Knowledge Representation Formalisms Research Articles

Trends, Challenges and Opportunities of Engaging Digital Forensics for Cybercrime Investigations

The use of digital forensic tools and techniques has continued to evolve as the security community makes efforts to stay ahead and mitigate cyber crimes. These tools and techniques are assisting cybersecurity experts and law enforcement in identifying fraudsters and protecting data by utilizing techniques such as digital traces left by data processing and storage. This paper identifies the peculiarities of digital forensics as a field of study, explores trends, challenges and opportunities presented by digital forensic tools in investigating cyber crimes. We carried out a systematic literature review of applicable tools and techniques. Our research identified challenges affecting the use of digital forensics in investigating cybercrime, and capture comprehensively the pulse of the domain. Recommendations were made that digital forensics lack a unified formal representation of standardized procedures and knowledge for analyzing and gathering digital artifacts. This inevitably causes incompatibility and conflict within various digital forensics tools. This leads to errors in the interpretation and analysis of digital artifacts due to lack of standardized or formalized procedure for analyzing, preserving, and collecting digital evidence is absent. Keywords: Cybercrimes, Police, Cyber Security, Challenges, Techniques CISDI Journal Reference Format Adams Terrence Addey (2024): Trends, Challenges and Opportunities of Engaging Digital Forensics for Cybercrime Investigations – A Review. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 1, Pp 1-.8. dx.doi.org/10.22624/AIMS/CISDI/V15N1P1x. Available online at www.isteams.net/cisdijournal

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Trends, Challenges and Opportunities of Engaging Digital Forensics for Cybercrime Investigations

The use of digital forensic tools and techniques has continued to evolve as the security community makes efforts to stay ahead and mitigate cyber crimes. These tools and techniques are assisting cybersecurity experts and law enforcement in identifying fraudsters and protecting data by utilizing techniques such as digital traces left by data processing and storage. This paper identifies the peculiarities of digital forensics as a field of study, explores trends, challenges and opportunities presented by digital forensic tools in investigating cyber crimes. We carried out a systematic literature review of applicable tools and techniques. Our research identified challenges affecting the use of digital forensics in investigating cybercrime, and capture comprehensively the pulse of the domain. Recommendations were made that digital forensics lack a unified formal representation of standardized procedures and knowledge for analyzing and gathering digital artifacts. This inevitably causes incompatibility and conflict within various digital forensics tools. This leads to errors in the interpretation and analysis of digital artifacts due to lack of standardized or formalized procedure for analyzing, preserving, and collecting digital evidence is absent. Keywords: Cybercrimes, Police, Cyber Security, Challenges, Techniques

Imperative Formal Knowledge Representation for Control Engineering: Examples from Lyapunov Theory

In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.

D2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.

THE COMPLEX PROBLEM OF EXPANDING THE CAPABILITIES OF CADASTRAL SYSTEMS AND WAYS TO SOLVE IT

The development of cadastral systems in various countries, including Ukraine, has gone through creating information systems that serve users (stakeholders) in one of the subject areas, such as land administration, real estate construction, water resources, and others. Modern cadastral systems have no capacity to solve complex problems, and, at the same time, their structure does not contain specialised mathematical support in the form of models capable of simulating complex processes. Intelligent cadastres will be able to solve complex problems. Along with the existing types of cadastre support, intelligent cadastres should have well-structured mathematical and heuristic support. It should allow for solving private computing and service tasks, as well as loosely structured ones with a high degree of uncertainty, and provide good visualisation and detailing of objects, processes, and phenomena based on cloud and other modern information technologies. We propose introducing an intelligent cadastral system, presented as a geoinformation system with components of both a decision support system and a system for manipulating thematic layers. The decision support system should contain typical models used during the formalisation of one or another subject area or as a variant of the formal model of the subject areas’ description. The thematic layers manipulation system should allow the integration of attributive data belonging to different subject areas. In this case, ensuring prompt and fruitful work of cognitologists with experts is essential. The shortage of time and the variety of problems to be solved require the development of technology for formalising the knowledge of experts or specific subject areas. Implementing this technology and ensuring its implementation will require the creation of banks of typical knowledge models, which should store standardised procedures for formal representations. The bank of formal representations of heuristic knowledge includes procedures for forming production rules, building semantic and other networks, building frame representations and genetic algorithms, and so on. The bank of formal logical procedures contains procedures for creating logical rules based on the logic of statements, rules of logical inference based on the predicate logic, procedures of other modal logics, in particular, the presence logic, and procedures for building logical constructions based on the theory of categories and functors. Keywords: cadastral system, geoinformation system, decision support system, thematic layer manipulation system, formalisation.

Using Descriptive Logic in Intelligent Educational Systems

The purpose of the article is to study and solve the problems of using the basic principles of the modern formalism of descriptive (terminological) logic in intelligent learning systems that use knowledge bases of educational content. The research methods are the main methodological approaches and technological tools for the development of intelligent learning systems. In particular, systemic and comparative analyses to identify the features of intelligent learning systems, the method of expert evaluation, which involves the analysis of literature and information resources, interviews and surveys of experts in the field of information technology and mathematical logic, as well as the process of developing and testing an intelligent system. The scientific novelty of the study is the development of a system for obtaining expert knowledge for an intelligent system that combines the formation of an expert knowledge base, and the formalism for representing which is descriptive logic. The proposed approach of formalized knowledge representation using descriptive logic can be used in the development of authors’ intelligent learning systems in the field of online education and cognitive skills development. Conclusions. The scope of application of descriptive logic in intelligent systems is constantly expanding, increasing the capabilities of these systems to formally derive new knowledge, including in conditions of incomplete certainty. Descriptive logic, used in intelligent learning systems, provides ample opportunities for building a conclusion (i.e., forming an answer), in particular in situations such as forming and answering various types of queries to the system’s knowledge base (e.g., fully defined and clearly formulated, incompletely defined and vaguely formulated, completely vague, incompletely defined and clearly formulated, completely vague, completely vague and vaguely formulated), helping to specify the query. To do this, it is necessary to formulate a rigorous number system (to build an axiomatic theory). Thus, as a means of solving semantic and logical problems, descriptive logic has become a unique and powerful mechanism for representing knowledge. The successful use of descriptive logic in the author’s intelligent educational system will ensure the improvement of the quality of the educational process; monitoring of the learning process and control of its results; will facilitate the integration of this system into many modern information educational systems; will provide various consultations, advice, interpretations, explanations regarding the educational content or educational process, as well as a wide range of topics of educational content and tests of different levels.

Global Ontology Entities Embeddings

Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.

Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey

With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and errors within knowledge graphs. To address this, reasoning becomes essential for supplementing and rectifying these shortcomings. Logical rule-based knowledge graph reasoning methods excel at performing inference by uncovering underlying logical rules, showcasing remarkable generalization ability and interpretability. Moreover, the flexibility of logical rules allows for seamless integration with diverse neural network models, thereby offering promising prospects for research and application. Despite the growing number of logical rule-based knowledge graph reasoning methods, a systematic classification and analysis of these approaches is lacking. In this review, we delve into the relevant research on logical rule-based knowledge graph reasoning, classifying them into four categories: methods based on inductive logic programming (ILP), methods that unify probabilistic graphical models and logical rules, methods that unify embedding techniques and logical rules, and methods that jointly use neural networks (NNs) and logical rules. We introduce and analyze the core concepts and key techniques, as well as the advantages and disadvantages associated with these methods, while also providing a comparative evaluation of their performance. Furthermore, we summarize the main problems and challenges, and offer insights into potential directions for future research.

An analysis of knowledge representation methods in intelligent decision-making support systems

The scientific task, which is solved in the research, is the analysis of knowledge representation methods in intelligent decision-making support systems. The problem is explained by the fact that the form of knowledge representation significantly affects the characteristics and properties of the system. In order to operate all kinds of knowledge from the real world with the help of a computer, it is necessary to carry out their simulation. In such cases, it is necessary to distinguish knowledge intended for processing by computational devices from knowledge used by humans. In addition, with a large amount of knowledge, it is desirable to simplify the sequential management of individual elements of knowledge. A homogeneous representation leads to a simplification of the logic management mechanism and a simplification of knowledge management. The research is aimed at the analysis of knowledge representation methods in intelligent decision-making support systems. Currently, many models of knowledge representation have been developed. The main models include: logical models; frame model; network models (or semantic networks); production models. Therefore, the object of research is the intelligent decision-making support system. The subject of research is an intelligent decision-making support system. The following is set: – the methods (models, approaches) presented in the research for presenting knowledge in intelligent decision-making support systems in a canonical form are not advisable to use for a number of objective reasons given in subsection 3.1 of the research; – it is necessary to develop new (improvement of existing) representations of knowledge in intelligent decision-making support systems, which will have the advantages of these approaches without their disadvantages. Further improvement of these approaches to reduce the number of shortcomings and limitations of their application should be considered as the direction of further research.

Natural Language–Based Conceptual Modelling Frameworks: State of the Art and Future Opportunities

Identifying requirements for an information system is an important task and conceptual modelling is the first step in this process. Conceptual modelling plays a critical role in the information system design process and usually involves domain experts and knowledge engineers who brainstorm together to identify the required knowledge to build an information system. The conceptual modelling process starts with the collection of necessary information from the domain experts by the knowledge engineers. Afterwards, the knowledge engineers use traditional model driven engineering techniques to design the system based on the collected information. Natural language–based conceptual modelling frameworks or systems are used to help domain experts and knowledge engineers in eliciting requirements and building conceptual models from a natural language text. In this article, we discuss the state of the art of some recent conceptual modelling frameworks that are based on natural language. We take a closer look at how these frameworks are built, in particular at the underlying motivation, architecture, types of natural language used (e.g., restricted vs. unrestricted), types of the conceptual model generated, verification support of the requirements specifications as well as the conceptual models, and underlying knowledge representation formalism. We also discuss some future research opportunities that these frameworks offer.

Evidence Hub: A place to exchange medical knowledge and form communities.

Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers. Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of "Knowledge Objects". These objects represent various types of computer-interpretable knowledge. The platform includes features that encourage advancing medical knowledge, such as public discussion threads for civil discourse about each Knowledge Object, thus building communities of interest that can form and reach consensus on the correctness, applicability, and proper use of the object. Knowledge Objects are maintained by volunteers and published on Evidence Hub under GPL 2.0. Peer review and quality assurance are provided by volunteers. Users can explore Evidence Hub and participate in discussions using a web browser. An application programming interface allows applications to register themselves as handlers of specific object types and provide editing and execution capabilities for particular object types. By providing a platform for computable clinical knowledge and fostering discussion and collaboration, Evidence Hub improves the dissemination and use of medical knowledge.

Equivalence in Argumentation Frameworks with a Claim-centric View: Classical Results with Novel Ingredients

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration is claimaugmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.

Understanding Natural Language in Context

Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the assistant and the human is required. While this kind of dialog exists today mainly within static robots that do not make any movement in the household space, the challenge of reasoning about the information conveyed by the environment increases significantly when dealing with robots that can move and manipulate objects in our home environment. In this paper, we focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model. Thus, when the robot and the human communicate, there is already some formalism they can use -- the robot’s knowledge representation formalism. In this paper we describe an approach for translating natural language directives into the robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SoTA large language models, planning tools, and the robot knowledge of the state of the world and of its own model. This results in much more accurate interpretation of directives in natural language.

A Systematic Review on the Application of Ontologies to Improve Career Guidance

The review discusses students’ challenges as they move between educational levels and the labor market, mainly due to the lack of effective, efficient, and timely career guidance. The review suggests that using ontologies can improve career guidance delivery by providing a structured approach to representing and organizing career-related data. The study conducted a systematic literature review and found that ontologies have not been extensively applied in the career guidance domain. The review highlights the unique advantages of using ontologies for career guidance (CG), such as formal representation, interoperability, and knowledge representation and reasoning. The review also points out the loopholes in the few studies that attempted to use ontologies in CG. A systematic literature review was carried out to assess the extent to which ontologies have been applied in CG. Four reviewers performed a systematic search in IEEE, Scopus, Web of Science, and google scholar independently using agreed-upon criteria. To eliminate the bias of leaving out important studies, abstracts from selected conferences were carefully screened and reference scanning of the search results was performed. Out of 307 studies, 11 were found to match the search parameters and were included in the study.

Automatic knowledge generation for a persuasion dialogue system with enthymemes

Persuasion dialogues in multi-agent systems are required in situations in which agents debate to defend their viewpoint on a certain topic. In this work, we introduce a dialogue system for persuasion dialogues in which participants can exchange and complete enthymemes using structured argumentation as the underlying knowledge representation formalism. In our approach, the dialogue system provides the means to create arguments from enthymemes by generating assumptions based solely on the enthymeme's structure. Then, arguments are used to determine the dialectical status of the dialogue. We prove that under minimal restrictions, agents involved in a persuasion dialogue can reach an agreement successfully, and they can acquire knowledge such that their mental model is consistent with the outcome of the dialogue.

Identification of Key Elements in Prostate Cancer for Ontology Building via a Multidisciplinary Consensus Agreement.

Clinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research. A modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM). The panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM. This expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).

Fostering the consensus: a BERT-based Multi-label Text Classifier to support agreement in public design call for tenders

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) concerned with allowing computers to process natural human language. NLP is applied to solve several tasks in the design and construction process. However, in scientific literature, no applications are related to the pre-design phase and the processing of quality objectives and needs. The pre-design phase aims to reach a consensus between the stakeholders' quality demands and the design solution, relying on written natural language. Human language is the most pervasive and richest form of human knowledge representation and communication; however, at the same time, it is ambiguous, prone to misinterpretations, and hardly machine computable. Moreover, the mandatory procedural steps of the public tender procedure exacerbate the risk of misinterpretations inherent in using natural language. The study provides a methodology to design, assess, and evaluate an NLP tool based on the latest language model (i.e., BERT) to translate quality demands sentences into an evaluation grid in the Italian public tender context. The methodology is validated against a case study of an educational facility tender. The first results show good accuracy and capability of the NLP system to translate natural language into a numerical grid to support communication and foster consensus among actors, clarifying the appointing party and end-user's objectives to be reached via the design proposals.

Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach

As a new programming paradigm, neural-network-based machine learning has expanded its application to many real-world problems. Due to the black-box nature of neural networks, verifying and explaining their behavior are becoming increasingly important, especially when they are deployed in safety-critical applications. Existing verification work mostly focuses on qualitative verification, which asks whether there exists an input (in a specified region) for a neural network such that a property (e.g., local robustness) is violated. However, in many practical applications, such an (adversarial) input almost surely exists, which makes a qualitative answer less meaningful. In this work, we study a more interesting yet more challenging problem, i.e.,quantitativeverification of neural networks, which asks how often a property is satisfied or violated. We target binarized neural networks (BNNs), the 1-bit quantization of general neural networks. BNNs have attracted increasing attention in deep learning recently, as they can drastically reduce memory storage and execution time with bit-wise operations, which is crucial in recourse-constrained scenarios, e.g., embedded devices for Internet of Things. Toward quantitative verification of BNNs, we propose a novel algorithmic approach for encoding BNNs as Binary Decision Diagrams (BDDs), a widely studied model in formal verification and knowledge representation. By exploiting the internal structure of the BNNs, our encoding translates the input-output relation of blocks in BNNs to cardinality constraints, which are then encoded by BDDs. Based on the new BDD encoding, we develop a quantitative verification framework for BNNs where precise and comprehensive analysis of BNNs can be performed. To improve the scalability of BDD encoding, we also investigate parallelization strategies at various levels. We demonstrate applications of our framework by providing quantitative robustness verification and interpretability for BNNs. An extensive experimental evaluation confirms the effectiveness and efficiency of our approach.

On the Succinctness of Modal μ-Calculus Based on Covariant–Contravariant Refinement

The expressive power of logics is one of the major research topics in mathematical logic and computer science. One way of comparing the complexities of different formalisms (e.g. knowledge representation formalisms) stems from the perspective of representational succinctness. The concept of covariant–contravariant refinement (CC-refinement, for short) generalizes the concepts of refinement, simulation and bisimulation. We introduce an extension of the standard multi-agent modal [Formula: see text]-calculus system [Formula: see text] with CC-refinement operators ([Formula: see text]) and show that [Formula: see text] is equivalently expressive to [Formula: see text]. This paper presents the succinctness results of [Formula: see text] compared with [Formula: see text] from two viewpoints based on the sets of covariant and contravariant actions. We establish a family of CC-refinement formulas for comparing the succinctness based on the well-known parity symmetric alternating automata, which is often used to describe modal [Formula: see text]-calculus formulas.