Knowledge Representation Tool Research Articles

Multigranulation variable-scale fuzzy neighborhood measures and corresponding Choquet-like integrals for feature selection

Choquet-like integrals, a nonlinear fuzzy aggregation function, are diffusely applied in several real problems. However, the corresponding fuzzy measures in them are provided by human intervention and not by data-driven methods. As an effective knowledge representation tool, rough set models in different approximation spaces are established for data processing. Particularly, the multigranulation fuzzy β-covering approximation space has been concerned with only a single scale β∈(0,1]. However, different granulation structures should have different scales. Therefore, establishing a multigranulation approximation space with a variable scale by inducing fuzzy measures through data-driven methods and using corresponding Choquet-like integrals in practical problems is a significant challenge. To address this challenge, the notion of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented here, as well as multigranulation variable-scale fuzzy neighborhood measures in it. Furthermore, Choquet-like integrals with the presented measures are constructed to solve the issue of feature selection (i.e., attribute reduction) under the new approximation spaces. Firstly, the concept of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented, where different fuzzy β-coverings have different scales β∈Θ. Moreover, multigranulation variable-scale fuzzy neighborhood measures in it, as fuzzy measures, are presented. Subsequently, Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures are constructed. A novel attribute reduction method under Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures is proposed for any decision information table. Finally, the presented method is selected for solving problems of classification and diagnosis of rolling bearing faults. Several public data sets are used to demonstrate the effectiveness and feasibility of the presented methods mentioned above.

A detailed study of resampling algorithms for cyberattack classification in engineering applications.

The evolution of engineering applications is highly relevant in the context of protecting industrial systems. As industries are increasingly interconnected, the need for robust cybersecurity measures becomes paramount. Engineering informatics not only provides tools for knowledge representation and extraction but also affords a comprehensive spectrum of developing sophisticated cybersecurity solutions. However, safeguarding industrial systems poses a unique challenge due to the inherent heterogeneity of data within these environments. Together with this problem, it's crucial to acknowledge that datasets that simulate real cyberattacks within these diverse environments exhibit a high imbalance, often skewed towards certain types of traffics. This study proposes a system for addressing class imbalance in cybersecurity. To do this, three oversampling (SMOTE, Borderline1-SMOTE, and ADASYN) and five undersampling (random undersampling, cluster centroids, NearMiss, repeated edited nearest neighbor, and Tomek Links) methods are tested. Particularly, these balancing algorithms are used to generate one-vs-rest binary models and to develop a two-stage classification system. By doing so, this study aims to enhance the efficacy of cybersecurity measures ensuring a more comprehensive understanding and defense against the diverse range of threats encountered in industrial environments. Experimental results demonstrates the effectiveness of proposed system for cyberattack detection and classification among nine widely known cyberattacks.

Conjunctive block coding for hyperdimensional graph representation

Knowledge Graphs (KGs) have become a pivotal knowledge representation tool in machine learning, not only providing access to existing knowledge but also enabling the discovery of new knowledge through advanced applications. Among the scalable reasoning methods used for such applications, distributed graph embedding approaches, particularly GNNs, have become popular for large-scale graph-related tasks. However, many of these methods have limitations in their interpretability and fail to take into account structural similarity in their representation. Hyperdimensional Computing (HDC), also known as Vector Symbolic Architecture (VSA), addresses this issue by using well-defined cognitive operations on distributed representations of symbolic concepts. This work proposes and evaluates a new vector symbolic graph representation, CLOG, that preserves approximate structural similarity beyond edge correspondence and fundamentally differs from previous methods. The model's effectiveness in graph representation is evaluated through theoretical analysis, graph reconstruction experiments, and link prediction task, highlighting its efficiency and accuracy. This approach significantly advances the field by enhancing the capabilities of HDC in graph representation, representing a notable improvement over existing methods.

Editorial: Concept mapping: Improving learning and understanding

Concept mapping has undergone significant evolution over the past half-century. Initially developed by Joseph Novak and his graduate students as a graphic representation to model the science understanding of elementary school students through interviews, it has now become a widely used knowledge representation tool across age groups and in a broad range of domains. Although mainly used to support meaningful learning, its application has expanded to an ever-increasing variety of applications. Those of us who have worked with concept maps for years and seen their potential continually seek to improve their use, with a focus on understanding how to construct effective maps, how the maps can support knowledge construction and learning, and how they can aid in the development of higher-order thinking skills and knowledge integration. Such improvements can come in various forms, including software tools that support these efforts. This special issue showcases a selection of papers from the 9th International Conference on Concept Mapping that aim to improve and extend the use of concept mapping to enhance learning, understanding, and knowledge construction and sharing. Each author offers a unique perspective within this common theme.

Development and maturity of co-word thematic clusters: the field of linked data

PurposeThis research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.Design/methodology/approachThis applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.FindingsThe top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.Originality/valueThis study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Knowledge representation of the state of a cloud-native application

Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices.

A reference implementation for knowledge assisted robot development for planetary and orbital robotics

The use of modular, yet reusable and interchangeable components offers flexibility for system designers and thus leads to a higher degree of reconfigurability with cost effective, adaptable and scalable solutions. The development of a robotic system remains, however, a complex task with increasing demands depending on the targeted capabilities, functionalities and operational constraints of the robot. This requires expert knowledge in mechanical, electrical and software engineering, as well as tools that can collect, process and use relevant data. As a result, the amount of available and managed information is increasing, while its relevance, usability, and knowledge that can be extracted from it are consequently decreasing: a situation described as information overload paradox.The design of a modular system can be based on the use of standardized modules with universal interconnects, but it should be aligned with a strong formalism, e.g. the semantic representation of components, to allow for a better management of the overall complexity. An ontology-based knowledge representation, for instance, which provides domain and application specific knowledge for the design of robotic systems, can be used as a method of storing and sharing data in a uniform, machine-readable and standardized way.In this paper, we introduce the Knowledge-based Open Robot voCabulary as Utility Toolkit (korcut) as a core component to support the ontology-driven development of robotic systems as part of a reference implementation of the Q-Rock development cycle. Q-Rock gathers methods to assist users in designing robot configuration by: (a) automatically exploring robot capabilities based on robot hardware, (b) proposing robot designs to meet the user’s needs, (c) refining the proposed robot designs. We use korcut to improve the robot design process in orbital and planetary robotics from the requirements definition to the development phases of complex modular robotic structures (e.g. composition of an unmanned ground vehicle with robotic arms). This is implemented by embedding semantic component descriptions in a state-of-the-art open-source 3D modeling software. Furthermore, the korcut ontology family includes various sub-ontologies to address specific space-related tasks, such as the modeling of a Standard Interconnect (SI) for On Orbit Services (OOS) and Orbital Factory. The ontology design follows criteria that have been derived from a survey conducted as part of this work. This paper also covers an evaluation of its current applications from a methodological, knowledge representation, and software tool perspectives. In the application part, we introduce the use of the ontology to model a multi-functional SI that enables mechanical connections between various (modular) robotic components, as well as transfer of power and data. Finally, we provide a critical analysis of our work and outline its future work.

Logics and collaboration

AbstractSince the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions.

The terminological tagging of the NooJ italian compound word dictionary

In this paper, and in relation to the construction of electronic dictionaries for NooJ, we will deal with the tagging of Italian compound words, and with how it differs from that of simple words as for methods, functions, and purposes. We will especially focus our attention on the tagging of technical-scientific compound words, demonstrating how this operation, in NooJ, represents a crucial tool for both information extraction and knowledge automatic management and representation. Furthermore, with the intention to producing a complete analysis, we will provide the definitions of simple word and compound word, from both a formal and a linguistic point of view. As for the linguistic examination, we will adopt two different approaches. For the first one, we will use the analytic methods of Zellig S. Harris, who first set out, in structuralist terms and in relation to English, the study of the composition of different morphemes in more complex linguistic units, hence also of word groups or phrases. For the second one, we will make extensive reference to the methodological framework of language formalization described by Maurice Gross' Lexicon-Grammar, as to its subsequent adaptations to the Italian language. Finally, as we will see, it will be of fundamental importance for us to differentiate the definitions that we will give here of compound words from the more generic and less precise one of multiword expressions (MWE). To justify this differentiation, we will provide not only formal indications, but also lexical, morphosyntactic and semantic ones.

An Alternative Definition of Stable Models Via Łukasiewicz Logic

Extensions of G3 (3-valued Godel logic) were studied as tools for knowledge representation and nonmonotonic reasoning. One of these extensions was studied and baptized as G′ 3 by Osorio et al. as a tool to define semantics of logic programming. In this work, we will explore the possibility to use another fuzzy logic for knowledge representation. In particular, we show that Łukasiewicz 3-valued logic (for short, Ł3) can be used for knowledge representation based on logic programming. Firstly, we prove that the definition of stable model for Ł3 is equivalent to the obtained through G3 for augmented programs, but when we consider more general programs we obtain more answers for stable models on Ł3 than G3. Finally, we present and explore a new definition of stable model based on Lukasiewicz n-valued logic via the use of Monteiro-Baaz ∆ operator.

Flexible model weighting for one-dependence estimators based on point-wise independence analysis

Recent studies have shown that Bayesian network classifiers (BNCs) are powerful tools for knowledge representation and classification, and averaged one-dependence estimators (AODE) is one of the most popular and effective BNCs since it can achieve the tradeoff between bias and variance due to its independence assumptions and ensemble learning strategy. However, unverified independence assumptions may result in biased estimates of probability distribution and then degradation in classification performance. In this paper, we prove theoretically the uncertainty of probability-theoretic independence and propose to measure the independence between attribute values implicated in specific instance. The estimates of conditional probability can be finely tuned based on point-wise independence analysis. Point-wise log likelihood function is then applied as weighting metric for committee members of AODE to improve the estimate of joint probability. Extensive experiments on 36 benchmark datasets show that, compared to other state-of-the-art classifiers, weighted one-dependence estimators using point-wise independence analysis can achieve competitive classification performance in terms of zero-one loss, RMSE, bias-variance decomposition and conditional log likelihood.

The Influences of a Knowledge Representation Tool on Searchers with Varying Cognitive Abilities

While current systems are effective in helping searchers resolve simple information needs (e.g., fact-finding), they provide less support for searchers working on complex information-seeking tasks. Complex search tasks involve a wide range of (meta)cognitive activities, including goal-setting, organizing information, drawing inferences, monitoring progress, and revising mental models and search strategies. We report on a lab study ( N = 32) that investigated the influences of a knowledge representation tool called the OrgBox, developed to support searchers with complex tasks. The OrgBox tool was integrated into a custom-built search system and allowed study participants to drag-and-drop textual passages into the tool, organize passages into logical groupings called “boxes”, and make notes on passages and boxes. The OrgBox was compared to a baseline tool (called the Bookmark) that allowed participants to save textual passages, but not organize them nor make notes. Knowledge representation tools such as the OrgBox may provide special benefits for users with different cognitive profiles. In this article, we explore two cognitive abilities: (1) working memory (WM) capacity and (2) switching (SW) ability. Participants in the study were asked to gather information on a complex subject and produce an outline for a hypothetical research article. We investigate the influences of the tool (OrgBox vs. Bookmark) and the participant’s working memory capacity and switching ability on three types of outcomes: (RQ1) search behaviors, (RQ2) post-task perceptions, and (RQ3) the quality of outlines produces by participants.

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence.

E-Learning Methodology

E-learning is the most important explosion propelled by the internet transformation. It is the use of technology which enables people to learn anytime and anywhere. While teaching can be based in or out of the classrooms, the use of computers and Internet forms the major component of E-learning. It can be termed as a network enabled transfer of skills and knowledge, and the delivery of education is made to a large number of recipients at the same time. E-learning can offer effective instructional methods, such as practising with associated feedback, combining collaboration activities with self-paced study and personalizing learning paths based on learners’ needs. The focus of E-learning is on both asynchronous and synchronous methodology. It investigates three major E-learning tools which are (i) curriculum tools (ii) digital library tools and (iii) knowledge representation tools. This paper reports on the extent to which the effectiveness and students’ attitudes towards E-learning impacts on their learning styles. The paper also reviews literature and gives a scholarly background to the study by reviewing some contributions made by various researchers and institutions on the concept of E-learning. Finally, the paper suggests that synchronous tools should be integrated into asynchronous environments to allow for any-time learning model and gave remark that E-learning needs to improve from various barriers.

Networked Knowledge and Complex Networks: An Engineering View

Along with the development of information technologies such as mobile Internet, information acquisition technology, cloud computing and big data technology, the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role. Within this context, it is required to develop new methodologies as well as technical tools for network-based knowledge representation, knowledge services and knowledge engineering. Obviously, the term “network” has different meanings in different scenarios. Meanwhile, some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation, knowledge services and knowledge engineering. This paper first reviews some recent advances on complex networks, and then, in conjunction with knowledge graph, proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks. For the unique advantages of deep learning in acquiring and processing knowledge, this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering. Finally, some challenges and further trends are discussed.

Spherical Linguistic Petri Nets for Knowledge Representation and Reasoning Under Large Group Environment

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning of rule-based expert systems. However, there exist limitations in representing ambiguous knowledge and performing approximate inference in traditional FPNs. Additionally, knowledge parameters are usually provided by some experts in existing FPN methods. In response to these issues, a new version of FPNs, named spherical linguistic Petri nets (SLPNs), is introduced in this article for knowledge representation and reasoning in the large group context. To this end, spherical linguistic sets are applied to capture imprecise knowledge and represent the uncertainty of experts’ judgements. Furthermore, a large group knowledge acquisition approach is developed to determine knowledge parameters. A bidirectional inference algorithm is developed for implementing the reasoning process and identifying the root causes of an appointed event. Finally, the efficacy and superiority of our developed SLPNs are illustrated by a realistic example regarding stampede risk level assessment in a high-speed railway station.

Fidel Semantics for Propositional and First-Order Version of the Logic of CG’3

Paraconsistent extensions of 3-valued Gödel logic are studied as tools for knowledge representation and nonmonotonic reasoning. Particularly, Osorio and his collaborators showed that some of these logics can be used to express interesting nonmonotonic semantics. CG’3 is one of these 3-valued logics. In this paper, we introduce Fidel semantics for a certain calculus of CG’3 by means of Fidel structures, named CG’3-structures. These structures are constructed from enriched Boolean algebras with a special family of sets. Moreover, we also show that the most basic CG’3-structures coincide with da Costa–Alves’ bi-valuation semantics; this connection is displayed through a Representation Theorem for CG’3-structures. By contrast, we show that for other paraconsistent logics that allow us to present semantics through Fidel structures, this connection is not held. Finally, Fidel semantics for the first-order version of the logic of CG’3 are presented by means of adapting algebraic tools.

Multi-granularity dominance rough concept attribute reduction over hybrid information systems and its application in clinical decision-making

Multi-granularity conceptual analysis has become an essential tool for data analysis and knowledge representation. This paper abstracts a class of uncertain decision-making problems, which integrate data and knowledge features from the clinical knowledge representation and decision-making process of integrated traditional Chinese medicine (TCM) and Western medicine. Under the framework of multi-granularity rough sets, we combine dominance rough sets with formal concept analysis methods to give a quantitative description of this uncertain decision-making problem. First, we define a multi-granularity formal context with attribute dominance relation. Then we give the concept lattice generation method under the formal context of multi-granularity dominance attributes and establish a multi-granularity formal concept analysis model that integrates data and knowledge representation. Meanwhile, the basic form of the multi-granularity dominance rough concept analysis model, the differences between existing rough sets and concept lattices, and their connections are discussed in details. Further, a clinical diagnosis decision-making model of integrated TCM and Western medicine, which fuses data driven Western medicine classification standard and knowledge-driven TCM diagnostic standard, is formed with the actual clinical diagnosis and treatment decision-making process of integrated TCM and Western medicine.

The conversion of the higher education and literature ontology into RelOntoUML

In this article, I will transform an ontology representing higher education and an ontology representing a library into the RelOntoUML model. RelOntoUML modeling is a modeling created from a combination of relational model, ontology, and UML modeling. The relational model is used for database storage. The data is stored in relations. Ontology is a tool for knowledge representation. The ontology describes the elements of a topic. The UML model visually represents the elements of a topic. One of the disadvantages of the ontology model is that software developers are not familiar with it, but UML diagrams and the relational model are well known. The two models (CollageMngtSys, Literature) that will be presented and converted can be downloaded from the gihub in OWL format, and are open source. In this article, after introducing the ontology, relational model, and UML, these two systems are introduced and converted.

A Framework for Evaluating Agricultural Ontologies

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.