Knowledge Engineering Techniques Research Articles

Quantitative analysis of heavy metals in soil by X-ray fluorescence: Fusion, intelligentization and Nonstandard-sample calculation

Qualitative and quantitative analysis of heavy metal elements in soil by X-ray fluorescence (XRF) has received widespread attention and research from scholars as an important method for assessing environmental pollution. As a detection sample for multi-component systems, the study of matrix correction has always been the key to XRF data analysis for geological samples. In this study, we reviewed the calculation and analysis methods of XRF data used for geological samples since the Sherman equation was proposed, and divided the development of XRF data processing for soil samples into three stages based on the changes in the matrix correction methods used. By reviewing the processing ideas from past research, this paper summarizes the process of quantitative analysis of geological samples into seven stages and reviews the commonly used methods for each stage. Due to limitations in instrument and standard sample costs, as well as methodological constraints, geological samples currently face three challenges: a shortage of standard samples, insufficient generalization ability of established models, and large measurement errors in low-content element determination. With the further cross-penetration of multiple fields and disciplines and the summary of past research trends, we propose three research trends that may break through these limitations: fusion, intelligentization, and nonstandard-sample calculation. We also discuss the technical solutions related to these three research trends. We extensively discussed the feasibility and advantages of using spectral co-use, knowledge engineering, and adversarial data augmentation techniques to address problems. Our review provides insights into the XRF spectral data processing methods and frameworks for evaluating geological samples, and provides technical solutions to address the current challenges faced by XRF analysis of geological samples.

Automated multimodal sensemaking: Ontology-based integration of linguistic frames and visual data

Frame evocation from visual data is an essential process for multimodal sensemaking, due to the multimodal abstraction provided by frame semantics. However, there is a scarcity of data-driven approaches and tools to automate it. We propose a novel approach for explainable automated multimodal sensemaking by linking linguistic frames to their physical visual occurrences, using ontology-based knowledge engineering techniques. We pair the evocation of linguistic frames from text to visual data as “framal visual manifestations”. We present a deep ontological analysis of the implicit data model of the Visual Genome image dataset, and its formalization in the novel Visual Sense Ontology (VSO). To enhance the multimodal data from this dataset, we introduce a framal knowledge expansion pipeline that extracts and connects linguistic frames – including values and emotions – to images, using multiple linguistic resources for disambiguation. It then introduces the Visual Sense Knowledge Graph (VSKG), a novel resource. VSKG is a queryable knowledge graph that enhances the accessibility and comprehensibility of Visual Genome’s multimodal data, based on SPARQL queries. VSKG includes frame visual evocation data, enabling more advanced forms of explicit reasoning, analysis and sensemaking. Our work represents a significant advancement in the automation of frame evocation and multimodal sense-making, performed in a fully interpretable and transparent way, with potential applications in various fields, including the fields of knowledge representation, computer vision, and natural language processing.

Hill Dam Design to Improve Water Use in Rural Areas—Case Study: Sacachún, Santa Elena

The Sacachún commune in Ecuador currently faces a lack of water for its agricultural development; this reduces its possibilities of socio-economic development and causes migration to urban areas. This research proposes a presa colinar (hill dam) design that uses ancestral knowledge and classic engineering techniques to promote water use during the dry season in semi-arid regions. The methodology consisted of: (i) a systematic compilation of the ancestral structures used for water planting and harvesting in Ibero-American; (ii) selecting an appropriate place to build the hill dam; (iii) the dam’s body design; (iv) and creating a proposal for agricultural water use. The results helped define a good location for the hill dam implementation. In addition, a 10 m high and 40 m wide earthen dam was designed, with a storage capacity of 114,341 m3, extending agriculture to 7.5 ha through a polyculture system of six different plants. The embankment has a cofferdam located downstream, which provides stability in static and pseudo-static conditions. In addition, the dam slopes have a 2:1 ratio, and a spillway channel and filter drain were dimensioned to protect the dam.

Analysis of the Potential for Resilience of Integrated Oil & Gas Operations in Brazil

Resilience is presented in literature as the capacity of a system to disarm, adapt and recover from unexpected events. Despite the increase of interest of industries and academia in the subject, there are a lack of models that describes the elements that condition and determines resilient performance. This article presents a knowledge model that characterizes intangibles which determine resilient responses and is a central piece in a data science strategy supporting monitoring and analysis of potential for resilience in high-risk industries. Through an interdisciplinary approach, this model was established using an integrative review of the literature and the contribution of experts from several areas. The potential for resilience is represented by a set of leading indicators that allows continuous monitoring of both static characteristics of complex operations and dynamic resource mobilization in the face of unexpected events. Knowledge engineering and data science techniques are applied to treat data from various sources. The established approach addresses several elements that are not traditionally explored in safety management systems, including those related to knowledge that determine resilient responses, as well as factors related to human, structural and relational capital that condition resilient performance. Results of the application of the model are presented, including how the analytical model supports the definition of knowledge management and safety investment strategies in oil and gas companies in Brazil. The approach supports the prioritization of actions and investments to promote safety and enable strategies to learn from accidents and positive conditions that make operations safer despite unpredictability in daily operational routine.

An artifiсial intelligence computer system for differential diagnosis of lysosomal storage diseases

Aim. To improve the efficiency of diagnosis of hereditary lysosomal storage diseases using an intelligent computerbased decision support system.Materials and methods. Descriptions of 35 clinical cases from the literature and depersonalized data of 52 patients from electronic health records were used as material for clinical testing of the computer diagnostic system. Knowledge engineering techniques have been used to extract, structure, and formalize knowledge from texts and experts. Literary sources included online databases and publications (in Russian and English). On this basis, for each clinical form of lysosomal diseases, textological cards were created, the information in which was corrected by experts. Then matrices were formed, including certainty factors (coefficients) for the manifestation, severity, and relevance of signs for each age group (up to 1 year, from 1 to 3 years inclusive, from 4 to 6 years inclusive, 7 years and older). The knowledge base of the expert system was implemented on the ontology network and included a disease model with reference variants of clinical forms. Decision making was carried out using production rules.Results. The expert computer system was developed to support clinical decision-making at the pre-laboratory stage of differential diagnosis of lysosomal storage diseases. The result of its operation was a ranked list of hypotheses, reflecting the degree of their compliance with reference descriptions of clinical disease forms in the knowledge base. Clinical testing was carried out on cases from literary sources and patient data from electronic health records. The criterion for assessing the effectiveness of disease recognition was inclusion of the verified diagnosis in the list of five hypotheses generated by the system. Based on the testing results, the accuracy was 87.4%.Conclusion. The expert system for the diagnosis of hereditary diseases has shown fairly high efficiency at the stage of compiling a differential diagnosis list at the pre-laboratory stage, which allows us to speak about the possibility of its use in clinical practice.

Prognosticate Engine to Invest Money

Stock market prediction facilitates in deciding the organization’s stock valuation trading on any exchange. One can gain profits by estimating the future price of stock. The market assumptions suggests that the current updated information is reflected by the stock prices, so we can attempt to predict the fore coming stock price by observing market trends by utilizing technology. This purportedly allows us to gain future price information using predictive analysis. This paper Prognosticate Engine to Invest Money using knowledge engineering techniques is an attempt to predict stock prices through the use of advanced technologies to find promising results for trend prediction. This paper provides investors with an investment system to navigate through the stock market. It laid a basis for democratizing knowledge engineering technologies to the stockers in the scenario of choosing investment opportunities. It also takes precedence for developing and evaluating advanced models and upgrading Auto-ML in the fiscal environment. Keywords—Stock Market, Investment, Profit, Prognosticate Engine.

An Application of Knowledge Engineering to Mathematics Curricula Organization and Formal Verification

The authors present a theoretical proposal for the organization of mathematical contents, more precisely to curricula development formalization and formal verification, inspired by knowledge engineering techniques. The situation addressed is the following: the starting point is a mathematical “official curriculum” (or part of it), not necessarily completely detailed. In our proposal, a group of experts would have to first build a detailed formulation of this curriculum (including the “prerequisite” relation between contents), which we will denominate “preprocessed official curriculum.” We detail how any “official curriculum development” could then be rigorously formalized and formally verified in a way inspired by rule-based expert system formal verification. We have defined the following terms: “contents soundness,” “contents completeness,” “relation soundness,” “relation completeness,” and “absence of cycles.” We believe that this is a completely new formalization within mathematics teaching theory that, once computer is implemented, would be very helpful. That would be the case, for instance, in countries where government sets the “official curricula” for Primary and Secondary Education and textbook contents have to be manually checked and approved by academic authorities: evaluators would “only” have to extract the textbook contents and set the “prerequisite” relation among them and let the computer do the rest.

Exploration of Knowledge Engineering Paradigms for Smart Education: Techniques, Tools, Benefits and Challenges

Knowledge engineering paradigms (KEPs) deal with the development of intelligent systems in which reasoning and knowledge play pivotal role. Recently, KEPs receive increasing attention within the fields of smart education. Researchers have been used the knowledge engineering (KE) techniques, approaches and methodologies to develop a smart tutoring systems (STSs). The main characteristics of such systems are the ability of reasoning, inference and based on static and heuristic knowledge. On the other side, the convergence of artificial intelligence (AI), web science (WS) and data science (DS) is enabling the creation of a new generation of web-based smart systems for all educational and learning tasks. This paper discusses the KEPs techniques and tools for developing the smart educational and learning systems. Four most popular paradigms are discussed and analyzed namely; case-based reasoning, ontological engineering, data mining and intelligent agents. The main objective of this study is to determine and exploration the benefits and advantages of such computational paradigms to increase the effectiveness and enhancing the efficiency of the smart tutoring systems. Moreover, the paper addresses the challenges faced by the application developers and knowledge engineers in developing and deploying such systems. In addition to institutional and organizational aspects of smart educational technologies development and application

Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.

Non-Invasive Meningitis Diagnosis Using Decision Trees

Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients' data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database.

Ontologies for Liver Diseases Representation: A Systematic Literature Review.

Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.

A knowledge based infrared camera system for invisible gas detection utilizing image processing techniques

An infrared camera system for real-time monitoring of invisible methane gas leakages is proposed, which can be used in production, transportation, and gas plants. Instead of using conventional lasers, a medium wavelength light-emitting diode (LED) with a center wavelength of approximately 3300 nm is used for the light source of the gas detector. We achieve compact cooling with low power consumption by using a small electronic part with a Peltier element instead of a larger cooling unit. We also propose an algorithm based on adaptive average method and adaptive histogram equalization process to extract the background efficiently. This knowledge based infrared camera system effectively detects the leakage of invisible gas. Preliminary experimental results confirm the potential and effectiveness of the developed equipment to be commercialized for use in real-time gas monitoring systems. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.

Requirement analysis to design the knowledge-based system for long jump coaching

This paper presents the requirement for coaching the long jump of sports schools in Thailand. Moreover, a knowledge-based system (KBS) was designed for improving the long jump of athletes. Telephone and face-to-face interview approaches were used for collecting data from sports schools. There were 11 interview questions to investigate the requirement. MAXQDA 12 was employed to generate code and analyse data from interviewees. The results demonstrated that one of the most significant problems of coaching was the lacking of the system for analysis and suggestion. The framework, furthermore, was designed by experts using the knowledge engineering technique to create IF-THEN rules and an algorithm of all phases of the long jump biomechanics. In conclusion, there were many issues of requirements, for instance, all of the coaches required some systems for analysis and suggestion to improve the long jump skills. For the future works, the decision making algorithms of the system should be developed by using computer vision techniques.

A Knowledge-Based Real-Time Vision System for Monitoring the Inside of a Fluid Bed Heat Exchanger Chamber

In this paper, we present a vision system with a special camera for knowledge-based real-time monitoring of the inside of a fluid bed heat exchanger (FBHE) chamber in a thermal power plant. With the proposed system, it is possible to monitor the internal flux condition and analyze the inner temperature of a chamber. Due to the fact that the mixture of coal and sand inside a chamber flows by very quickly, there is an immense amount of smoke and dust, which make it difficult to capture images and analyze an existing system. An adaptive average method is proposed here to observe the background internal environment of an FBHE chamber. The experimental results show that real-time monitoring is possible, even under hot and dusty conditions. Preliminary experimental results confirm expectations about the possibility and effectiveness of the developed device for commercialized real-time monitoring systems. They demonstrate that a single camera with embedded image processing software can concurrently analyze the degree of fluidization of a mixture and the temperature of the chamber inside, even in extremely harsh and hazardous conditions. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.

Ontology-driven semantic unified modelling for concurrent activity recognition (OSCAR)

Activity recognition has a vital role in smart home operations. One of the major challenges in object-sensor-based activity recognition is to learn the complete activity model derived from a generic activity model for sequential and parallel activities. Such challenge exists due to erratic degrees of dissimilar activities in which inhabitants perform activities in sequential and interleaved fashion while interacting with different objects. The proposed work focuses on recognizing a complete set of actions (of activity) by exploiting different knowledge engineering techniques, ontology-based temporal formalisms and data driven techniques. Semantic Segmentation has been employed to establish the generic activity model. The spurious semantic segmentation produced by sensor noise or erratic behaviour is removed by Allen’s temporal formalism. Moreover, Tversky’s feature-based similarity has been used to remove the highly similar spurious activities produced as a result of mistaken interactions with wrong home objects. The duration to perform activities varies among inhabitants; such duration intervals are identified dynamically using the proposed model in order to have a complete activity model. A comprehensive set of experiments has been carried out for evaluating the proposed model where the results based upon different metrics assert its effectiveness especially when compared with other contemporary techniques.

Exploiting the knowledge engineering paradigms for designing smart learning systems

Knowledge engineering (KE) is a subarea of artificial intelligence (AI). Recently, KE paradigms have become more widespread within the fields of smart education and learning. Developing of Smart learning Systems (SLS) is very difficult from the technological perspective and a challenging task. In this paper, three KE paradigms, namely: case-based reasoning, data mining, and intelligent agents are discussed. This article demonstrates how SLS can take advantage of the innovative KE paradigms. Therefore, the paper addresses the pros of such smart computing approaches for the industry of SLS. Moreover, we concentrate our discussion on the challenges faced by knowledge engineers and software developers in developing and deploying efficient and robust SLS. Overall, this study introduces the reader the KE techniques, approaches and algorithms currently in use and the open research issues in designing the smart learning systems.

Image registration for varicose ulcer classification using KNN classifier

This paper proposes a clinically supportive tissue classification scheme for varicose ulcer classification using medical image processing and knowledge engineering techniques. Among various leg ulcers varicose ulcer accounts for over 90% of all cases. In this paper, we have focused on a complex image registration issue that occurs while the dependencies between intensities of images to be registered are not spatially homogeneous. In this paper, the classification scheme is divided into four distinct parts: pre-processing that includes image registration and difference image calculation, segmentation, feature extraction, KNN classification. In feature extraction step, color features like color correlogram, color moments; texture features like homogeneity, contrast, energy, correlation; shape features like solidity, eccentricity, major axis and minor axis are extracted. These extracted features are classified using K-Nearest Neighbor classifier to differentiate the different stages of wound. Our proposed approach gives efficient performance rates of average sensitivity (95.29%), specificity (94.4%), and accuracy (94.85%). Experiments conducted on varicose ulcer wound images show that the classifying registration improves both the registration and the detection, especially when the deformations are small. In future the same classification can be done using microscopic images (varicose ulcer tissue images).

MedDRA® automated term groupings using OntoADR: evaluation with upper gastrointestinal bleedings

ABSTRACTObjective: To propose a method to build customized sets of MedDRA terms for the description of a medical condition. We illustrate this method with upper gastrointestinal bleedings (UGIB).Research design and methods: We created a broad list of MedDRA terms related to UGIB and defined a gold standard with the help of experts. MedDRA terms were formally described in a semantic resource named OntoADR. We report the use of two semantic queries that automatically select candidate terms for UGIB. Query 1 is a combination of two SNOMED CT concepts describing both morphology ‘Hemorrhage’ and finding site ‘Upper digestive tract structure’. Query 2 complements Query 1 by taking into account MedDRA terms associated to SNOMED CT concepts describing clinical manifestations ‘Melena’ or ‘Hematemesis’.Results: We compared terms in queries and our gold standard achieving a recall of 71.0% and a precision of 81.4% for query 1 (F1 score 0.76); and a recall of 96.7% and a precision of 77.0% for query 2 (F1 score 0.86).Conclusions: Our results demonstrate the feasibility of applying knowledge engineering techniques for building customized sets of MedDRA terms. Additional work is necessary to improve precision and recall, and confirm the interest of the proposed strategy.

Towards a Mobile-Based Platform for Traceability Control and Hazard Analysis in the Context of Parenteral Nutrition: Description of a Framework and a Prototype App.

BackgroundThe parenteral nutrient (PN) mixtures may pose great risks of physical, microbiological, and chemical contamination during their preparation, storage, distribution, and administration. These potential hazards must be controlled under high levels of excellence to prevent any serious complications for the patients. As a result, management control and traceability of any of these medications is of utmost relevance for the patient care, along with ensuring treatment continuity and adherence.ObjectiveThe aim of this study is to develop a mobile-based platform to support the control procedures and traceability services in the domain of parenteral nutrient (PN) mixtures in an efficient and nonintrusive manner.MethodsA comprehensive approach combining techniques of software engineering and knowledge engineering was used for the characterization of the framework. Local try-outs for evaluation were performed in a number of application areas, carrying out a test/retest monitoring to detect possible errors or conflicts in different contexts and control processes throughout the entire cycle of PN. From these data, the absolute and relative frequencies (percentages) were calculated.ResultsA mobile application for the Android operating system was developed. This application allows reading different types of tags and interacts with the local server according to a proposed model. Also, through an internal caching mechanism, the availability of the system is preserved even in the event of problems with the network connection. A set of 1040 test traces were generated for the assessment of the system under various environments tested. Among those, 102 traces (9.81%) involved conflictive situations that were properly taken care of in this paper by suggesting solutions to overcome them.ConclusionsA mobile oriented system was generated and tested in order to allow enhanced control and quality management of PN mixtures that is easy to integrate into the daily praxis of health care processes.

Ontologies to Lead Knowledge Intensive Evolutionary Algorithms

Evolutionary Algorithms (EA) have proven to be very effective in optimizing intractable problems in many areas. However, real problems including specific constraints are often overlooked by the proposed generic models. The authors' goal here is to show how knowledge engineering techniques can be used to guide the definition of Evolutionary Algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. They propose a methodology based on the structuring of the conceptual model underlying the problem, in the form of a labelled domain ontology suitable for optimization by EA. The case studyfocuses on the logistics involved in the transportation of patients. Although this problem belongs to the well-known family of Vehicle Routing Problems, its specificity comes from the data and constraints (cost, legal and health considerations) that must be taken into account. The precise definition of the knowledge model with thelabelled domain ontology permits the formal description of the chromosome, the fitness functions and the genetic operators.