Knowledge Engineering Research Research Articles

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.

Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

Metaknowledge Extraction Based on Multi-Modal Documents

The triplet-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.

Ontology‐Based Smart Medical Solutions

E-Health systems play a significant role in the well-being of individuals, be it as medical solutions or for health care towards the everyday well-being of individuals. New models and methods continue to be developed to produce safer health care environments. The rapid growth in the use of such approaches and technologies in medical fields has generated new opportunities for the improvement of emerging applications. However, huge challenges are yet to be resolved in order to develop consistent, suitable, safe, flexible, and power-efficient systems fit for medical needs. The development of smart medical systems and services for the health sector has become a multidisciplinary effort involving various key disciplines such as Artificial Intelligence, Machine Learning, Human–Computer Interaction, Information Technology, Data Mining, Big Data Analysis, Statistics, and Deep Learning. Smart medical solutions provide opportunities to enhance the delivery of health services, improve patient experience, and advance integrated health care. Efforts to represent knowledge efficiently have been essential to progress in various aspects of medical informatics. In particular, knowledge-based systems can improve medical systems through rule-based, case-based, and model-based reasoning through intelligent computing methods. Computational means, especially semantic technologies such as ontology-based approaches, play a central part in achieving the development of effective smart medical solutions. This special issue on “Ontology-Based Smart Medical Solutions,” in the general topic area of “e-Health and Staying Smarter,” was organized for the prestigious Wiley journal “Expert Systems: The Journal of Knowledge Engineering.” The issue was primarily intended for revised and substantially extended versions of selected papers presented at the 13th IEEE International Workshop on E-Health Systems and Web Technologies (ESAS 2018) which was organized in Tokyo, Japan. Papers covering state-of-the-art research in knowledge engineering in relation to smart medical solutions were reviewed in this special issue. The guest editors of this special issue are grateful to the authors of all papers submitted for their contributions and, to the following reviewers, for their perceptive and comprehensive reviews that allowed a very rigorous and complete review process: ABIYEV, Rahib; ANELLI, Vito Walter; ANIKEEV, Maxim; BELLI, Fevzi; BRANCO, Paula; BULUZ, Başak; BURSA, Okan; CAN, Özgü; CIMATO, Stelvio; DOĞDU, Erdogan; ERDUR, Rıza Cenk; FERRANDEZ, Jose; FREITAS, Alberto; GARRIGOS, Javier; JAIN, Divya; KANTARCI, Burak; KÖSE, Mehtap; KUT, Alp; NABIZADEH, Amir Hossein; OLCA, Emre; ÖZACAR, Tuğba; ÖZTÜRK, Övünç; PHAM, Phu; RAFIEIPOUR, Moslem; RATRE, Avinash; SANTUR, Yunus; SHEKHAWAT, Rajveer; SONG, Houbing; SONG, Jiayun; TAÇYILDIZ, Özgü; TOYGAR, Önsen; ÜNALIR, Murat Osman; YILMAZER, Dilek; ZHANG, Yudong; ZONTUL, Metin. The guest editors are greatly indebted to the EiC Jon G. Hall for his guidance in the preparation of this special issue as well as during the paper review process. The editorial assistant Albert Abdon's readiness to help out whenever needed was appreciated.

Discovering process models for the analysis of application failures under uncertainty of event logs

Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertainty, i.e., missing or noisy events in the logs. A body of research that has dealt successfully with uncertainty in event logs is process mining from the business process management community, specifically by discovering process models. The literature has shown the value of process mining across several domains, but as yet there is no study that quantifies possible improvements from using process models, and the impact of uncertainty in the context of application failures. This work addresses the use of process mining for detecting failures from application logs. First, process models are discovered from logs; then conformance checking is used to detect deviations from the models. We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. We obtain a mixed answer depending on the application under test; our measurements provide insights into the use of process mining for failure analysis.

Developing Knowledge Based Recommender System for Crop Selection

The most important role that agriculture plays in a country's political, economic and social stability makes degree of agricultural productions seriously sensitive. Agricultural production patterns vary markedly over Ethiopia according to agro-climatic conditions, in specific, broadly varying rainfall and elevation. To increase the cultivation process and improve agricultural productivity it is also require knowledge-based recommender system for crop selection, in order to advice how the farmers and experts select the crop type by considering soil texture, soil PH value, temperature, average rainfall, and other factors. Knowledge-based systems are a branch of artificial intelligence which is a computer program that attempts to replicate the reasoning processes of a human expert. It can make decisions and recommendations and perform tasks based on user input. The expert`s knowledge is accessible when the human expert might not be and so that the knowledge can be accessible at all times and in numerous places, as necessary. Cultivators (farmers) need advance or specialist knowledge to take decision during soil preparation, seed selection, fertilizer management, pesticide management, water planning, weed management etc., so that to get high yield. In Ethiopia Current cereal yields are low, by international standards, indicating growth potential, since of lack of advanced agriculture experts and shortage of agriculture facilities. In the efforts to address such problems, it is important to develop Knowledge-based system recommender system that can provide advice for farmers and agriculture professionals to facilitate crop selection process to become more productive. In this study, a prototype knowledge-based system (KBS) recommender system for crop selection is proposed. So, in order to develop the proposed recommender system an implicit and explicit knowledge used and acquired through interview, document analysis respectively. The data were collected from Gondar Agricultural Research Center which is found in Ethiopia. Knowledge Engineering research design was employed to develop the system. The prototype system reaches a good performance and meets the goals of the study.

Curadoria Digital de dados e Web de Dados: mantendo Dados Abertos Conectados para estudos bibliométricos e cientométricos

Discorre sobre os primeiros passos em direção à Curadoria Digital de índices cientométricos como Dados Abertos Conectados na Web de Dados. Por se tratar de um relato de experiência, com sua base constitutiva permeando a interdisciplinaridade, o procedimento metodológico é baseado nos ciclos de vida da Curadoria Digital proposto pelo Digital Curation Centre e do Linked Data Lifecycle proposto pelo grupo de pesquisa Agile Knowledge Engineering and Semantic. Como resultado, implementa-se o endpoint <http://lod.unicentro.br>, custodiando os recursos de dados do histórico de três índices cientométricos (Qualis, SJR e SNIP, no período 2005-2016). Diante disso, os referidos índices estão disponibilizados na Web de Dados, privilegiando o acesso, o reuso, a interoperabilidade e a processabilidade de seus recursos para com outras pesquisas bibliométricas/cientométricas. Conclui-se que, ao custodiar três conjuntos de índices cientométricos como Dados Abertos Conectados, o endpoint <http://lod.unicentro.br> permite: i) a encontrabilidade dos recursos na Web de Dados; ii) a navegabilidade entre os elementos distribuídos na web; iii) a exploração informacional do relacionamento entre os recursos disponibilizados; e iv) a replicação de resultados em estudos científicos ao longo do tempo.

Amharic based Knowledge-Based System for Diagnosis and Treatment of Chronic Kidney Disease using Machine Learning

Chronic kidney disease is an important challenge for health systems around the world and consuming a huge proportion of health care finances. Around 85% of the world populations live in developing country of the world, where chronic kidney disease prevention programs are undeveloped. Treatment options for chronic kidney disease are not readily available for most countries in sub-Saharan Africa including Ethiopia. Many rural and urban communities in Ethiopia have extremely limited access to medical advice where medical experts are not readily available. To address such a problem, a medical knowledge-based system can play a significant role. Therefore, the aim of this research was developing a self- learning knowledge based system for diagnosis and treatment of the first three stages of kidney disease that can update the knowledge without the involvement of knowledge engineer. In the development of this system, the following procedures are followed: Knowledge Engineering research design was used to develop the prototype system. Purposive sampling strategies were utilized to choose specialists. The information was acquired by using both structured and unstructured interviews and all knowledge’s are represented by using production rule. The represented production rule was modeled by using decision tree modeling approach. Implementation was employed by using pro-log tools. Testing and evolution was performed through test case and user acceptance methods. Finally, we extensively evaluate the prototype system through visual interactions and test cases. Finally, the results show that our approach is better than the current ones.

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes.

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998. To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future. We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers. We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication. There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited.

Semantic-based knowledge categorization and organization for product design enterprises

Former knowledge engineering research aimed at boosting automatic reasoning. However recent knowledge management research focused on promoting the knowledge sharing and reusing among the people. Because of the different aims between the two directions, former knowledge representation schemata, such as rule based representation, frame from knowledge engineering research does not fit to the current knowledge management scenarios. In this paper, for the purpose of building knowledge management systems for product design enterprises, knowledge items are classified into seven types based on the semantics of their usage. Then their representations are discussed respectively. Based on the above classification, a knowledge representation meta-model and a basic domain ontology reference model for cooperative knowledge management systems are put forward. The reference model is an abstraction that can be reused and extended in knowledge management systems of different enterprises. Finally, the patterns of knowledge acquisition processes in cooperative knowledge management scenarios of product design processes are studied.

A comparative study of architecture knowledge management tools

Recent research suggests that architectural knowledge, such as design decisions, is important and should be recorded alongside the architecture description. Different approaches have emerged to support such architectural knowledge (AK) management activities. However, there are different notions of and emphasis on what and how architectural activities should be supported. This is reflected in the design and implementation of existing AK tools. To understand the current status of software architecture knowledge engineering and future research trends, this paper compares five architectural knowledge management tools and the support they provide in the architecture life-cycle. The comparison is based on an evaluation framework defined by a set of 10 criteria. The results of the comparison provide insights into the current focus of architectural knowledge management support, their advantages, deficiencies, and conformance to the current architectural description standard. Based on the outcome of this comparison a research agenda is proposed for future work on AK tools.

オークランド工科大学The Knowledge Engineering and Discovery Research Institute

オークランド工科大学The Knowledge Engineering and Discovery Research Institute

An Ontology Engineering Approach For Knowledge Discovery From Data In Evolving Domains

Knowledge discovery in evolving domains presents several challenges in information extraction and knowledge acquisition from heterogeneous, distributed, dynamic data sources. We define an evolving process if the process is developing, changing over time in a continuous manner. Examples of such domains include biological sciences, medical sciences, and social sciences, among others. This paper describes research in progress on a new methodology for leveraging the semantic content of ontologies to improve knowledge discovery in complex and dynamical domains. We consider in this initial stage the problem of how to acquire previous knowledge from data and then use this information in the context of ontology engineering. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models , followed by an analysis of some of the methods and ongoing researches in the process of building ontology from databases in evolving domains, or ontology learning from databases. In the second part we describe our approach to build a framework able to enhance ontology learning and discovery from data and present future directions of our research integrating ontology and evolving connectionist systems that is being developed in the Knowledge Engineering & Discovery Research Institute -Kedri.

Argument-based applications to knowledge engineering

Argumentation is concerned with reasoning in the presence of imperfect information by constructing and weighing up arguments. It is an approach for inconsistency management in which conflict is explored rather than eradicated. This form of reasoning has proved applicable to many problems in knowledge engineering that involve uncertain, incomplete or inconsistent knowledge. This paper concentrates on different issues that can be tackled by automated argumentation systems and highlights important directions in argument-oriented research in knowledge engineering.

Knowledge engineering: Principles and methods

This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

Requirements acquisition

An overview of knowledge engineering research, practice, theories, methodologies, and tools is presented, and parallels are drawn with analogous phenomena and activities in requirements engineering. Knowledge-based systems are distinguished from other advanced information systems by their reflective emphasis on meta-information processing about the basis of system operation. In terms of requirements elicitation, this corresponds to an emphasis on maintaining an audit trail from requirements through design, implementation, use and maintenance that supports continuing user involvement in system specification, design and evolution. Examples of knowledge elicitation methodologies and tools are provided, and it is suggested that they all have some applicability in requirements elicitation for advanced information system development. It is concluded that closer collaboration between the knowledge engineering and requirements engineering communities will be mutually beneficial.

Knowledge and data engineering

The authors provide an overview of the current research and development directions in knowledge and data engineering. They classify research problems and approaches in this area and discuss future trends. Research on knowledge and data engineering is examined with respect to programmability and representation, design tradeoffs, algorithms and control, and emerging technologies. Future challenges are considered with respect to software and hardware architecture and system design. The paper serves as an introduction to this first issue of a new quarter.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Knowledge engineering for medical decision making: A review of computer-based clinical decision aids

Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques.