Knowledge Representation Approach Research Articles

Multicriteria requirement ranking based on uncertain knowledge representation and reasoning

The abundance of complex natural language descriptions related to requirements data presents major challenges for requirement analysis. Knowledge graphs (KGs), as the latest achievement in symbolic studies, are widely used in various fields due to their rich semantic expressive abilities. However, existing methods that mine resource description framework (RDF) triples in classical KGs cannot effectively capture the fuzzy semantic information in the product development system. To address this, we propose a multicriteria requirement ranking method based on uncertain knowledge representation and reasoning (KRR). First, we defined a model for representing uncertain knowledge to organize diverse data from multiple sources. This process was accomplished by constructing the requirement ontology, which is based on the function-behaviour-structure (FBS) model and requirements modelling-related documents. Next, a knowledge representation approach named the fuzzy requirement knowledge graph (FRKG) was devised by combining attribute confidence and predicate fuzziness. Then, knowledge reasoning rules were designed to enhance the edges in the FRKG, unveiling potential relationships between nodes. Utilizing the enriched FRKG (EFRKG), we proposed a multicriteria requirement ranking method based on grey relational analysis (GRA). To validate the effectiveness of the proposed approach, we conducted a case study involving unmanned aerial vehicles (UAVs). Furthermore, the semantic extension capability of FRKG was evaluated, and a comparison with traditional multicriteria requirement ranking methods was performed to demonstrate the efficiency of the proposed approach from both perspectives.

Enhancing health-care data integration via automated semantic mapping

PurposeThis paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.Design/methodology/approachA design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.FindingsThe study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.Originality/valueThe originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

AbstractA plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time-consuming, labor-intensive, and error-prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.

Graph of Graphs: A New Knowledge Representation Mechanism for Graph Learning (Student Abstract)

Supervised graph classification is one of the most actively developing areas in machine learning (ML), with a broad range of domain applications, from social media to bioinformatics. Given a collection of graphs with categorical labels, the goal is to predict correct classes for unlabelled graphs. However, currently available ML tools view each such graph as a standalone entity and, as such, do not account for complex interdependencies among graphs. We propose a novel knowledge representation for graph learning called a {\it Graph of Graphs} (GoG). The key idea is to construct a new abstraction where each graph in the collection is represented by a node, while an edge then reflects similarity among the graphs. Such similarity can be assessed via a suitable graph distance. As a result, the graph classification problem can be then reformulated as a node classification problem. We show that the proposed new knowledge representation approach not only improves classification performance but substantially enhances robustness against label perturbation attacks.

A BRIEF REVIEW OF THE DEVELOPMENT PATH OF ARTIFICIAL INTELLIGENCE AND ITS SUBFIELDS

The broad discipline of Artificial Intelligence (AI) aims to automate processes that currently require human intelligence. Understanding intelligence and creating intelligent systems are the two clear objectives of AI. AI is interested in decision-making tools including knowledge representation, machine learning, heuristic reasoning, and inference approaches. The roots of AI can be found in philosophy, literature, and the human imagination. Early advancements in engineering, electronics, and many other fields have inspired AI. In this paper, we begin with a general introduction to the area of AI, then move on to its inception, history, subfields, and various applications.

An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology

To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.

An alternative word embedding approach for knowledge representation in online consumers’ reviews

An alternative word embedding approach for knowledge representation in online consumers’ reviews

An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

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.

OntoStrength: An Ontology for Psychomotor Strength Development

An ontology is a formal, explicit description of concepts and relations from a domain while considering underlying properties, restrictions, and instances.With the advancement of the Semantic Web, the rise of the Educational Semantic Web, and the lack of uniformity between approaches for knowledge representation, ontologies are becoming more and more popular, including adaptive learning environments such as the Intelligent Tutoring Systems (ITSs). OntoStrength is an ontology developed to support the Selfit ITS, a platform that aims to improve the fundamental human psychomotor skills and, more specifically, bio-motor strength abilities. The goal of Selfit is to prevent the negative consequences of a sedentary lifestyle and accidents involving inadequate strength skills. Most ontologies in the sports domain support the development of digital solutions for sports performance and data collected during competitions. In contrast, OntoStrength’s goal is to contribute to the development of digital solutions dedicated to bio-motor strength ability analysis. OntoStrength considers other bio-motor skills like speed, endurance, or flexibility, as well as other activities like muscle analysis, movement patterns, or training load management, to support sport, professional and daily-life activities. OntoStrength enables the personalization of strength development programs in the Selfit ITS by providing a comprehensive data layer for its student, domain, and tutoring models.

Adding Interpretability to Neural Knowledge DNA

This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems.

New perspectives and results for Smart Operators in industry 4.0: A human-centered approach

Digital solutions (including Extended Reality as well as web, mobile and AI technologies), among others, are entering a broad variety of industries and modifying the capabilities of operators through (close to) real-time display of context-dependent information. However, little is known with respect to two major issues: (i) how these technologies can be seamlessly integrated for product/process and overall manufacturing system management providing of syntactic, semantic and functional interoperability and reusability among the different manufacturing systems departments; (ii) how they can be conveyed to operators also taking into account the cognitive load that is incurred by the operators.To this end, starting from a previous article (Longo et al., 2017), this article designs and proposes the KNOW4I approach and its practical implementation in an ICT platform (the KNOW4I platform) to further empower the Smart Operators concept.Two major objectives are pursued. The former is to set a standard referred to as the KNOW4I methodological approach for knowledge representation, knowledge management and digital contents management within the Smart Operator domain. The latter is the implementation of the aforementioned approach as part of the KNOW4I platform that includes a suite of Smart Utilities and Objects intelligently and interactively linked with a newly released version of the Sophos-MS digital and intelligent Assistant. Experimentations and results based on multiple KPIs are carried out to account for the effectiveness of the proposed framework.

Model and Knowledge Representation for the Reuse of Design Process Knowledge Supporting Design Automation in Mass Customization

Mass customization aims to meet individual requirements and, therefore, is one way to attract and retain customers—a key challenge in the design industry. The increase in design automation has offered new opportunities to design customized products at high speed in a way that is cost equivalent to mass production. Design automation is built upon the reuse of product and process knowledge. Ontologies have proven to be a feasible, highly aggregated knowledge representation in engineering design. While product and process knowledge from other lifecycle phases are represented in multiple approaches, the design process of the product as well as the adaption process of product variants is missing, causing breakpoints or additional iterations in design automation. Therefore, suitable knowledge representation tailored to design automation is still missing. Accordingly, this contribution proposes a novel knowledge representation approach to enable design automation for mass customization. Methodically, this novel approach uses semantic enrichment of CAD environments to automatically deduce information about a design task, design rationale, and design process represented by a formal ontology. The integration of the design process significantly differentiates the approach from previous ones. The feasibility of the approach is demonstrated by a bike crank customization process.

Automated Creation of Expert Systems with the InteKRator Toolbox.

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning.

Semantic Approach for Al-Quran Knowledge Representation in Islamic Knowledge Management Framework

Using ontology data model to accurately represent Al-Quran knowledge using its inherent structure of Division (Juz), Chapter (Surah), and Verse (Ayah) should be done within the framework of the Islamic Knowledge Management (IKM). Data modelling neglecting the IKM framework would result in inaccuracy and irrelevant result in knowledge processing activities involving the Al-Quran. Many of the current approaches utilizing conventional knowledge management methods such as taxonomy, hierarchy, or tree structure are limited in the sense that they only define the concepts of knowledge without relating it to a theme that defined from corpus of Islamic knowledge. In the case of Al-Quran, a classification of themes from background knowledge are important to provide a structural explanation of the knowledge and to ensure that the results obtained from a searching function of the corpus of the knowledge will result in an accurate and appropriate meaning intended by the Islamic knowledge. The aims of this paper is to demonstrate the construction of Al-Quran ontology and elaborate the searching method for representing and retrieving the relevance and accurate verses toward the theme of Al-Quran by using the semantic-based approach. The specification of Al-Quran ontology of which the prototype application was built on has been validated by experts. The evaluation test of the Al-Quran ontology application furthermore shows high precision result from searching functions done. With a good level of accuracy achieved from the test, it is hopeful that Al-Quran ontology application will provide a new experience of Al-Quran themes searching and browsing within the IKM framework for the Al-Quran readers. A capability to efficiently and accurately performing search of Al-Quran will certainly improve their understanding of Al-Quran contents.

АВТОМАТИЗАЦІЯ ПРОТИАВАРІЙНОГО КЕРУВАННЯ ЕНЕРГОСИСТЕМОЮ НА ОСНОВІ ЛОГІКО-ІМОВІРНІСНОГО МОДЕЛЮВАННЯ НАДІЙНОСТІ ПРОДУКЦІЙНИМИ МЕРЕЖАМИ ПЕТРІ

The paper presents the results of research on methods for constructing knowledge bases of decision support systems for dispatch control of power supply systems in crisis situations. Models of products based on Petri nets are proposed for assessing the parameters of reliability of power supply systems. An algorithm for calculating the reliability parameters of the electrical network using analytical matrix equations of Petri nets has been implemented. The work is devoted to the topical problem of intellectualization of power supply systems and means of automation of control of large power systems. The relevance of research in the field of building intelligent power systems is grounded. The basic requirements for the reliability of modern power supply systems are formulated. The concept of intellectualization of power systems and management of their reliability is a stable trend in the development of large energy systems and automated dispatch control systems. The solution to the problem lies in the implementation of intelligent decision support systems. The main task of the work is to model the reliability of the power supply system in order to build an effective knowledge base about the parameters of the reliability of the power supply system. For this purpose, we use a probabilistic approach to assessing reliability. This approach is based on the concepts of the probability of failure-free operation and the probability of failure, which are expressed in terms of the failure rate. On the basis of the obtained models, the construction of intelligent software systems for dispatching can be carried out taking into account the reliability indicators of the components of power supply systems. As a mechanism for formalizing failure circuits in power supply circuits, the production approach of knowledge representation is applied, which implements conjunctive-disjunctive connections of statements associated with failures. The outlined approach is invariant in relation to subject areas, which makes it possible to build effective decision support systems in the field of operational emergency control of the energy system.

A process knowledge representation approach for decision support in design of complex engineered systems

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.

Metaheuristics-based ontology meta-matching approaches

Ontologies have emerged to establish a well-defined meaning for information, solving problems of heterogeneity in data semantics and facilitating the process of information exchange. However, ontologies have generated a new semantic problem, since using more than one ontology can generate ambiguity in the meaning of a given data. The problem of ontology matching is to search for relationships between entities of distinct ontologies, solving the problem of semantic heterogeneity of the data. The problem is a relevant issue in the area of knowledge representation and several approaches have been proposed to solve it. This work presents a systematic mapping of the main works published in the area with an emphasis on metaheuristics-based meta-matching approaches.

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate ‘objectivization’ and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.

An integrated and discriminative approach for group decision-making with probabilistic linguistic information

Group decision-making (GDM) is a complex process. The diversity, discrimination, and inevitable uncertainty due to human intervention characterize such problems that add to this complexity. To circumvent this challenge, there is an urge for an appropriate knowledge representation and decision-making approaches. The present paper is concerned with a prescriptive approach to GDM that can aid a group of decision-makers (DMs) to arrive at a decision. To this end, the recent concept of probabilistic linguistic term set is utilized. The discrimination among the alternatives, as in the real world, are mimicked using an integrated framework that adopts CRITIC and variance methods for attribute weight calculation, Gini index for calculating the weights of DMs, Maclaurin symmetric mean for aggregating preferences, and weighted distance-based approximation for prioritization of alternatives. A real-world problem on electric bike selection illustrates the usefulness of the proposed work. Finally, comparative analysis with extant methods demonstrates the technical results, and it is inferred that the proposed work is (i) highly consistent (from Spearman correlation) and (ii) produces broad rank values (from standard deviation) that could be efficiently discriminated for rational decision-making and backup management during critical situations.