Knowledge Modeling Techniques Research Articles

From data acquisition to digital reconstruction: virtual restoration of the Great Wall’s Nine Eyes Watchtower

This article presents the virtual restoration of the Nine Eyes Watchtower, a significant cultural heritage site along the Great Wall. By applying the Seville Charter and digital technology, a detailed virtual restoration workflow is developed. The methodology involves acquiring data from multiple sources, including physical evidence, historical data, and comparative data. Advanced survey technologies, architectural knowledge, historical research, and computer modelling techniques are integrated to accurately capture the architectural and historical significance of the Nine Eyes Watchtower. The virtual restoration process follows a systematic approach, combining evidence interpretation and explicit deduction steps. The main outcome is a comprehensive virtual restoration model that accurately represents the architectural features and historical context of the Nine Eyes Watchtower. The virtual scene includes environmental elements, with potential for immersive exploration. By bridging the gap between interpretation and deduction, this study advances the scientific understanding and presentation of virtual restorations. The project contributes to ongoing research, education, and appreciation of the Great Wall's cultural legacy, ensuring its continued relevance for future generations.

Integrating Heterogeneous Ontologies in Asian Languages Through Compact Genetic Algorithm with Annealing Re-sample Inheritance Mechanism

An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries’ intelligent applications. However, due to the different knowledge backgrounds of ontology developers, the entities in the ontologies could be defined in different ways, which hamper the communications among the intelligent applications built on them. How to find the semantic relationships among the entities that are lexicalized in different languages is called the Cross-lingual Ontology Matching problem (COM), which is a challenge problem in the ontology matching domain. To face this challenge, being inspired by the success of the Genetic Algorithm (GA) in the ontology matching domain, this work proposes a Compact GA with Annealing Re-sample Inheritance mechanism (CGA-ARI) to efficiently address the COM problem. In particular, a Cross-lingual Similarity Metric (CSM) is presented to distinguish two cross-lingual entities, a discrete optimal model is built to define the COM problem, and the compact encoding mechanism and the Annealing Re-sample Inheritance mechanism (ARI) are introduced to improve CGA’s searching performance. The experiment uses Multifarm track to test CGA-ARI’s performance, which includes 45 ontology pairs in different languages. The experimental results show that CGA-ARI is able to significantly improve the performance of GA and CGA and determine better alignments than state-of-the-art ontology matching systems.

INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II

The identification, prediction, management, and control of economic and financial systems render extremely challenging tasks, which require comprehensively integrating the knowledge of different expert systems. Ontology, as a state-of-the-art knowledge modeling technique, has been extensively applied in the domain of economics and finance. However, due to ontology engineers’ subjectivity, ontology suffers from the heterogeneity issue, which hampers the co-operation among the intelligent expert system based on them. To address this issue, ontology matching for finding heterogeneous concept pairs between two ontologies has been rapidly developed. It is difficult to find the perfect ontology alignment that satisfies the needs of all decision-makers. Therefore, Multi-Objective Evolutionary Algorithm, such as Non-dominated Sorting Genetic Algorithm (NSGA-II), attracts many researchers’ attention. However, when facing large-scale ontology matching problems, NSGA-II tends to fall into local optimal solutions due to the large search space. To effectively address this drawback, we model the large-scale ontology problem as a nonlinear optimization problem, and propose an Adaptive Co-Evolutionary NSGA-II (ACE-NSGA-II) to deal with it. Compared with NSGA-II, ACE-NSGA-II introduces a co-evolutionary mechanism to increase the diversity of populations in order to decrease the probability of premature convergence. In particular, ACE-NSGA-II uses an adaptive population maintenance strategy to assign more resources toward the dominant ones in order to improve the solution efficiency for solving large-scale ontology matching. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track to test the effectiveness of ACE-NSGA-II, and the resulting experiment demonstrated that compared to NSGA-II and OAEI’s participants, ACE-NSGA-II is able to find better alignment.

Mobile Application Based Expert System for Cattle Disease Diagnosis and Treatment in Afan Oromo Language

Ethiopia is best known for its cattle population in Africa. Nonetheless, cattle diseases is the major constraint to cattle production and its safe utilization. For the developing countries, cattle disease creates a considerable effect on the livelihood and lives of the farmers. In this research work we have developed a mobile application based expert system in afan oromo language for diagnosing and treating cattle diseases. Knowledge Base regarding the various diseases affecting cattle’s in the study area was acquired by using both secondary and primary sources. The primary knowledge source was collected from West Guji and Bornea zonal area domain experts and the secondary sources of knowledge was collected using document analysis. Purposive sampling technique was used for selecting domain experts for knowledge acquisition process. The proposed expert system was developed using rule based knowledge representation approach and using decision tree knowledge modeling technique. After knowledge is acquired it was represented using rule based knowledge representation method using Java Programming language and a user interface was designed and developed using XML that fits into the present day smart phones and the application will be cross platform. User acceptance testing and overall system performance testing was made after the prototype was developed. Accordingly, the mobile application demonstrated 81% of accuracy and among the users who were made a user experience evaluation, 84% of them got satisfied by the performance of the mobile application.

Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm

Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts’ similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative’s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.

‘Domain-specific’ patent analytics: Focus on company's technology priorities

This paper aims to depict a new usage of patent analytics on a corporate level mitigating the gap between strategy (technology policy making) and operational activities (R&D program development and actualisation).The approach proposed for such mitigation is based on three techniques enriching patent analytics practical implementation:1) knowledge modelling techniques;2) deep technical analysis based on knowledge model;3) ‘insight-driven analytics’ approach.Such three techniques aim to facilitate the implementation of PL in the company's technology management processes.The paper contains examples related to oil & gas, transport, and water treatment industries.

Assessing similarities between gold ores, concentrates, and tailings with case-based reasoning

Searching for information has always been a challenge for a researcher. Decades ago it was a matter of gaining access to the information, but in this day and age, the problem is more about how to find the most relevant information amongst the ever-growing amount of scientific literature readily available. This research article presents an information retrieval tool that is designed to compare and rank gold raw materials, such as ores, concentrates, and tailings, according to the user’s research interests. The tool was constructed based on interviews with metallurgists specialized in gold and by utilizing a knowledge modeling technique called case-based reasoning. The tool can compare a gold raw material to existing material descriptions based on six attributes: Material type, Ore type, Mineralogy, Chemical assay, Particle size, and Gold content, resulting in a list of scientific articles that utilized similar raw materials. Different features in the software used enable the user to take their preferences into account and modify the effect of each attribute on the end result, i.e., the articles retrieved. The tool’s function was demonstrated with four test queries that ranked the entire case base of 84 gold raw material descriptions in relation to the test queries. While the tool’s default settings were able to retrieve similar materials from the case base, the best results are obtained when the user can modify the basis of retrieval in accordance with their particular research interests.

The Semantic Student: Using Knowledge Modeling Activities to Enhance Enquiry-Based Group Learning in Engineering Education.

This paper argues that training engineering students in basic knowledge modeling techniques, using linked data principles, and semantic Web tools - within an enquiry-based group learning environment - enables them to enhance their domain knowledge, and their meta-cognitive skills. Knowledge modeling skills are in keeping with the principles of Universal Design for instruction. Learners are empowered with the regulation of cognition as they become more aware of their own development. This semantic student approach was trialed with a group of 3rd year Computer Engineering Students taking a module on computer architecture. An enquiry-based group learning activity was developed to help learners meet selected module learning outcomes. Learners were required to use semantic feature analysis and linked data principles to create a visual model of their knowledge structure. Results show that overall student attainment was increased when knowledge modeling activities were included as part of the learning process. A recommendation for practice to incorporate knowledge modeling as a learning strategy within an overall engineering curriculum framework is described. This can be achieved using semantic Web technologies such as semantic wikis and linked data tools.

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

SU‐E‐J‐26: A Novel Technique for Markerless Self‐Sorted 4D‐CBCT Using Patient Motion Modeling: A Feasibility Study

Purpose:To develop an automatic markerless 4D‐CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D‐CT images.Methods:Each phase of onboard 4D‐CBCT is considered as a deformation of one phase of the prior planning 4D‐CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D‐CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for each single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended‐cardiac‐torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on‐board images. Projections were simulated over 180 degree scan‐angle for the 4D‐XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy.Results:With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%.Conclusion:The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D‐CBCT phase sorting.National Institutes of Health Grant No. R01‐CA184173 Varian Medical System

Illusions of control and social domination strategies in knowledge mapping system use

In recent years, the overload of information has driven companies to develop visualization technologies as a way to graphically represent knowledge for decision making. On the basis of visualization technologies and on knowledge modeling techniques, knowledge maps (Kmaps) help represent the knowledge assets of a company as graphical discrete objects. Yet such systems, because of their visual representation of knowledge, are prone to creating illusions. Using a case study of three firms’ use of Kmaps in their Human Resource departments and drawing upon Bourdieu's social theory, illusion of control theory, and IS as signal and symbol theory, our study provides a critical perspective on the use of Kmaps by groups of actors to reinforce their social domination in business organizations. We find that Kmap use can lead to illusions of knowledge control and illusions of executive control and that in response to the potential for knowledge illusions, three social domination strategies – a signal and symbol strategy, an avoidance strategy and an evidence strategy – are employed. Implications of these results on Knowledge Management and Information Systems Management future research are discussed.

2.3.2 An Enterprise Systems Engineering Framework

AbstractTraditional systems engineering (SE) is performed on a development project with only one or a few related systems being developed. Enterprise SE, on the other hand, must deal with deciding among many enterprise opportunities that the enterprise projects will work on. This entails several processes that are not provided for in traditional SE practice. An Enterprise SE Framework was developed to characterize the full scope of SE at the enterprise level.1 This framework was used to evaluate several case studies as part of my doctoral dissertation. This evaluation focused on how they used knowledge modeling techniques identified in my dissertation for development of an enterprise architecture.2

INTEROPERABILITY OF INFORMATION SYSTEM COMPONENTS FOR MONITORING OF SEWAGE AND INTELLIGENT ANALYSIS OF WATER RESOURCES / INFORMACINIŲ SISTEMŲ KOMPONENTŲ SĄVEIKUMAS VYKDANT NUTEKAMŲJŲ VANDENŲ STEBĖSENĄ IR INTELEKTINĘ VANDENS IŠTEKLIŲ ANALIZĘ

The interoperability problems of distributed databases are important in the developing of the operatively working web services aimed for all sectors of public administration. The following web services are designed for solving tasks in water resource management and contamination evaluation sector with a due attention to the international environment protection context. The paper is devoted to problems of developing the component‐based architecture of the integrated decision‐support system that afford ground for the monitoring and intellectual analysis of water management. Such investigations are made according to the requirements of European Union (EU) Water Framework Directive, Sustainable development Directives and EIONET ReportNet infrastructure. The main components of decision‐support system are analyzed by using different knowledge modelling and web service development techniques. The structure of water resource management information system (WRMIS) becomes the core of the decision‐support system in which web services are implemented. The main components for evaluation of processes of contamination and water monitoring are represented by data warehouse structures. The solutions to satisfy the interoperability requirements are demonstrated by architectural design decisions of the system, integrating the distributed data warehouses and geographical information system means. The web services are based on common portal technology. The organizational and political arrangements require deeper and stronger participation activities by all member states of EU in reporting, understanding the importance of sustainable development problems and risk evaluation possibilities. Santrauka Vandens išteklių valdymas ir nutekamojo vandens kokybės vertinimas yra viena svarbiausių problemų, susijusių su aplinkos apsaugos ir žmonių sveikatos uždaviniais. Vanduo yra vienas iš pagrindinių išteklių visiems biologinės įvairovės gyvavimo ciklams egzistuoti. Vandens kokybės reikalavimai daro įtaką daugeliui darniosios plėtros reikalavimų. Aplinkos apsaugos principai susideda iš daugelio tarpusavyje sąveikaujančių komponentų. Didelių įmonių, institucijų ir organizacijų veikla turėtų būti grindžiama įvairiopa atsakomybe už daromos veiklos ir žalos aplinkai padarinius. Nagrinėjami informacinių sistemų, vykdančių vandens užterštumo stebėseną ir analizę, sąveikumo užtikrinimo klausimai. Informacijos perteikimo metodai yra svarbūs kuriant konsultacines sistemas, kurios padėtų spręsti daugelį sprendimų priėmimo problemų, vertinant kompleksinius aplinkos taršos procesus. Šiame straipsnyje analizuojami pagrindiniai sprendimų paramos sistemos kūrimo komponentai aplinkos vertinimo sektoriuje, leidžiantys efektyviau spręsti šias problemas naudojant E-tinklus (vertinimo tinklus, t. y. Petri tinklų praplėtimą). Modeliai, leidžiantys atvaizduoti ir vertinti sprendimų priėmimo procesus, projektuojami keliais detalumo lygmenimis, taikant semantinio informacijos struktūrizavimo ir imitacinio modeliavimo priemones. Taršos procesai stebimi vykdant monitoringą, pirminius duomenis fiksuojant daugiamatėse duomenų saugyklose ir perteikiant vartotojams sprendimų paramos sistemos analizės priemonėmis. Aprašomi vandens išteklių ir nuotekų kontrolės duomenų analizės modeliai ir gauti rezultatai. Straipsnyje analizuojami pagrindiniai sprendimų paramos sistemos komponentai ir spendimams priimti svarbūs vandens ir aplinkos vertinimo rezultatai.

Integrated ecological-economic modelling of water pollution abatement management options in the Upper Ems River Basin

This paper presents the results of the FLUMAGIS project, in which we developed a spatial decision support system (SDSS) to support the implementation of the European Water Framework Directive (WFD). The modelling approach is based on the integration of ecological and socio-economic assessment methods, scale-specific and GIS-based data and knowledge modelling and visualization techniques. The project study area is the intensively cropped Upper Ems River Basin in north-western Germany. A method was developed that enables the transfer of scale-specific data and information. Analyses were performed for baseline conditions and specific management and planning scenarios to improve water quantity and quality at micro-, meso- and macro-scale. The results of the study indicate that substantial, expensive water and land management changes at different scales would be necessary to achieve the WFD water quality targets in this basin. Ecological-economic analysis of cost-effectiveness reveals that the costs of achieving certain goals of the WFD can vary more than tenfold depending on which measure is chosen out of the pool of management alternatives. Moreover, the study shows that the differentiation between landscapes and other regional characteristics although considered essential to the successful implementation of WFD measures is very data intensive.

Toward efficient modeling of fuzzy expert systems: a survey

Efficient modeling of the artificial intelligence tools has become a necessity in order to cut down the development and maintenance cost associated with building application systems in the business, industrial and agriculture sectors that are frequently amendable to sudden unexpected environmental and economic conditions changes. This can be accomplished through developing an efficient modeling language which exploits the beneficial features of the emerging object-oriented technology. This research is aimed at reviewing the recent scientific aspects of the research concerning conceptual modeling of fuzzy knowledge-based system, which exhibits a large extent of applicability in last few decades due to its capability to deal with vagueness, uncertainty and subjectivity, those are inherent in real world problems. The most recent researches and applications of fuzzy expert system are surveyed. The existing knowledge modeling techniques are reviewed and the prominent ones are pinpointed. This paper is intended to identify the main and common bottlenecks of the existing knowledge modeling tools to overcome it in developing a reliable conceptual model of fuzzy expert system. 

PLATO Helps Athens Win Gold: Olympic Games Knowledge Modeling for Organizational Change and Resource Management

Planning, designing, and implementing systems to support venue operations at the Olympic Games is complicated. The organizing committees must create designs that result in reliable, high-quality venue operations at reasonable cost. The organizational backdrop is unique. The organizing committee has a limited lifetime, it has no organizational memory, any learning disappears with its dissolution, and during its lifetime it must change rapidly from a function-oriented entity to a process-oriented one. The Athens 2004 Olympic Games Organizing Committee (ATHOC) used innovative techniques from management science, systems engineering, and information technology to change the planning, design, and operation of venues. We developed the Process Logistics Advanced Technical Optimization (PLATO) approach for the games. In the PLATO project, we developed a systematic process for planning and designing venue operations by using knowledge modeling and resource-management techniques and tools. We developed a rich library of models that is directly transferable to future Olympic organizing committees and other sports-oriented events. The direct financial benefit to ATHOC was the reduction of the costs of managing venue operations by over $69.7 million. The success of the games raised Greece’s international profile in terms of capabilities in managing large and complex projects which, in the medium to long term, will yield financial, political, and social benefits. Internationally, the PLATO legacy of its Olympics knowledge base will enable future organizers of large-scale events to reuse and customize the knowledge to gain benefits and reduce the financial burdens on governments and society.

FROM KNOWLEDGE MODELING TO ONTOLOGY CONSTRUCTION

This paper presents the Knowledge Modeling System (KMS) and its application. The KMS supports users in formalizing and documenting knowledge elements acquired for development of knowledge-based systems. The system was developed based on a knowledge modeling technique called the Inferential Modeling Technique. It facilitates building an application ontology of a domain by explicitly structuring and formalizing both domain and task knowledge of any industrial problem domain. The system also supports knowledge sharing by converting the ontology into the Extensible Mark-Up Language (XML). The tool is compared against some other existing efforts at ontology construction support. Application of the KMS for constructing an application ontology in the domain of monitoring and control of a petroleum separation process is also presented.

The role of knowledge modelling techniques in software development: a general approach based on a knowledge management tool

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modelling. Then, the current state of knowledge modelling techniques is described where increased reliability is available through the modern knowledge-acquisition techniques and supporting tools. The knowledge structure manager (KSM) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object-oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are made regarding the feasibility of using the knowledge modelling tools and methods for general application design.

A survey of modern knowledge modeling techniques

A major characteristic regarding developments in the broad field of artificial intelligence (AI) during the 1990s has been an increasing integration of AI with other disciplines. A number of other computer science fields and technologies have been used in developing intelligent systems, starting from traditional information systems and databases, to modern distributed systems and the Internet. This paper surveys the knowledge modeling techniques that have received most attention in recent years among developers of intelligent systems, AI practitioners and researchers. The techniques are described from two perspectives, theoretical and practical. Hence the first part of the paper presents major theoretical and architectural concepts, design approaches, and research issues. The second part deals with several practical systems, applications, and ongoing projects that use and implement the techniques described in the first part.

A system architecture for knowledge-based hypermedia

Hypermedia systems and knowledge systems can be viewed as flip-sides of the same coin. The former are designed to convey information and the latter to solve problems; developments beyond the basic techniques of each system type requires techniques from the other type. In this paper, we introduce the concept of knowledge-based or intelligent hypermedia and analyse various constellations of merged hypermedia and knowledge systems. A hypermedia system deals with informal and formalized theories and the relations between and within these. Therefore, the corner stones of our analysis are the very basic notions involved in formalizing domain knowledge: an informal domain theory, a formal object theory axiomatizing the informal theory and a metatheory analysing the properties and interrelations between and within these. We integrate these notions into a system architecture which is to serve as a programmable system schema for supporting the composition of actual intelligent hypermedia systems. Programming in the large is supported by the schema which defines the overall system structure whereas programming in the small is supported by knowledge modelling techniques. The application of the system architecture is illustrated by the construction of an interactive diagnosis system which involves knowledge-based reasoning both for navigation in hyperspace and problem-solving within the domain.