Formal Representation Research Articles

A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction

As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an effective solution for systematically integrating knowledge in the task-planning domain. The mission-planning business model is able to systematically capture and portray domain knowledge in mission planning through a formal representation of mission planning processes, rules, and constraints. Thus, it becomes an important source of knowledge for mission-planning knowledge mapping. This paper proposes a business-model-driven knowledge graph construction method for mission planning. First, under the support of conceptual business knowledge, the multidimensional task-planning ontology network expression method is utilized to construct the task-planning ontology network, and then the data-based business knowledge is structured to transform it into business data mapping to complete the acquisition of business knowledge. Then, the task-planning ontology network is constructed using the multidimensional task-planning ontology network representation method under the support of conceptual knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to realize the matching mapping between the ontology network and business data mapping to complete the categorization of task-planning domain knowledge. Finally, the generated task-planning domain knowledge graph is stored in the Neo4j graph database. In order to ensure the completeness of the knowledge graph, an adaptive adjustment method based on its actual effectiveness is conceived, which is able to detect and adjust the completeness of the knowledge graph. The effectiveness of the proposed methodology is validated by constructing a space-station mission-planning knowledge graph driven by a space-station mission-planning business model.

Towards an ontology of mental health: Protocol for developing an ontology to structure and integrate evidence regarding anxiety, depression and psychosis

Background Research about anxiety, depression and psychosis and their treatments is often reported using inconsistent language, and different aspects of the overall research may be conducted in separate silos. This leads to challenges in evidence synthesis and slows down the development of more effective interventions to prevent and treat these conditions. To address these challenges, the Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) Project is conducting a series of living systematic reviews about anxiety, depression and psychosis. An ontology (a classification and specification framework) for the domain of mental health is being created to organise and synthesise evidence within these reviews and present them in a structured online data repository. Aim This study aims to develop an ontology of mental health that includes entities with clear labels and definitions to describe and synthesise evidence about mental health, focusing on anxiety, depression and psychosis. Methods We will develop and apply the GALENOS Mental Health Ontology through eight steps: (1) defining the ontology’s scope; (2) identifying, labelling and defining the ontology’s entities for the GALENOS living systematic reviews; (3) structuring the ontology’s upper level (4) refining entities via iterative stakeholder consultations regarding the ontology’s clarity and scope; (5) formally specifying the relationships between entities in the Mental Health Ontology; (6) making the ontology machine-readable and available online; (7) integrating the ontology into the data repository; and (8) exploring the ontology-structured repository’s usability. Conclusion and discussion The Mental Health Ontology supports the formal representation of complex upper-level entities within mental health and their relationships. It will enable more explicit and precise communication and evidence synthesis about anxiety, depression and psychosis across the GALENOS Project’s living systematic reviews. By being computer readable, the ontology can also be harnessed within algorithms that support automated categorising, linking, retrieving and synthesising evidence.

SME representation in chambers of commerce bodies – a model perspective

Background: The research analyzes the formal representation of micro, small, and medium-sized enterprises (SMEs) in the bodies of chambers of commerce in a model-based approach. Research purpose: Chambers of commerce are managed by their bodies (board, general assembly, etc.). Their composition is most often chosen by members in elections. It should reflect the structure of the associated enterprises – at least in terms of industry and size classes. The division of mandates affects the operational activity of the organization – including the content of positions developed in consultation processes and services provided to entrepreneurs. The aim of the study is to verify whether in the countries selected for analysis (representing different models of functioning of chambers of commerce) there are regulations securing the formal representation of SMEs in the bodies of chambers. And if they do exist, what form do they take? In addition to determining the actual situation, the advisability and possible way of regulating the SME parity in several model scenarios is analyzed. Methods: Using the deductive method and critical analysis, the study analyzes the statutes of chambers of commerce in selected countries and reviews sociological, economic and legal literature. Conclusions: The findings indicate that there are only a few examples of regulations protecting the representation of SMEs in chambers’ bodies. It was also found that the rules for representing different sized enterprises in the bodies and task groups of chambers of commerce are closely linked to the model of these organizations. In the Anglo-Saxon private law model, business owners primarily participate voluntarily in selected organizations. However, in countries with a single chamber per region, free choice of organization does not in fact exist. In the continental public law model, where membership is universal by operation of law, statutes focus primarily on mapping the structure of dominant industries in a region and usually omit other criteria. The sectoral model offers the greatest potential for balancing the representation of every size of industry and enterprise.

Cyber risk management technology to strengthen the information security of the national economy

Purpose. Developing a technology for managing cyber risks based on their improved classification by the level of impact on the occurrence of an extreme situation. Methodology. To achieve the goal, general scientific and special methods of cognition were used in the study: dialectical and systemic approaches, analysis and synthesis, logical generalization and grouping, structural-logical method, iterative approach, modeling, method of formal representations of uncertainty. Findings. A cyber risk management technology has been developed, consisting of four main stages: analysis of cyber threats (context establishment; security audit; formation of scenario concepts); scenario modeling (threat decomposition; scenario formation; setting criteria; setting probability estimates of concepts (variables); building a network architecture; formation of a private threat model; scenario analysis); risk assessment; object classification. The proposed approach to cybersecurity risk management provides vulnerability detection and risk assessment (risk potential) and simplifies the development of management solutions to prevent events affecting cybersecurity. Originality. The proposed technology differs from the existing ones by focusing on identifying those vulnerabilities and cyber threats that, according to their improved classification by the level of impact on the occurrence of an extreme situation, can lead to serious disruptions in the functioning of critical information infrastructure of the national economy. Practical value. The practical significance of the study lies in the fact that the proposed cyber risk management technology is one of the tools for preventing the realization of risks in cyberspace and the basis for strengthening the information security of economic entities in particular and the national economy as a whole.

An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.

Women’s entrepreneurship in the informal economy: A socio-spatial perspective

What role does ‘space’ play in shaping entrepreneurial choices? Much of the western-centric evidence offers abstract models of spatial dynamics, reflecting socio-cultural assumptions about entrepreneurship and the need to fix ‘women’s deficits’, within formal representations of space. We problematize these views by focusing on the everyday realities of women involved in (informal) entrepreneurial activities in a developing country context, exploring their contradictions with the abstract representations of the informal economy and the development policy goals of reforming women’s work through formalization. Based on Lefebvre’s conceptualization of space to explore these contradictions, we draw on empirical data from entrepreneurs in the informal economy in Nepal, where the International Labour Organisation reports that almost 85% of the economically active population to be in informal employment. We offer an entrepreneurial narrative that focuses on women’s work on the maintenance of local attachments and connections, underpinned by reciprocity, moral obligation and indebtedness often appropriated or disrupted by development strategies. By placing emphasis on informal spatial dynamics, we provide empirical support for the limitations of a policy approach that renders women’s work invisible through its homogenizing economic focus, to contribute towards a richer theorization of entrepreneurial contexts.

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

Using query semantic and feature transfer fusion to enhance cardinality estimating of property graph queries

With the increasing complexity and diversity of query tasks, cardinality estimation has become one of the most challenging problems in query optimization. In this study, we propose an efficient and accurate cardinality estimation method to address the cardinality estimation problem in property graph queries, particularly in response to the current research gap regarding the neglect of contextual semantic features. We first propose formal representations of the property graph query and define its cardinality estimation problem. Then, through the query featurization, we transform the query into a vector representation that can be learned by the estimation model, and enrich the feature vector representation by the context semantic information of the query. We finally propose an estimation model for property graph queries, specifically introducing a feature information transfer module to dynamically control the information flow meanwhile achieving the model’s feature fusion and inference. Experimental results on three datasets show that the estimation model can accurately and efficiently estimate the cardinality of property graph queries, the mean Q_error and RMSE are reduced by about 30% and 25% than the state-of-art estimation models. The context semantics features of queries can improve the model’s estimation accuracy, the mean Q_error result is reduced by about 20% and the RMSE result is about 5%.

Historical Architecture and BIM Modelling: Between Representation of Reality and Conceptual Abstraction

The essay aims to explore the applications of BIM to design regarding historic buildings, which will become mandatory in Italy from 2025 for public works above €1 million. Despite the advantages in coordinated data management and information sharing among the actors involved in contracts, problems related to the geometric representation and semantic definition of the virtual entities that populate the models have been recognized by the scientific community. Critical issues that have emerged from the study of significant experiences can be traced back to the difficulty of adapting tools designed for the projects of new buildings to the characteristics of the cultural heritage. In particular, the complexity of proposing congruent categorisations and parameterisations seems related to only partial correspondence between historical building components, IFC classes and BIM software categories. These limitations often lead to the identification of contingent solutions and expedients, which solve only the problems of geometric representation. In contrast, the ability to adequately share information stored in databases, interoperable through the IFC format, is conveyed by an appropriate semantic description. Formal representation in the BIM environment effectively refers to standardized industrial production, reproducibility of building elements and, more generally, to the goals of globalisation. Thus, the proposed reflections aim to encourage a conscious use of these tools and to outline implementation perspectives useful to bring the digital environment closer to the concrete reality of historical architecture, unique and irreproducible in origin and transformation, often realized through artisanal processes and anchored to specific contexts in multiple aspects.

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.

Comparison of Several Energy-Efficient Control Laws Using Energetic Macroscopic Representation for Electric Vehicles

Energy transition and decarbonization present significant challenges to transportation. Electric machines, such as motors and generators, are increasingly replacing internal combustion engines to reduce greenhouse gas emissions. This study focuses on enhancing the energy efficiency of electric machines used in vehicles, which are predominantly powered by batteries with limited energy capacity. By investigating various control strategies, the aim is to minimize energy losses and improve overall vehicle performance. This research examines two types of electric motors: Permanent Magnet Synchronous Motor (PMSM) and Induction Motor (IM). Real-time loss measurements were conducted during simulated driving cycles, including acceleration, constant speed, and braking phases, to mimic typical driving behavior. The simulation utilized characteristics from commercial vehicles, specifically the Renault Zoé and Bombardier eCommander, to assess the controls under different configurations. This study employed the Energetic Macroscopic Representation (EMR) formalism to standardize the analysis across different motors and controls. The results demonstrate significant loss reductions. The controls investigated in this study effectively reduce energy losses in electric motors, supporting their applicability in the automotive industry.

A Formal Language for Performance Evaluation Based on Reinforcement Learning

Temporal Logics are a rich variety of logical systems designed for specifying properties over time, and about events and changes in the world over time. Traditional temporal logic, however, is limited to binary outcomes true or false and lacks the capacity to specify performance properties of a system such as the maximum, minimum, or average costs between states. Current languages do not accommodate the quantification of such performance properties, especially in scenarios involving infinite execution paths where performance property like cumulative sums may fail to converge. To this end, this paper introduces a novel formal language aimed at assessing system performance, which encapsulates not only temporal dynamics but also various performance-related properties. In this study, this paper utilizes reinforcement learning techniques to compute the values of performance property formulas. Finally, in the experimental part, a formal language representation of system performance properties was implemented, and the values of the performance property formulas were computed using reinforcement learning. The effectiveness and feasibility of the proposed method were validated.

A New Version of a Broadly Applicable, Cross-lingual Meaning Representation Formalism and Its Significance for Biomedical Sciences

A New Version of a Broadly Applicable, Cross-lingual Meaning Representation Formalism and Its Significance for Biomedical Sciences

Formalization of Elements and Control Schemes in Intelligent System Models

The purpose of the work is to formalize the concepts and principles that form the basis of the mathematical model for controlling the life cycles of intelligent systems. The controlling elements formal description is part of a universal model of such systems. This model part is important for implementing complex intelligent systems with a large amount and diversity of knowledge flows and knowledge processing schemes performed simultaneously. The other two areas of this model are associated with a unified logical-algebraic description of the concept of knowledge representation formalism, as well as concepts related to the architecture of intelligent systems. Formalization of ideas about control in intelligent systems is carried out according to the principle of a hierarchy of controlling elements and processes. The elements of the mathematical model that implements this principle correspond to event-controlled automata with infinite memory and associated algorithms for the automata simulation in specific systems. The objectives of the research include the development of formats for describing the hierarchy of diagrams and assemblies of control elements in a universal model of an intelligent system, and diagrams of algorithms for the functioning of intelligent systems based on the processing of model descriptions. Models of intelligent system control elements hierarchy and execution algorithms for the sets of activated control elements for the implementation time of life cycles of intelligent systems are described by extending the special ontological structures. These structures implement customizations of universal control model

Populist Party Responsiveness and Populist Party Voter Satisfaction With Democracy in Europe

<p>Voters of populist parties tend to be dissatisfied with democracy. Some scholars attribute this dissatisfaction with how our democracies function to poor representation by mainstream parties and a feeling of not being heard. We should see this representation improve with the success of populist parties. This improved representation should, in turn, have a positive impact on populist party voters’ satisfaction with democracy (SWD). Existing case studies have only looked at the link between formal populist party representation in parliament or government, and populist party voters’ SWD, with mixed findings, the most puzzling of which is that populist party voters may even become less satisfied with growing formal representation. There is no comparative study on populist parties’ actual responsiveness to populist party voters and the connection to their SWD. Thus, we ask: How well do populist parties represent populist party voters, and how does this populist party responsiveness influence populist party voters’ satisfaction with democracy? We define populist party responsiveness as issue-based agenda-responsiveness between populist party voters and populist parties and investigate the link to SWD using data on 21 countries from the 2019 European Election Studies. We find that populist parties in Europe are not generally more responsive to populist party voters than mainstream parties. Populist parties’ agenda-responsiveness has a positive effect on populist voters’ SWD while being in government does not increase the positive effect of populist party responsiveness on their voters’ SWD. They may be disenchanted by how well their parties can eventually “walk the talk.”</p>

Automatic similarity feature selection for ontology matching with semantic sampling

Ontology provides a formal representation of knowledge as a set of concepts within a domain and the relationships between them. Ontology Matching (OM) identifies similar entities across different ontologies. Similarity Features (SFs) characterize the similarity between two entities from different perspectives, which serves as the foundation of OM method. However, noisy and redundant SFs can obstruct the relevance of useful ones, reducing the quality of matching results. To offer a practical and efficient SF selection, this work proposes a new automatic SF selection framework for OM, which consists of two new components. First, a semantic sampling method is proposed to automatically construct a balanced and representative training dataset, without the need for expert intervention. This method first generates a basic sample set that considers both the representativeness of the concepts and the heterogeneous characteristics of the two ontologies, and then over-samples the minority samples based on their semantic context. Second, a wrapper-based SF selection method is designed, which includes an Adaptive Compact Genetic Algorithm (ACGA) and a Relaxed Naive Bayes (RNB) classifier. The ACGA is able to efficiently identify the SF subset, reducing the computational cost by adaptively maintaining a population probability model based on the diversity and confidence of the SFs. Moreover, the RNB can effectively evaluate the quality of selected SFs by modifying the feature independence assumption with recombination and weighting strategies. Experimental results on the ontology alignment evaluation initiative’s Benchmark demonstrate that our method can efficiently find accurate OM results, and significantly outperform the state-of-the-art matching techniques.

EXTERNALIZATION OF TACIT KNOWLEDGE IN THE MENTAL MODEL OF A USER OF AN ARTIFICIAL INTELLIGENCE SYSTEM

The subject of the study is the processes of forming the user's mental model in artificial intelligence systems. The construction of such a model is associated with solving the problem of opacity and incomprehensibility of the decision-making process in such systems for end users. To solve this problem, the system user needs to receive an explanation of the obtained decision. The explanation should take into account the user's perception of the decision and the decision-making process, which is formalized within the user's mental model. The mental model considers the user's use of explicit and implicit knowledge, the latter of which usually lacks formal representation. The externalization of such knowledge ensures its transformation into a formal form. The aim of the work is to develop an approach to the externalization of implicit knowledge based on identifying patterns and causal dependencies for the decision-making process in an intelligent system when constructing the user's mental model. To achieve this goal, the following tasks are solved: developing a user's mental model of an artificial intelligence system that takes into account both explicit and implicit knowledge and developing an approach to the externalization of implicit knowledge of the user of the artificial intelligence system. A user's mental model of an artificial intelligence system that accounts for both explicit and implicit knowledge of the user is proposed. The model considers the connections between the user's explicit and implicit knowledge regarding the artificial intelligence system, the decision-making process, the method of using the decisions, and the general concept of the intelligent system. This creates conditions for the externalization of the user's implicit knowledge and the subsequent use of this knowledge in forming explanations regarding the decision-making process in the artificial intelligence system. An approach to the externalization of knowledge from the statistical and semantic layers of the user's mental model is proposed. In practical terms, the approach makes it possible to translate into explicit form the conditions and constraints regarding the formation and use of decisions in the artificial intelligence system.

Cclib 2.0: An updated architecture for interoperable computational chemistry.

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots-the formalization of cclib's intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.

Research Paper on Artificial Intelligence

Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.

Variational Feature Extraction in Scientific Visualization

Across many scientific disciplines, the pursuit of even higher grid resolutions leads to a severe scalability problem in scientific computing. Feature extraction is a commonly chosen approach to reduce the amount of information from dense fields down to geometric primitives that further enable a quantitative analysis. Examples of common features are isolines, extremal lines, or vortex corelines. Due to the rising complexity of the observed phenomena, or in the event of discretization issues with the data, a straightforward application of textbook feature definitions is unfortunately insufficient. Thus, feature extraction from spatial data often requires substantial pre- or post-processing to either clean up the results or to include additional domain knowledge about the feature in question. Such a separate pre- or post-processing of features not only leads to suboptimal and incomparable solutions, it also results in many specialized feature extraction algorithms arising in the different application domains. In this paper, we establish a mathematical language that not only encompasses commonly used feature definitions, it also provides a set of regularizers that can be applied across the bounds of individual application domains. By using the language of variational calculus, we treat features as variational minimizers, which can be combined and regularized as needed. Our formulation not only encompasses existing feature definitions as special case, it also opens the path to novel feature definitions. This work lays the foundations for many new research directions regarding formal definitions, data representations, and numerical extraction algorithms.