis and treatment. The knowledge acquisition process would be significantly simplified if an automated system could look at these records, and extract valuable pieces of knowledge. Doctors would only need to verify the output of these knowledge acquisition tools rather than sit through hours of interview with knowledge engineers. This paper presents a new methodology for knowledge acquisition from databases. The p roposed methodology coincide with RITIO algorithm 13) in the rule induction without drawing the decision tree and in the eliminating less effective attributes. It builds the decision tree when needed. The new methodology differs from the ID3' likes algorithms (15) and RITIO since it Gods the global optimal solutions via back tracking. It is tested on standard example and applied on a real world database.

Advancing anatomy teaching by incorporating the neurophysiological concepts of learning

Gross anatomy is staring at a challenge either to transform or to disappear, if not as a science may be as a faculty, in the present era of ever-evolving medical education systems. It is a well-understood fact that retention of anatomical information is not an easy job. It is also worthwhile to mention here that technology is a great contributor in the elaborate process of teaching and learning, but a logical methodology for delivering knowledge must gain precedence over any means of technology. A scientific approach in the acquisition of knowledge shall always enhance the sensory input in the brains of the learners. The knowledge disseminated should be delivered for a larger purpose to make it useful for the humankind. The scientific methodology of knowledge acquisition implies that documented sensory input shall not be automatically converted into a repertory of facts and figures. To make the process interesting and active, the need of the hour is to develop ordered arrays of data deliverance. All modalities of scientific teaching should move ahead through interconnected phases of knowledge. The delivery of sheer facts as a description is many a times entering the brain as a mere theoretical fact with not deductive results. The accumulated facts need to be correlated with the various facts that are being thrown open by the relevant other branches to discern the underlying patterns. Each organ is to a great extent the product of the function it performs, in the present as well as in the past. We have tried to summarize the above concept, thus trying to establish the fact that “the structure is a function in space and function is a structure in time.” The neuroanatomic-physiologic approaches should play the leading role in the development of the concepts of learning of anatomy.

The transformation of methodological guidelines in a scientific research in the context of art education

The article examines the methodological guidelines in scientific research based on scientific reflection, their changes connected with the growth of science selfconsciousness and widening the forms of its reflection, the priority of axiological, anthropo and cultural centeredness in the educational and scientific environment, the transformation of the whole style of thinking based on the new paradigm of cognition, which is characterized by a general tendency for synthesis, interdisciplinarity, integrativeness, methodological pluralism. The new ideals of scientific knowledge as a combination of certain conceptual, value and methodological strategies, which form the basis of the new methodology of scientific knowledge acquisition in the context of artistic theory and practice are outlined.

A time-series clustering methodology for knowledge extraction in energy consumption data

In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd’s method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics.

Enhancing knowledge construction processes within multicriteria decision analysis: The Collaborative Value Modelling framework

This paper proposes a new Collaborative Value Modelling framework, that combines Delphi and multicriteria decision conferencing, to build widely informed evaluation models. Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to the technical soundness and meaningfulness of the methods and tools used, it is critical to design adequate social processes to promote shared understanding around key evaluation issues while capturing multiple stakeholders’ values and perspectives. Multicriteria decision conferencing processes have been typically adopted for collaborative modelling using MCDA methods in decision conferences with relatively small groups. Such a socio-technical approach has proven to be effective, in a variety of contexts, in creating a collaborative environment that enables surfacing individual beliefs, identifying common concerns, managing eventual value conflicts and promoting agreement in group model building. But, extending this framework to broader participatory contexts requires a different design of the social process, in order to ensure that model building captures the full panoply of points of view. This challenge can be tackled by enhancing multicriteria decision conferencing with an all-embracing (Web-)Delphi participatory process. We depart from the existing collaborative knowledge acquisition methodology to design, with the Delphi method, a participatory knowledge construction process that elicits and analyses individual judgemental knowledge from a (very) large and diverse number of stakeholders. The knowledge acquired is then digested by a small group of key-players, in a subsequent decision conferencing, to collaboratively develop a widely informed multicriteria evaluation model. This new Web-Delphi-decision conferencing social setting has been tested already in real complex evaluation contexts using a specific multicriteria method, the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to develop a variety of value modelling activities. We call this socio-technical design the Collaborative Value Modelling framework. Here, we describe its real use to support the construction of value functions, focusing on how the judgemental knowledge collected flows between the participatory and collaborative stages of the framework. Results validate that enhancing MACBETH decision conferencing with an ex ante Web-Delphi process fosters higher participation and collaboration in multicriteria modelling.

How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM).

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

Devising a Sketch Grammar for Academic Portuguese

This paper presents the development of a new sketch grammar designed specifically for CoPEP, a newly compiled 40-million corpus comprising texts from academic journals, tagged with Freeling v3, the default tagger available in the Sketch Engine for corpora of Portuguese. We first provide an overview and evaluation of existing sketch grammars for Portuguese, followed by a detailed description of the development of a new sketch grammar, and the presentation of some of the problems encountered. We conclude by summarizing the main findings, highlighting important implications, and offering suggestions for further improvement of the sketch grammar. More accurate and varied word sketch results than those offered by the current default sketch grammar indicate that our sketch grammar can be used for advanced lexicographic tasks such as automatic extraction of lexical data from CoPEP, the methodology of knowledge acquisition planned for the compilation of the proposed dictionary of Portuguese for university students. Moreover, this new sketch grammar can be used with any other corpus of Portuguese tagged with Freeling v3, which makes it an important resource for lexicographic and corpus linguistic research of the Portuguese language.

Comprehensible knowledge model creation for cancer treatment decision making

Background: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. Materials and Methods: An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Results: Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Conclusion: Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.

Externalising and reusing of tacit knowledge in manufacturing task

In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.

A Cyber-physical System Architecture in Shop Floor for Intelligent Manufacturing

A Cyber-physical System (CPS) architecture in shop floor is proposed for achieving the goals of intelligent manufacturing. The proposed architecture provides a guideline to construct a CPS system from the hardware interconnection, to the data acquisition, processing, and visualization, and the final knowledge acquisition and learning. Furthermore, three key enabling technologies are discussed, i.e., interconnection and interoperability among different devices, industrial big data analysis for production process management and control, and intelligent decision-making based on knowledge acquisition and learning methodology. Finally, a CPS added on a small-scale flexible automated production line in our Micro Manufacturing System Lab is taken as an example to verify the feasibility of proposed CPS architecture.

LEXA: Building knowledge bases for automatic legal citation classification

This paper presents a new approach to building legal citation classification systems. Our approach is based on Ripple-down Rules (RDR), an efficient knowledge acquisition methodology. The main contributions of the paper (over existing expert-systems approaches) are extensions to the traditional RDR approach introducing new automatic methods to assist in the creation of rules: using the available dataset to provide performance estimates and relevant examples, automatically suggesting and validating synonyms, re-using exceptions in different portions of the knowledge base. We compare our system LEXA with baseline machine learning techniques. LEXA obtains better results both in clean and noisy subsets of our corpus. Compared to machine learning approaches, LEXA also has other advantages such as supporting continuous extension of the rule base, and the opportunity to proceed without an annotated data set and to validate class labels while building rules.

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted.

MNEMO (Methodology for Knowledge Acquisition and Modelling):Definition of a Global Knowledge Management Approach Combining Knowledge Modelling Techniques

It is widely believed that the most valuable knowledge is also tacit. It is difficult to be preserved, to be made reusable and to be enhanced, because it lies in people's mind and it is hard to express and share. Knowledge engineering techniques have been adapted and used for knowledge management, especially, for the acquisition and modelling of this kind of knowledge. In this paper, we present MNEMO (Methodology for kNowledgE acquisition and MOdelling), a mixed methodological approach, which combines the strengths of two of the mentioned techniques, CommonKADS and MASK (Method of Analysis and Structuring Knowledge), both analyzed and evaluated through real applications in specific domains. The goal is to dispose of a global method, which aims to be complete and flexible in supporting the whole knowledge management cycle, from its acquisition to its exploitation and valorisation, and which can face the constant knowledge memory updating. This predisposition needs to be verified through a testing phase, which will be carried out within the field of building.

Dynamic evaluation of the development process of knowledge-based information systems

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system’s performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system—the cost of keeping a domain expert on-line—is reduced.

The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation

The knowledge acquisition (KA) process is not “mining from the expert’s head” and writing rules for building knowledge-based systems (KBS), as it was 20years ago when KA was often confused with knowledge elicitation activity, and modern engineering tools did not exist. The KA process has definitely changed. Today knowledge acquisition is considered a cognitive process that involves both dynamic modeling and knowledge generation activities. KA should be seen as a spiral of epistemological and ontological content that grows upward by transforming tacit knowledge into explicit knowledge, which in turn becomes the basis for a new spiral of knowledge generation. This paper presents some of our attempts to build a new knowledge acquisition methodology that brings together and includes all of these ideas. KAMET II, the evolution of KAMET (Cairó, 1998), represents a modern approach to creating diagnosis-specialized knowledge models that can be run by Protégé 2000, the open source ontology editor and knowledge-based framework.

SaFIN: A Self-Adaptive Fuzzy Inference Network

There are generally two approaches to the design of a neural fuzzy system: 1) design by human experts, and 2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: 1) an inconsistent rulebase; 2) the need for prior knowledge such as the number of clusters to be computed; 3) heuristically designed knowledge acquisition methodologies; and 4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved.

The methodology of knowledge acquisition and modeling for troubleshooting in automotive braking system

This study introduces a methodology of knowledge acquisition and modeling for troubleshooting in automotive braking system. It is essential for constructing a knowledge base that can be maintained and shared. A knowledge base should not merely be a set of rules, but a framework of troubleshooting that can be controlled and customized by rules. For the construction of a knowledge base, identifying the types of knowledge components to be included is a prerequisite. To identify the knowledge components, this study employs a modeling methodology consisting of three sub-models: instance model, functional model and dynamic model. In addition, mechanisms for maintaining dependency relations of the troubleshooting descriptions at different locations and reasoning using the knowledge at different locations are also developed. The system is implemented using an object-oriented programming language. The proposed methodology is applied to the troubleshooting in automotive braking system.

Articulating knowledge sharing processes in multinational e-business product design

A grounded-theory case study method was applied to a profitable multinational e-business company in the mobile communication service/product industry. The objective was to examine how new product development (NPD) subject matter specialists learned and acquired knowledge to produce innovative designs. An overarching research motive was to determine why it seemed this multinational organisation and its NPD community of practice did not apparently value contemporary university education. A brain storming technique was used in a focus group (n = 17) to capture and contrast the NPD knowledge acquisition process with educational psychology theories applied in similar e-businesses studies. Three models were created to externalise the tacit NPD processes. The unique features in the NPD models were: a knowledge continuum (innate, tacit, and explicit); and a knowledge acquisition methodology that contained three similar phases when compared to a common educational psychology theory, but the phases were ordered differently and in the opposite direction.

Learning to innovate in Nigeria's cable and wire manufacturing subsector: inferences from a firm-level case study

Firm performance is known to be connected to firm-level innovation capability. Innovation capability, in turn, is an output of technological learning. This paper evaluates technological learning among firms in the cable and wire manufacturing subsector in Nigeria, using a case firm. We developed a model of the relationship between the innovative activities of the firm – as evidence of its capability – and its knowledge acquisition methodology. These are discussed within the context of the firm's stock of human capital. We found low technological innovation capability and high capability for organisational and marketing innovation. Preparedness for technological learning is relatively poor with a staff training intensity of 5% and an innovation intensity of 0.0075%. We therefore propose stronger interconnectedness between the National Innovation System and the creation of industry-specific structures that could enhance learning.