Knowledge Engineering Process Research Articles

A hybrid approach to analysing large scale surveys: individual values, opinions and perceptions

High-dimensional large scale surveys enable broad research capabilities and potential insight. However, when dealing with the intrinsic complexity of social science, the underlying knowledge engineering process may play a critical role and require to consider the characteristics and peculiarities of a given problem in context. This study proposes an analysis framework based on clustering techniques, which have been applied to discover patterns among a number of abstracted features resulting from selected attributes of the World Values Survey (WVS). As an assumption, such features have been softly classified as values, opinions and perceptions, based on their theoretical likelihood to change along the time. From a more philosophical perspective, this work assumes hybrid practices as there is no pre-formulated theory but rather an attempt to discover patterns and new knowledge from data. Given the relatively manageable dimensionality of the input dataset, the feature selection has been performed according to an application-oriented approach (rather than driven by statistical analysis) to establish a more comprehensive and consistent research framework. Among the main findings, a symbiotic relationship between the perception of satisfaction and of financial stability, as well as between the perception of security and of happiness, in addition to more complex patterns involving traditional values (e.g. family and religion), politics and believes with an impact on society. Last but not least, despite its holistic focus, the study has allowed the identification of few research gaps and, therefore, potential further research direction in the broad domain of Social Sciences.

Networks method of hybrid construction and support of data transmission routes in programmed tools of mobile radio communication based on neural networks

The article proposes a method of hybrid construction and support of data transmission routes in programmable means of mobile radio communication based on neural networks. Taking into account the peculiarities of the operation of mobile radio communication networks and means of communication, the article proposes to intellectualize the processes of managing data flows by using knowledge processing technologies in the construction of a control system. The construction of the control system is based on the use of neural networks, the methodology proposes three main steps of its construction, namely the method of hybrid construction of data transmission routes, the method of maintaining data transmission routes and modeling the process of construction and maintenance of data transmission routes in the network using neural networks. Unlike similar methods, which: are used in stationary communication networks with a small number of end nodes, without the possibility of monitoring the functions of data transmission routes; which are characterized by the absence of a clear decision-making hierarchy and the presence of unpredictability when applied in data transmission systems of mobile radio communication networks; do not take into account the peculiarities of the functioning of mobile radio communications. The proposed technique is able to build data transmission routes, increases the time of network operation and reduces the loading of network channels based on the application of a hybrid structure of neural network construction, by minimizing service traffic in the information direction; selection of the type, number and method of control of data transmission routes; introduction of the hierarchy of the decision-making process for the construction of the data transmission route and the selection of data parameters, taking into account the characteristic features of the use of programmable mobile communication tools to ensure the given quality of network service under different conditions of operation of mobile radio communication tools, using neural networks.

Modern linguistic resources and IT applications for the Tatar language: Regional practices

The issue of preserving the language of an ethnic group as a language of communication, as well as a carrier of cultural codes, has now acquired particular urgency and relevance. How to preserve and develop a language in the conditions of globalization and widespread digitalization, in the conditions of coming, and in many ways already real, information chaos and in conditions when in the near future most of the known languages are threatened with complete extinction. Obviously, one of the reliable and promising solutions is embedding a language in the digital space as a language of accumulation, processing and transmission of information; a language of communication in the global Internet network. Currently, in the Republic of Tatarstan, a set of organizational and policy measures has been almost completely implemented and the necessary information and technological base has been created to ensure full functioning of the Tatar language in computer technologies, including business and management spheres, publishing, educational activities, linguistic research and development. Another effective and very promising activity is the research and use of the language as a cognitive-conceptual tool that can enrich the mental and spiritual space of civilization as a linguistic-semiotic resource that has the necessary potential for creating new knowledge processing technologies and an intelligent interface. This paper presents the experience of research and practical activities of the Institute of Applied Semiotics of the Academy of Sciences of the Republic of Tatarstan in one of the important areas of artificial intelligence called “Natural Language Processing”. Research and development are carried out in three theoretical and applied areas: 1) Tatar national localization of infocommunication technologies, 2) Development of software tools, systems and technologies for natural language processing and creation of linguistic resources, 3) Creation of new intelligent information processing technologies based on the research the cognitive potential of the Tatar language.

Guest Editorial Machine Learning in Antenna Design, Modeling, and Measurements

Machine learning (ML) is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. As a modern data-driven optimization and applied regression methodology, ML aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy and/or efficiency by discovering and exploiting regularities in training data. Indeed, many ML and data-driven methods, such as conventional artificial neural networks (ANNs), were introduced and studied within electromagnetics a few decades ago. However, these past studies did not benefit from the most recent advances in ML, which have been driven by the present confluence of improved hardware performance at lower cost, advanced network algorithms and architectures, data science, and considerable efforts dedicated to advancing the computational electromagnetics (CEM) benchmark. Today, a broader family of ML techniques based on ANNs has been developed. Examples include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network, generative adversarial network, and deep reinforcement learning, which have been successfully applied to different engineering and science problems, ranging from image and video recognition, social media services, virtual personal assistant to autonomous vehicles, to name a few. This naturally suggests that applying ML to real-world electromagnetic problems could be one of the emerging trends in ML and artificial intelligence (AI) <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> . Indeed, ML has been becoming an important complement to existing experimental, computational, and theoretical aspects of electromagnetics.

Model of data flow control subsystem of the manet class mobile radio network control system

The article proposes a model of the data flow control subsystem of MANET class mobile radio control system to evaluate the effectiveness of the model, which will be built based on the developed methods and techniques (corresponding units). The essence of the model is to apply appropriate methods of DFCS control, which interact with each other and other functional subsystems within the operation of the subsystem units and their interaction, based on the target function. To ensure effective control of a mobile radio network in conditions of frequent changes of the situation (operational, tactical, communication, etc.) is possible only if there is a nodal control system capable of making control decisions in conditions of uncertainty. In addition, provision of the ability of mobile radio networks to self-organization, as well as adaptation of its nodes to different operating conditions requires the presence of a training subsystem as part of the node control system. To ensure effective control of a mobile radio network in conditions of frequent changes of the situation (operational, tactical, communication, etc.) is possible only if there is a nodal control system capable of making control decisions in conditions of uncertainty. In addition, provision of the ability of mobile radio networks to self-organization, as well as adaptation of its nodes to different operating conditions requires the presence of a training subsystem as part of the node control system. Considering the peculiarities of the functioning of MANET class radio networks, the article proposes to intellectualize the control processes by using knowledge processing technologies in the construction of a node control system. Based on the integrated use of fuzzy logic and neural networks, the probabilities of violations are considered, which, depending on the parameters of the state of the nodes, allow selecting the required value of the violation of the state of the network. Unlike existing models that are used in computer or fixed networks that do not require resource-intensive structure, in this model it is proposed to develop the structure of the DFCS to the requirements and features of usage in the mobile radio network. The application of the model will allow: to simplify and improve the structure of the construction of the data flow control subsystem; to process incomplete, inaccurate, unpredictable, different in its physical content data; to reduce the time of making a control decision. The further directions of research will be the development of the method of connectivity of the elements of the data flow control subsystem of the mobile radio network control system.

The Development of a MOOCs Knowledge Repository System Using a Digital Knowledge Engineering Process to Enhance Digital Entrepreneurs’ Competencies

The objectives of this research were as follows: the development of a MOOCs knowledge repository system using a digital knowledge engineering process, and a competency assessment of digital entrepreneurs engaging in a MOOCs knowledge repository system using a digital knowledge engineering process. A total of 30 people were selected as a sampling group (purposive sampling) for this study, these included Small and Medium Enterprises (SMEs) in Bangkok with expertise in Human Performance Technology (HPT), as well as the MOOCs knowledge repository system. The mean, standard deviation, percentage, and a dependent sample t-test were used in the procedure for data analysis. The research findings suggested that: (1) the overall result concerning the development of a MOOCs knowledge repository system using a digital knowledge engineering process was at the highest level (Mean = 4.89, S.D. = 0.31), and (2) the overall result regarding the competencies of digital entrepreneurs after engaging in a MOOCs knowledge repository system using a digital knowledge engineering process passed the 80% rating, according to criteria. Moreover, learners who had undertaken activities through a MOOCs knowledge repository system using a digital knowledge engineering process improved their learning outcomes with a significance level of .05 based on the research hypothesis.

Evaluation of risk causing factors for the incidence of neck and shoulder pain in adolescents using fuzzy analytic hierarchy process

The incidence of neck and shoulder pain is recurrent in adolescents, constituting a large socioeconomic challenge and is responsible for substantial personal and social impacts; requiring intensive research to identify potential causes and their precedence in pain occurrence. This study's intent is to explore the prevalence of neck and shoulder pain among adolescents and to examine the risk factors associated with the hazard of the pain. Through knowledge engineering process, risk factors identified are physical-, psychological-, psychosocial-, behavioural-, emotional- and sedentary-related. A fuzzy analytic hierarchy process method is applied to prioritise the risk factors and determines its precedence in pain incidence among the adolescents, categorised based on their ages as early, middle and late adolescence group (between 10-17, 18-21 and 22-24 respectively). The results indicate that the middle adolescence category have a significantly greater chance for pain occurrence. The outcomes will support in decision making and planning for the youth's welfare.

ТЕНДЕНЦІЇ РОЗВИТКУ ШТУЧНОГО ІНТЕЛЕКТУ В УКРАЇНІ

In recent years, artificial intelligence technologies have begun to be used in many types of human life: education, medicine, business, finance, management, marketing, industry, etc., and have become a key trend of the time. The article provides information and analyzes the current state and trends in the development of artificial intelligence in Ukraine. An analysis of the strategy for the development of artificial intelligence, which is proposed by a number of ministries, considered the concept of development of artificial intelligence, which is proposed by the State Agency for e Government. Data on companies working in the field of artificial intelligence are analyzed and provided. The main activities of Ukrainian companies in the field of artificial intelligence, including a number of Ukrainian startups, have been identified. Provides information on scientific schools and research conducted in the field of artificial intelligence in Ukraine. It is established that the main research focuses on the following areas: neural networks, pattern recognition, mathematical informatics, creation of intelligent information systems and knowledge bases, development of intelligent robotic systems, knowledge-oriented technologies, intelligent search, development of intelligent learning systems, big data analysis, fuzzy systems decision support, multi-agent systems and much more. In general, scientific research conducted in Ukraine in the field of artificial intelligence covers the basis of areas of work that correspond to research in the world. Also in the article the directions of dissertation researches for the last years on intellectual systems and systems of artificial intelligence are analyzed. The main areas in which the dissertation research was performed: intellectual analysis and data processing, knowledge processing and multi-agent technologies, intelligent technologies, including intelligent technologies to support acceptance, image recognition and a number of others.

Research on the Construction Technology of Knowledge Graph in Aviation

In order to alleviate the overload of airspace information under the network environment and integrate the scattered and useful airspace information, the technical architecture of knowledge graph in the aviation field is proposed. This paper describes the technical architecture of aviation knowledge graph construction, including knowledge extraction, knowledge fusion, knowledge processing, knowledge update and other key technologies and applications. Finally, an instance of knowledge graph for air combat is constructed. The knowledge graph discovers the hidden relationship between task and maneuver through resource sharing and information mining, which can be used for mission optimization and maneuver prediction to assist air combat decision-making.

Knowledge process of health big data using MapReduce-based associative mining

Big-data knowledge processing technology facilitates efficient health management services by systematically collecting and promoting information using distributed/parallel processing with the health platform’s common data model. Thus, it enables knowledge expansion for healthcare data. In this study, we propose a big-data knowledge process for the health industry using Hadoop’s MapReduce software for association mining. The proposed method provides efficient health management knowledge services by collecting and processing heterogeneous health information using WebBot and the common data model. Hadoop is a proprietary method of effectively processing distributed big data. It is a knowledge processing model that combines MapReduce-based distributed processing and a method of finding mining-based associations. The input data in MapReduce is extracted from chronic disease nomenclature from health big data. The corpus divides big data into several blocks of a certain size, creating map tasks. Through the map function of the mapper of each map task, sets composed of pairs of a key and a value are created. In the map process, a key is created using the same method used for a frequent item set of the Apriori algorithm. The key is a set of 2p keys and its value is set to the occurrence frequency of the key. By summing up the values of the same keys by combining, the size of data is decreased and the load of a software program is also decreased. In addition, for each key, the reducer is designated through hash partitioning and stored in the reduce task. In the reduce process, the results of the map are allocated to each reducer, and alignment and merge steps are taken based on the keys. For the same |key|, the values are summed up by performing the reduce function. In this instance, keys whose values fail to meet the minimum support criterion are eliminated. Therefore, from a set of , a frequent item set that meets the minimum support criterion is extracted. The association rules between datasets constituting the frequent item set are determined, and the support and reliability are calculated to examine whether they are actually associated. As the value of the frequent item set is higher, the support and reliability are also higher. Thus means that the association is obvious. A knowledge base is then constructed using the extracted association rules by repeatedly performing the MapReduce process. Closely associated knowledge bases are created and semantically related in real time with high probability. Furthermore, mining-based knowledge processing of health big data infers more meaningful associations between chronic diseases. The proposed method adds technological value and intelligent efficiency to support the health and medical fields promote healthy lives.

A new hierarchical approach to requirement analysis of problems in automated planning

The use of Knowledge Engineering (KE) processes to analyze and configure domains in automated planning is becoming more appealing since it was noticed that this issue could make a difference to solve real problems. The contrast between a generic domain independent approach, taken as canonical in AI, and alternative processes that include knowledge engineering – eventually adding specific knowledge – has been discussed by Computer and Engineering communities. A big impact has been noticed mainly in the early phase of requirement analysis when KE approach is normally introduced. Requirement analysis is responsible for carrying out the Knowledge modeling of both problem and work domains, which is a key issue to guide different planner algorithms to come out with efficient solutions. Also, there is the scalability issue that appear in most real problems. To face that, hierarchical methods played an important hole in the history of planning and inspired several solutions since the proposal of NONLIN in the 70’s. Since then, the idea of associating hierarchical relational nets with partial ordered actions has prevailed when large systems were considered. However, there is still a gap between the hierarchical approach and the state of art of requirements analysis to allow features anticipated by KE approach to really appear in the requirements of a planning process. This paper proposes a pathway to solve this gap starting with requirements elicitation represented first in the conventional semi-formal (diagrammatic) language – UML – that is translated to Hierarchical Petri Nets (HPNs) by a new enhanced algorithm. The proposed process was installed in a software tool – developed by one of the authors – that analyzes the performance of the KE planning model: itSIMPLE (Integrated Tools Software Interface for Modeling Planning Environment). This tool was initially designed to use classic Place/Transition nets and an old version of UML (2.1). It is now enhanced to use UML 2.4 and a hierarchical Petri Net extension, also developed by the authors. Realistic examples illustrate the process which is now being applied to larger problems related to the manufacturing of car sequencing domain, one of challenge of ROADEF 2005 (French Operations Research & Decision Support Society). Finally, we consider the possibility to introduce another approach to the KE process by using KAOS (Keep All Object Satisfied) to make the planning design more accurate.

A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations

Innovative technology on intelligent processes for smart home applications that utilize Internet of Things (IoT) is mainly limited and dispersed. The available trends and gaps were investigated in this study to provide valued visions for technical environments and researchers. Thus, a survey was conducted to create a coherent taxonomy on the research landscape. An extensive search was conducted for articles on (a) smart homes, (b) IoT and (c) applications. Three databases, namely, IEEE Explore, ScienceDirect and Web of Science, were used in the article search. These databases comprised comprehensive literature that concentrate on IoT-based smart home applications. Subsequently, filtering process was achieved on the basis of intelligent processes. The final classification scheme outcome of the dataset contained 40 articles that were classified into four classes. The first class includes the knowledge engineering process that examines data representation to identify the means of accomplishing a task for IoT applications and their utilisation in smart homes. The second class includes papers on the detection process that uses artificial intelligence (AI) techniques to capture the possible changes in IoT-based smart home applications. The third class comprises the analytical process that refers to the use of AI techniques to understand the underlying problems in smart homes by inferring new knowledge and suggesting appropriate solutions for the problem. The fourth class comprises the control process that describes the process of measuring and instructing the performance of IoT-based smart home applications against the specifications with the involvement of intelligent techniques. The basic features of this evolving approach were then identified in the aspects of motivation of intelligent process utilisation for IoT-based smart home applications and open-issue restriction utilisation. The recommendations for the approval and utilisation of intelligent process for IoT-based smart home applications were also determined from the literature.

A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors.

Knowledge Engineering Processes and Tools in Enter-prise Environments A Systematization Attempt

Knowledge Engineering Processes and Tools in Enter-prise Environments A Systematization Attempt

Knowledge Engineering Tools and Processes in Enterprise Environments

Despite a lot of persuasive scientific studies on the benefits of semantic technologies, Knowledge Engineering (KE) in enterprise environments is far away from wide practical implementation. The consultancy Gartner locates “Enterprise Taxonomy and Ontology Management" in its 2016 Hype Cycle for Emerging Technologies on the half way down to “Through of Disillusionment" with an expectation of more than 10 years to mainstream adoption. The paper aims at analyzing this contradiction and at investigating systematically the obstacles for successful and sustainable KE in enterprises. This is based on two case studies and an ethnographical survey in organizations from different sectors: IT services and software production, public administration, and life sciences. For analyzing and visualizing characteristic use cases, processes and roles, methods of system analysis are applied; among others the triad of business process modeling BPMN, CMMN and DMN. A focus is put on the analysis and systematization of tools and services available for KE. The results of the paper form a basic framework for the constitution of KE as a business function crucial for bringing semantic technologies in enterprise environments to life.

BOYOLALI MILK CENTRE DENGAN PENDEKATAN GREEN ARCHITECTURE

&lt;p&gt;&lt;em&gt;Design of Boyolali Milk Centre are motivated by the low numbers of milk consumption, the relatively low price of milk from dairy farmers to collectors, the lack of public knowledge processing technology products, and waste treatment. The mission of this project is to develop a major potential Boyolali to attract people who are still low in the milk through educational tours, so as to raise the living standards of farmers to absorb the results of local milk, milk-based industry to develop products, and as one of the tourist facilities that concept green (green). Design objectives are: to design a building that can facilitate information, educational tours, and marketing of milk based on the concept of Green Architecture. Design issues are: how the concept of green architecture can be translated to the expression of design forms an attractive building, reflecting the dynamic character of the milk, and contextuality of the environment (eco-friendly). The method used is a method of designing the Green Architecture (Green Architecture). The results obtained in the form of the design of an educational tour of the building named Boyolali Milk Centre which has never existed, consisting of dairy information center, museum milk, green pilot model dairy farm, green farm, mini plant / milk-based agro-industry, marketing center in the form of resto and milk shop, as well as play and sports facilities.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; Education Travel, Green Architecture, Information Center, Milk Centre.&lt;/em&gt;&lt;/p&gt;

Linguistic fuzzy consensus model for collaborative development of fuzzy cognitive maps: a case study in software development risks

Fuzzy cognitive maps (FCMs) have been widely used in several domains for information processing, such as, data fusion, decision making. Although several methods to automatically learn FCMs are recognized from the scientific literature, the most used approach to build an FCM relies on a collaborative task involving single person or, more suitably, group of experts. Collaborative development increases reliability and robustness of the resulting FCM, but rises some problems in terms of group decision making to aggregate different perspectives of the problem representation. This paper proposes to support collaborative development of FCMs introducing knowledge engineering process that relies on Linguistic Fuzzy Consensus Model. In the proposed approach, each expert builds the own version of the FCM. When all different versions are available, a Group Decision Making process is activated in order to reach the consensus on conflictual modeling opinions. The result is a unique final version of the FCM that is not a simple aggregation of the versions provided by the experts but is the result of a well-suited mathematical model. In addition, this work adopts consensus model with incomplete preference relations scheme to address knowledge harmonization issues. Finally, advantages and the limitations of the proposed framework are argued.

RANCANG BANGUN SISTEM PAKAR PENANGGULANGAN PENYAKIT KEDELAI

Early detection and identification of soybean diseases is important to support better productivity of soybean. The demand for the availability of an expert on soybean disease is very high, especially for the beginners in the field of agriculture. However, the number and time allocation of the experts are not adequate to serve farmers located in different geographical areas. Therefore, an expert system is proposed as a solution to use as a diagnostic tool for soybean diseases just like a human expert. It will be even easier when the system is implemented into an Android-based application to be used anywhere and anytime. The objective of this study was to analyze and design an expert system for early identification of soybean diseases. This study was adopting the Expert System Development Life Cycle (ESDLC) approach. The stages were project initialization, knowledge engineering process, and implementation. The study was started with the project initialization phase that conducted in September 2014 and the completion of the implementationphase in August 2015. The results of research were in the form of document analysis and prototype system.

An Interactive Narrative Format for Clinical Guidelines

Clinical guidelines are standardised documents, which summarise best practice in complex medical situations. Their target audience comprises health professionals, or in some cases patient groups, for whom they constitute important sources of patient education. These documents are characterised by a rich knowledge content, which also relies on a complex, largely implicit background. At the heart of guidelines is a set of recommendations describing expected behaviour throughout specific, evolving contexts. Such complex documents can be challenging to assimilate, in particular their patient education versions. The need to contextualise information and visualise behaviours and their consequences suggests the use of virtual environments, as in serious gaming. However, knowledge representation in serious games are often limited and the overall implementation mainly empirical. On the other hand, interactive narratives technologies have demonstrated their ability to embed complex behavioural knowledge and support principled behaviour responding to dynamic contexts. This is why they support the exploration of complex situations, their rehearsal, and the understanding of expected behaviour through what-if interaction. The narrative perspective also provides better user guidance than a pure simulation system, allowing mixed-initiative access to information. The translation of medical protocols as interactive narratives is faced with a number of knowledge representation challenges, in particular for the representation of non-compli-ance and the consequences of incorrect behaviour. Another technical issue is the need to represent both common sense and domain knowledge, and articulate their representation with the Planning domain that forms the backbone of the interactive narrative. As part of the Open FET project MUSE (FP7-296703), we are developing a proof-of-concept prototype exploring the above aspects, and embedding the logical structure of guidelines into a real-time interactive narrative, which provides a principled simulation of the situations faced by patients, which preserves causal and deontic constraints. This paper describes the knowledge engineering process supporting the development of this prototype, from the analysis of patient guidelines to the use of planning representations supporting the interactive narrative.

Systematic Knowledge Engineering: Building Bodies of Knowledge from Published Research

Context. Software engineering researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, relevant knowledge collected in the SLR process is not publicly available, which considerably slows down building BoKs incrementally. Objective. We present and evaluate the Systematic Knowledge Engineering (SKE) process to support efficiently building BoKs from published research. Method. SKE is based on the SLR process and on Knowledge Engineering practices to build a Knowledge Base (KB) by reusing intermediate data extraction results from SLRs. We evaluated the feasibility of applying SKE by building a Software Inspection BoK KB from published experiments and a Software Product Line BoK KB from published experience reports. We compared the effort, benefits, and risks of building BoK KBs regarding the SKE and the traditional SLR processes. Results. The application of SKE for incrementally collecting and organizing knowledge in the context of a BoK was feasible for different domains and different types of evidence. While the efforts for conducting the SKE and traditional SLR processes are comparable, SKE provides significant benefits for building BoKs. Conclusions. SKE enables researchers in a scientific community to reuse and incrementally build knowledge in a BoK. SKE is ready to be evaluated in other software engineering domains.