Ontology Engineering Research Articles

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Gathering information from multiple data sources takes a long time to collect, analyze, and classify. Furthermore, if the data sources have different data structures, the merged data structure must support such heterogeneity. In addition, semantic of data must also be considered. This paper proposes automated knowledge integration from heterogeneous data sources using ontology engineering combined with text analytics. Text stemming is used to preprocess data. Part-of-speech (POS) tagging, Universal Dependencies (UD), and text similarity measurement called cosine similarity are used to analyze and integrate data. Our work focuses on five COVID- 19 knowledge scopes: COVID-19, coronaviruses, diseases, pandemics, and vaccines. For evaluation, six ontologies were constructed with six different cosine similarity values ranging from 0.5 to 1.0. Each constructed ontology has COVID-19 related and non-COVID-19 data in a ratio of 70 to 30. The six constructed ontologies were evaluated for consistency with the original data. Using cosine similarity with 0.6, precision, recall, and F1-score are 0.82, 0.71, and 0.76, respectively, and the constructed ontology is optimal, containing the highest amount of relevant COVID-19 information for this case study.

Модели и методы онтологического анализа данных в задаче структурного анализа и синтеза технических решений

The article explores the potential and demonstrates the effectiveness of using some models and methods of ontological data analysis in the problem of structural analysis and synthesis of technical decisions. Morphological analysis and synthesis of layout diagrams of artifacts are considered as a working methodology for structural design. A set-theoretic model of the morphological decision space is constructed for the classical version of the methodology. The model in-cludes the functional subsystems of the designed artifact, implementation options for these subsystems, and potential technical decisions themselves. Moreover, in contrast to existing versions of such formalization, the emphasis is on the description of binary relationships between the entities of the knowledge domain of morphological analysis. It is pro-posed to describe the incompatibility of implementation options for a single functional subsystem of a designed arti-fact and the restricted compatibility of implementation options for various functional subsystems within the frame-work of a single model, known in ontological engineering and, in particular in ontological data analysis as “existence constraints”. Analysis of one of the regularities inherent in this model made it possible to strictly substantiate the ap-proach common in structural design to describing the restricted compatibility of implementation options for various functional subsystems in the form of prohibitions. The advantages of using existence constraints to describe the mor-phological decision space are shown, and the frame of a formal ontology for the knowledge domain of morphological analysis is constructed. The structural synthesis of a technical decision is compared with the method of semantic identi-fication of an object, developed within the framework of ontological data analysis for processing incomplete and in-consistent empirical information about the results of measuring object properties. A conclusion is made about the suit-ability of the latter for solving the problem of structural synthesis when correcting the concept of a “normal” subset of properties and information about the costs and/or benefits of including each individual implementation option for the functional subsystems of the designed artifact.

Ontological modeling of the psychological field of a play

Ontological modeling of the psychological field of a play

Ontology engineering of automatic text processing methods

<span lang="EN-US">Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.</span>

Activities for the Implementation of Machine-Readable Standards in the Field of Military and Space Activities Using Ontological Engineering Methods

Activities for the Implementation of Machine-Readable Standards in the Field of Military and Space Activities Using Ontological Engineering Methods

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations.
 This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference.

Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

Smart Product Design Ontology Development for Managing Digital Agility

Digital agility is a critical dynamic capability that is becoming increasingly important in the context of collaborative product development processes (PDPs). This paper aims to address the complexity of today's PDPs by considering various quality aspects including safety, environment, and the entire lifecycle, along with diverse dynamic capabilities such as digital agility and circular economy. The authors employed a semantic web methodology and created an ontology-based knowledge model. The proposed ontology uses Design for X techniques, circular economy, digital agility, and the semantic web under the PDP perspective to increase performance and cooperation between designers and the project team. To validate the ontology, measures for domain ontology evaluation have been used. The paper presents a detailed guide for ontology engineering and evaluation for collaborative smart PDP, which incorporates digital agility as a critical dynamic capability. The proposed ontology can help boost PDP performance and increase customer satisfaction.

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.

Completing and Debugging Ontologies: State-of-the-art and Challenges in Repairing Ontologies

As semantically enabled applications require high-quality ontologies, developing and maintaining ontologies that are as correct and complete as possible is an important although difficult task in ontology engineering. A key task is ontology debugging and completion. In general, there are two steps: detecting defects and repairing defects. In this article, we discuss the state-of-the-art regarding the repairing step. We do this by formalizing the repairing step as an abductive reasoning problem and situating the state-of-the-art with respect to this framework. We show that there are still many open research problems and show opportunities for further work and advancing the field.

Development system for coordination of activities of experts in the formation of machineschetable standards in the field of military and space activities based on ontological engineering: a case study

Standardization and technical regulation involve the digitization of international and regional standards into "smart" standards, introducing new requirements for the presentation of complex industrial manuals. The introduction of artificial intelligence and machine learning into the process of standardization, improving process automation, data analysis will provide an opportunity to create innovative standards. Creation of machine-readable standards and their presentation in the form of a database will contribute to better interoperability between systems. The article demonstrates the method of ontological engineering for the automation of partial methodological examination of documents in accordance with the National Standardization Plan. As part of the experiment, an independent tool was developed, based on the extraction of qualitative data, using an uncontrolled automatic keyword extraction algorithm. The algorithm does not require the creation of a learning corpus, can be applied to any text and language, and does not have limitations on the amount of processed data. The algorithm provides adjustment of the parameters of the repetition of keywords, with the possibility of taking into account less important keywords. The work presents the following components: model classification standards, essence extraction module, categorization, thesaurus formation, monitoring and analysis. Calculations were carried out using formalization, determination of the target function, establishment of similarity measures, and description of document features. The work presents the theoretical aspect of application, design and description of the functional system.

Representing Product Tolerances using Metr-Ontology

t is important to know the dimensions, material composition, manufacturing processes, product performance history, and inspection methodology while designing products and selecting manufacturing processes. This information could be encapsulated into product lifecycle management tools. Use of ontologies as knowledge base for product information have been on a rise to overcome the drawbacks inherent to many knowledge-based approaches. The current ontologies that exist lack sufficient information regarding product inspection and tolerance, that is important from the perspective of the field of product and process metrology. This paper discusses the concepts for development of an engineering ontology for product metrology and is termed as “metrontology” herein. The focus of this developed metrontology is to aid in the understanding of product tolerances for future products being designed. The methodology is demonstrated through a simple example of single-cylinder engine. This research could lead to the creation of a metrology information enclave that will host the knowledge base of metrology information of several products.

Living without Beth and Craig: Definitions and Interpolants in Description and Modal Logics with Nominals and Role Inclusions

The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP reduce potentially hard existence problems to entailment in the underlying logic. Description (and modal) logics with nominals and/or role inclusions do not enjoy the CIP nor the PBDP, but interpolants and explicit definitions have many applications, in particular in concept learning, ontology engineering, and ontology-based data management. In this article, we show that, even without Beth and Craig, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as 𝒜ℒ𝒞𝒪, 𝒜ℒ𝒞ℋ, and 𝒜ℒ𝒞ℋ𝒪ℐ and corresponding hybrid modal logics. However, living without Beth and Craig makes these problems harder than entailment: the existence problems become 2ExpTime -complete in the presence of an ontology or the universal modality, and coNExpTime -complete otherwise. We also analyze explicit definition existence if all symbols (except the one that is defined) are admitted in the definition. In this case, the complexity depends on whether one considers individual or concept names. Finally, we consider the problem of computing interpolants and explicit definitions if they exist and turn the complexity upper bound proof into an algorithm computing them, at least for description logics with role inclusions.

The Construction of Seaports in the Arctic: Prospects and Environmental Consequences

The Arctic zone of the Russian Federation is of strategic importance for the country. Considering the fragility of Arctic ecosystems, special attention needs to be paid to the sustainable development of transport and related infrastructure within the framework of the “blue economy” concept, which is relevant for Arctic waters. At the same time, it is necessary to identify the main factors and tasks of creating transport and port infrastructure, building a modern fleet, and organizing fisheries and tourism in an environmentally sound manner. The purpose of the study is to consider the problems of anthropogenic influence for seaport facilities and to create a conceptual model of an environmental risk management system. The existing problems of Arctic ports and infrastructure are analyzed and existing business processes are considered, taking into account the peculiarities of their functioning in Arctic conditions. To systematize environmental assessments and establish dependencies between the main indicators describing the impact of port activities on elements of the natural environment, ontological domain engineering is proposed. It systematizes the basic terminology used within different subject areas of ecology and risks and allows one to visualize the relationships between elements of the natural environment, objects, port systems, their parameters and impact factors to assess the impact of the seaport on the natural environment. The results of ontological engineering (design and development of ontologies) in the field of risk management are presented. Future research will be aimed at developing the applied aspect of applying the results of ontological engineering in terms of specific engineering studies related to the assessment of anthropogenic load on the Arctic territory using simulation modeling.

The Balinese Lontar Manuscript Metadata Model: An Ontology-Based Approach

The digitization of cultural heritage, particularly lontar manuscripts, is a focus of research to aid stakeholders in the management of lontar metadata. Managing the metadata of Balinese Lontar manuscripts through the application of ontology is one method for ensuring the preservation and accessibility of Balinese Lontar manuscripts. This research aims to apply ontology technology to the preservation of the cultural heritage of Balinese lontar manuscripts so that all information about lontar details can be categorized according to their respective properties and relevant information can be presented based on the user's preferences. This research method employs the stages of the Design Science Research Methodology (DSRM) when devising the ontology for the Balinese lontar manuscript. The results demonstrated that the construction of metadata using an ontology-based methodology can provide the information required to describe, categorize, and connect ontology entities. Metadata consists of entity descriptions, hierarchies and classifications, relationships and properties, as well as the necessary semantics for constructing effective ontologies

Towards human-compatible XAI: Explaining data differentials with concept induction over background knowledge

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge. To make the explanations easily understandable for non-specialists, the complex description logic explanations generated by our concept induction system (ECII) were presented as a word list consisting of the concept names occurring in the highest rated system responses.

Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.

Toward the Development of NII RDC Application Profile Using Ontology Technology

In recent years, there has been international progress in developing platforms that support the reproducibility and reusability of research data. Typical platforms adopt a service architecture integrating multiple information systems to cover the entire research data lifecycle. In realizing this architecture, specifications for inheriting processes and results executed on different information systems play an essential role. This study introduces our practices for application profile development using ontology technology in the NII Research Data Cloud.

Relating Optimal Repairs in Ontology Engineering with Contraction Operations in Belief Change

The question of how a given knowledge base can be modified such that certain unwanted consequences are removed has been investigated in the area of ontology engineering under the name of repair and in the area of belief change under the name of contraction. Whereas in the former area the emphasis was more on designing and implementing concrete repair algorithms, the latter area concentrated on characterizing classes of contraction operations by certain postulates they satisfy. In the classical setting, repairs and contractions are subsets of the knowledge base that no longer have the unwanted consequence. This makes these approaches syntax-dependent and may result in removal of more consequences than necessary. To alleviate this problem, gentle repairs and pseudo-constractions have been introduced in the respective research areas, and their connections have been investigated in recent work. Optimal repairs preserve a maximal amount of consequences, but they may not always exist. We show that, if they exist, then they can be obtained by certain pseudo-contraction operations, and thus they comply with the postulates that these operations satisfy. Conversely, under certain conditions, pseudo-contractions are guaranteed to produce optimal repairs. Recently, contraction operations have also been defined for concepts rather than for whole knowledge bases. We show that there is again a close connection between such operations and optimal repairs of a restricted form of knowledge bases.