Knowledge Representation Techniques Research Articles

Search Query Refinement Using Context, Knowledge and Long-term Memorization

In the era of information overload, search tools are crucial, yet users often struggle to articulate their precise dataneeds, resulting in sub-optimal search outcomes. This is often due to users associating products with influencers or celebrities rather than knowing the specific brand or product name. This research aims to enable users to find products based on real-life associ- ations, emphasizing the importance of upgrading search query refinement for accuracy and relevance. A significant challenge faced by existing web tools is refining queries involving unrelated entities. This research addresses this gap by proposing a compre- hensive approach that integrates context, knowledge represen- tation, and long-term memorization. The framework combines contextual information with advanced knowledge representation techniques, enhancing the system’s understanding of user intent and domain-specific concepts. Long-term memorization reduces the time complexity of query refinement by leveraging past search experiences. This study underscores the potential of incorporating context, knowledge representation, and long-term memorization in refining search queries, offering more accurate results. As the digital landscape evolves, our approach has the potential to upgrade the search engine experience, providing personalized and contextually relevant results globally.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Ontology-Driven Computer Systems

This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.

Generalized decision directed acyclic graphs for classification tasks

The recent studies present the various perspectives of Formal Concept Analysis from the Artificial Intelligence point of view since it can provide powerful techniques for knowledge representation, classification tasks, recommendation techniques, or data analysis. Formal Concept Analysis can be generally seen as an unsupervised Machine Learning technique that applies mathematical lattice theory to organize data based on objects and their shared attributes. However, the connections of Formal Concept Analysis with supervised learning are also thoroughly investigated. Regarding supervised learning, the learning algorithms of random forests or decision jungles have become popular and powerful Machine Learning ensemble methods. In this paper, we propose a generalization of a classification method based on the generalized decision directed acyclic graphs. We outline and review the recent studies on connections between Formal Concept Analysis and supervised learning. Finally, we present the results of our experiments regarding several datasets from the UCI Machine Learning repository.

Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research.

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency.

Sistem Pakar Diagnosa Kerusakan Motor Suzuki Smash Titan 115 Cc Menggunakan Metode Forward Chaining

Expert system is a computer-based system that uses knowledge, facts, and reasoning techniques that adapts the methods of an expert in a particular field to solve complex problems using a computer as an aid and a certain programming language. This expert system is built to assist Suzuki motorcycle users and technicians in resolving problems, especially with the Smash Titan 115 cc motorcycle. Problems with the Smash Titan 115 cc motorcycle require expert knowledge to find a solution. The expert system is built using the Forward Chaining method, where the system makes forward-based reasoning based on existing symptoms, then diagnoses the damage of each symptom of a problem and the system will draw conclusions from these symptoms. After this expert system is designed and implemented using the CodeIgniter framework and using the MySQL database, the process of searching for problems can be done quickly and accurately. In using the software that is built, the menus are designed very simply so that users can easily use it. The knowledge representation technique used in this expert system is production rules with the Best-First Search technique.
 Expert System, Forward Chaining, Expert Knowledge, Best first search

ONTOLOGICAL BASIS OF KNOWLEDGE MANAGEMENT IN THE CONTEXT OF EDUCATIONAL AND RESEARCH ACTIVITY

The main problem in organizing the transfer of knowledge in scientific and educational activities is to extract it from data and information, as well as to effectively present it to the end user in a form that is convenient for perception. The exponential growth of the volumes of information and data and their accumulation in spatially distributed databases and repositories has created the need to use new methods and tools that allow to significantly increase the efficiency of the processes of extraction and transformation into knowledge. At the same time, the main goal is to ensure high quality of educational and scientific materials, which, in turn, depends on the volumes of information sources processed by teachers and researchers. Simultaneously, the problem is the fragmentation and dispersion of information, which significantly complicates its holistic perception. The solution is the application of ontological models, as one of the most modern techniques of knowledge representation. Therefore, it is quite appropriate to use ontological and ontology-controlled approaches in the processes of knowledge transfer. An option of the implementation of such an approach based on cognitive services that implement the processes of creating annotations, extracting named entities, generalizing terms, determining the semantic proximity of terms, categoriz­ing and classifying terms, and full-text search is proposed. As a result, a high level of efficiency in the presentation of educational and scientific materials is achieved for their successful use and perception.

A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)

Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge representation and reasoning” is devoted to the representation of information about the environment in a way where a computer system can utilise it to accomplish difficult problems. This research is an extensive survey of the knowledge representation techniques used in smart agriculture, and specifically in the urban agricultural domain. Relevant articles on the knowledge base are extracted from the retrieved set to study the fulfillment of the criteria of the system. Various interesting findings were observed after the review. Spatial–temporal characteristics were rarely approached. A generalised representation technique to include all domains in agriculture is another issue. Finally, proper validation technique is found to be missing in such an ontology.

Personalized Intelligent Syndrome Differentiation Guided By TCM Consultation Philosophy.

Traditional Chinese Medicine (TCM) is one of the oldest medical systems in the world, and inquiry is an essential part of TCM diagnosis. The development of artificial intelligence has led to the proposal of several computational TCM diagnostic methods. However, there are few research studies among them, and they have the following flaws: (1) insufficient engagement with the patient, (2) barren TCM consultation philosophy, and (3) inadequate validation of the method. As TCM inquiry knowledge is abstract and there are few relevant datasets, we devise a novel knowledge representation technique. The mapping of symptoms and syndromes is constructed based on the diagnostics of traditional Chinese medicine. As a guide, the inquiry knowledge base is constructed utilizing the "Ten Brief Inquiries," TCM's domain knowledge. Subsequently, a corresponding assessment approach is proposed for an intelligent consultation model for syndrome differentiation. We establish three criteria: the quality of the generated question-answer pairs, the accuracy of model identification, and the average number of questions. Three TCM specialists are asked to undertake a manual evaluation of the model separately. The results reveal that our approach is capable of pretty accurate syndrome differentiation. Furthermore, the model's question and answer pairs for simulated consultations are relevant, accurate, and efficient.

A systematic literature review of automatic ontology construction

In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.

A Multiscale Modelling Approach to Support Knowledge Representation of Building Codes

Knowledge representations of building codes are essential and critical resources for the organization, retrieval, sharing, and reuse of implicit knowledge in the AEC industry. Against this background, traditional code compliance checking is time-consuming and error-prone. This research aimed to utilize various knowledge representation techniques to establish a knowledge model of building codes to facilitate the automated code compliance checking. The proposed knowledge model consists of three levels to achieve conceptual, logical, and correlational representations of building codes. The concept-level model provides the basic knowledge elements. The clause-level model was developed based on a unified top schema and provides the conceptual graph, mapping logics, and checking logics of each clause. The code-level model is constructed based on the explicit cross-references and semantic connections between clauses. The investigations on the model applications indicate two aspects. On the one hand, the proposed knowledge model shows high potential for semantic searching and knowledge recommendation. On the other hand, the automated code-compliance-checking processes based on the proposed multiscale knowledge model can achieve three main advantages: guiding designers to create a building model with completely necessary information, mitigating the differences between building information and regulatory information, and making the checking procedures more friendly and relatively transparent to users.

An Ontology-based Knowledge Mining Model for Effective Exploitation of Agro Information

The quality of agriculture depends on the quality of the yield, which is usually obtained through the well-being of the crop. The quality of any crop depends on the minerals in the soil, the type of soil, the location, and the seasons. The crop yield depends on soil fertility, availability of water, climate, and disease prevention. Although this information is prevailing in plenty among the expert farmers, the means of abducting the information to the future generation has not been much promoted. Hence, the knowledge disseminated regarding agriculture becomes scarce, affecting the entire agricultural process. Given these facts, a single source, strong knowledge management system is proposed to be designed. The system aims to embrace the different kinds of knowledge associated with agriculture and attempt to obtain a single source of agro information that is very much usable and reusable to the users. To ensure the maximum level of reusability, the knowledge of the domain needs to be modeled and represented in a way that is scalable and flexible. One of the knowledge representation techniques that emphasizes on reusability and scalability is ontology. Thus, this paper attempts to design an ontology-based agro knowledge management system. A rule base is constructed to improve the expressiveness of the knowledge. An incremental mining approach is adopted to extract the knowledge from multiple ontology. To understand better to aid decision-making, a visualization task is carried out. A multi ontology-based knowledge mining model is attempted in this research to provide better insight regarding agro knowledge.

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of “the same mode” has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes. These axioms glue together classes, properties and data types as well as a constraint. Such data clustering cannot be achieved through general use of simple classification schemes. Hence it has been observed and established that domain ontology using faceted principles provide better information retrieval with enhanced precision. This ontology should be seen not only as an alternative of the existing classification system but as a Knowledge Base (KB) system which can handle complex queries well, which is the ultimate purpose of any classification system or indexing system. In this paper, we try to understand how ontology-based information retrieval systems can prove its utility as a useful tool in the field of library science with a particular focus on the education domain.

Ontology Driven Testing Strategies for IoT Applications

Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in three-dimensional view by the domain experts of software testing, IoT and ontology engineering. Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements. The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

Modeling of Surgical Procedures Using Statecharts for Semi-Autonomous Robotic Surgery

In this paper we propose a new methodology to model surgical procedures that is specifically tailored to semi-autonomous robotic surgery. We propose to use a restricted version of statecharts to merge the bottom-up approach, based on data-driven techniques (e.g., machine learning), with the top-down approach based on knowledge representation techniques. We consider medical knowledge about the procedure and sensing of the environment in two concurrent regions of the statecharts to facilitate re-usability and adaptability of the modules. Our approach allows producing a well defined procedural model exploiting the hierarchy capability of the statecharts, while machine learning modules act as soft sensors to trigger state transitions. Integrating data driven and prior knowledge techniques provides a robust, modular, flexible and re-configurable methodology to define a surgical procedure which is comprehensible by both humans and machines. We validate our approach on the three surgical phases of a Robot-Assisted Radical Prostatectomy (RARP) that directly involve the assistant surgeon: bladder mobilization, bladder neck transection, and vesicourethral anastomosis, all performed on synthetic manikins.

Knowledge graphs as tools for explainable machine learning: A survey

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions.An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations.Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research.

Planning in RTS Games with Incomplete Action Definitions via Answer Set Programming

Standard game tree search algorithms, such as minimax or Monte Carlo Tree Search, assume the existence of an accurate forward model that simulates the effects of actions in the game. Creating such model, however, is a challenge in itself.One cause of the complexity of the task is the gap in level of abstraction between the informal specification of the model and its implementation language. To overcome this issue, we propose a technique for the implementation of forward models that relies on the Answer Set Programming paradigm and on well-established knowledge representation techniques from defeasible reasoning and reasoning about actions and change. We evaluate our approach in the context of Real-Time Strategy games using a collection of StarCraft scenarios.

Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.

Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance

Machine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) is applied to facilitate Machine Learning. For effective and efficient decision-making in Machine Learning, the environment’s own experience is captured, stored and reused using the DDNA technique. The proposed approach is implemented on practical test cases like a Chatbot. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The experimental test and results of the presented implementation of Decisional DNA Chatbot case studies support it as a technology that can improve and be applied to the technology, enhancing intelligence by predicting capabilities and facilitating knowledge engineering processes.