Ontology Development Environment Research Articles

Leveraging Ontology Development to Enhance Corrosion Visualisation in Engineering Design

Designing automated solutions for understanding, visualising, and detecting damage has become a key area in the era of digitalisation and advanced engineering. The use of deep learning and other artificial intelligence approaches has proven very innovative and rendered manual methods of damage inspection primitive. However, there remains a critical gap in creating a unified knowledge base that helps in understanding, conceptualising, and promoting collaborative engineering design, particularly in the context of automated corrosion detection through images. This research addresses this gap by presenting a conceptualised model for understanding corrosion detection through ontology development. This is implemented in an ontology development environment using Protege 5.5.0 and ELK 0.5.0 reasoner. Evaluation is done using expert competency questions and hypothetical scenarios thereby establishing a robust framework that is beneficial to Engineering design in terms of terminology standardisation, design process facilitation, and building corrosion inspection systems that are interoperable. The Ontology also enables sharing and reusing knowledge between automated corrosion detection systems as well as integration with existing industrial standards such as Industrial Ontology Foundry (IOF) and semantic web standards. Hence, offering a significant contribution to the digitalisation of engineering design. Besides the enhancement of damage detection, this work also advances the engineering field to utilise visual data more effectively in design, maintenance, and product lifecycle management.

РОЗРОБКА ОНТОЛОГІЧНОЇ МОДЕЛІ БАЗИ ЗНАНЬ «БІБЛІОТЕКА» НА БАЗІ СЕРЕДОВИЩА PROTÉGÉ

Modern development of society, increasing the degree and pace of integration of information technology achievements into the sphere of human life, traditional approaches to the construction of information systems cease to be effective or become too cumbersome. The development and use of knowledge-based systems is one way to solve this problem. The material of the work is devoted to the ontological modeling of the new subject area «Library». Ontology is considered in the context of knowledge sharing. The created ontological model of the «Library» knowledge base is quite modern and relevant today. The developed ontological model of the knowledge base in this field can be implemented on thematic web resources and will significantly facilitate the semantic search of information within the subject area in comparison with the existing ones. The terminological dictionary of this subject area is defined using the term system. An analysis of the capabilities of the Protégé ontology development environment for modeling the specific subject area «Library» was carried out. The basic principle of ontology modeling in the form of a semantic network was chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area «Library». Using the Protégé environment for the implementation of the ontological model of the knowledge base made it possible to use the advantages and features of the created «Library» model, such as: structuredness, completeness, reflexivity, functionality, transitivity, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base was selected and used. Testing of the developed ontological knowledge base was conducted and a fairly high level of its correctness in the process of information search was confirmed. The average indicator (Average) according to the SUM metric, determined for all users, is equal to 83.25%, which is an acceptable indicator of the ontological knowledge base. When using the classic relational model of database organization to implement the «Library» database. the average SUM metric for 10 users is 75.26%. An example of the developed ontology in Protégé is considered, a graphical representation of the basic graph of the ontological model of the «Library» knowledge base is given, the model includes 10 classes and subclasses, 2 properties-relations and up to 10 properties-data are defined for each class and subclass, an example of the classes of the ontological model is given «Library», an example of the "property-relations" of the ontological model «Library» is given, an example of the "property-data" of the ontological model «Library» is given. Possible directions of further development of the ontological model «Library» have been determined.

Using ontologies for life science text-based resource organization

Ontologies are used to support access to a multitude of databases that cover domains relevant information. Heterogeneity and different semantics can be accessed by using structured texts and descriptions in a hierarchical concept definition. We are interested in Life Sciences (LS) related ontologies including components taken from molecular biology, bioinformatics, physics, chemistry, medicine and other related areas. An Ontology comprises: (i) term connections, (ii) the identification of core concepts, (iii) data management, (iv) knowledge classification and integration to collect key information. An ontology may be very useful in navigating through LS terms. This paper explores some available biomedical ontologies and frameworks. It describes the most common ontology development environments (ODE): Protégé, Topbraid Composer, Ontostudio, Fluent Editor, VocBench, Swoop and Obo-edit, to create ontologies from textual scientific resources for LS plans. It also compares ontology methodologies in terms of Usability, Scalability, Stability, Integration, Documentation and Originality.

ОНТОЛОГІЧНЕ МОДЕЛЮВАННЯ БАЗИ ЗНАНЬ З ОРГАНІЗАЦІЇ ПОДОРОЖЕЙ

In modern conditions of society development, increasing degree and pace of integration of information technology achievements in the field of human life, traditional approaches to building information systems become too cumbersome or cease to be effective. One of the ways to solve this problem is to develop knowledge-based systems. The work is devoted to ontological modeling of a new subject area "travel organization". The ontology is considered in the context of knowledge exchange. The created travel ontology is quite modern and relevant today. The developed ontological model of the knowledge base in this area can be implemented on thematic web resources and greatly facilitate the semantic search for information within the subject area in comparison with existing ones. A terminological dictionary from this subject area is defined using the concept of terminological system. An analysis of the possibilities of the Protege ontology development environment for modeling a specific subject area of "travel". The basic principle of ontology modeling in the form of a semantic network is chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area of "travel". The use of the Protege environment to implement the ontological model of the knowledge base allowed to use the advantages and features of the created model of "travel organization", such as: functionality, transitivity, reflectivity, structuring, completeness, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base is chosen. Testing of the developed ontological knowledge base was carried out and a rather high level of its correctness in the process of information retrieval was confirmed. The average metric on the SUM metric for all users is equal to 82.95%, which is an acceptable indicator of the ontological knowledge base. When using the classical relational model of database organization to implement the "travel" database, the average SUM metric for 10 users is 73.68%. An example of the developed ontology in Protege is considered, a graphic representation of the basic graph of the ontological mode "travel" is given, the model includes 10 classes and subclasses, for each class and subclass 2 properties-relations and from 2 to 10 properties-data are defined, an example is given classes of the ontological model of "travel", an example of "properties-relations" of the ontological model of "travel" is given, an example of "properties-data" of the ontological model of "travel" is given. Possible directions of further development of the ontological model "travel organization" are formulated.

Towards a Modern Ontology Development Environment

Abstract Ontologies provide engineers and developers with an unambiguous, verifiable, and expandable knowledge base related to a certain domain. Every project that requires control over consistent knowledge, which is especially relatable when using artificial intelligence with datasets increasing in size every second, would reap benefits from adding ontologies to the equation. It is a powerful asset enabling the development of a project with integrity between platforms or teams. Unfortunately, the cost of entry for a developer into the ontology engineering area is high, as it has been proven over the last decades that developing an ontology is a complex, collaborative task, which requires the support of an adequate methodology as well as software tools. The current paper’s objective is twofold. First, it provides a survey on the methodology and software tools used for the creation of the ontology, its maintenance and collaboration. The paper investigates how the tools evolved over the years and what trends have emerged. Second, as the result of the analysis conducted, we show that current solutions have deficiencies and a technological debt; therefore, we present our plan to build a modern tool that uses state-of-the-art technology.

A fully computational and reasonable representation for karyotypes.

SummaryThe human karyotype has been used as a mechanism for describing and detecting gross abnormalities in the genome for many decades. It is used both for routine diagnostic purposes and for research to further our understanding of the causes of disease. Despite these important applications there has been no rigorous computational representation of the karyotype; rather an informal, string-based representation is used, making it hard to check, organize and search data of this form. In this article, we describe our use of OWL, the Ontology Web Language, to generate a fully computational representation of the karyotype; the development of this ontology represents a significant advance from the traditional bioinformatics use for tagging and navigation and has necessitated the development of a new ontology development environment called Tawny-OWL.Availability and implementationThe Karyotype Ontology and associated Tawny-OWL source code is available on GitHub at https://github.com/jaydchan/tawny-karyotype, under a LGPL License, Version 3.0.

Ontology and XML-based Specifications for Collaborative B2B Relationships


 
 
 A collaborative B2B relationship implies jointly executing business processes. This relationship demands a complete access to available information and knowledge to support decision-making activities between trading partners. To support information interchange between enterprises in collaborative B2B e- commerce there are some XML-based standards technologies, like RosettaNet, ebXML and OAGIS. However, XML does not express semantics by itself. So, these standards only provide an infrastructure to support the information interchange. They are suitable to integrate information but not to support decision-making activities where a common understanding of the information is needed. In this paper we analyze the integration of these standards with ontology to describe the meaning of the information and knowledge interchanged between trading partners to jointly execute business processes. Furthermore, we define the main components of an ontology development environment to support the entire ontology lifecycle.
 
 

An introduction to knowledge computing

This paper deals with the challenges related to self-management and evolution of massive knowledge collections. We can assume that a self-managed knowledge graph needs a kind of a hybrid of: an explicit declarative self-knowledge (as knowledge about own properties and capabilities) and an explicit procedural self-knowledge (as knowledge on how to utilize own properties and the capabilities for the self-management).We offer an extension to a traditional RDF model of describing knowledge graphs according to the Semantic Web standards so that it will also allow to a knowledge entity to autonomously perform or query from remote services different computational executions needed. We also introduce the concepts of executable knowledge and knowledge computing on the basis of adding an executable property to traditionally used (datatype and object) properties within the RDF model. The knowledge represented with such an extended model we call as an executable knowledge, or the one which contains explicit (executable) instructions on how to manage itself. The appropriate process of the executable knowledge (self-)management we call as a Knowledge Computing. Unlike the knowledge answering machines, where computations over knowledge are used just for addressing a user query, the knowledge computing in addition provides computations for various self-management purposes. The paper also presents some pilot (proof-of-concept) implementation of the executable knowledge as a plug-in to Protégé ontology development environment.

Building up a class hierarchy with properties by refining and integrating Japanese Wikipedia Ontology and Japanese WordNet

Previously, we constructed the Japanese Wikipedia Ontology JWO via a semi-automatic process using the Japanese Wikipedia, but it had problems due to a lack of upper classes and appropriate definitions of properties. Thus, the aim of the current study was to complement the upper classes in JWO by refining and integrating JWO and Japanese WordNet JWN to build a class hierarchy with defined properties based on the considerations of property inheritance. To achieve this, we developed tools that help users to refine the class-instance relationships, to identify the JWO classes that need to be aligned with JWN synsets, and to align the JWO classes with the JWN synsets via user interaction. We also integrated JWO and JWN using a domain ontology development environment, DODDLE-OWL. We also propose a method for building a class hierarchy with defined properties by elevating the common properties defined in sibling classes to higher classes in JWO.

Large-Scale Ontology Development and Agricultural Application Based on Knowledge Domain Framework

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management – the knowledge domain framework (KDF), and introduces an integrated development environment (IDE) named large-scale ontology development environment (LODE), which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the principle of knowledge domain framework and LODE was described briefly.

Semantic web reasoners and languages

Semantic web reasoners and languages enable the semantic web to function. Some of the latest reasoning models developed in the last few years are: DLP, FaCT, RACER, Pellet, MSPASS, CEL, Cerebra Engine, QuOnto, KAON2, HermiT and others. Some software tools such as Protege, Jena and others also have been developed, which provide inferencing as well as ontology development and management environments. These reasoners usually differ in their inference procedures, supporting logic, completeness of reasoning, expressiveness and implementation languages. Various semantic web languages with increasing expressive power continue to be developed for describing web services. We survey the some of the more recent languages like OWL-S (Ontology Web Language-Schema), WSML (Web Service Modeling Language), SWRL (Semantic Web Rule Language) and others that have been tested in early use. We also survey semantic web reasoners and their relationship to these languages.

DODDLE-OWL: Interactive Domain Ontology Development with Open Source Software in Java

In this paper, we propose an interactive domain ontology development environment called DODDLE-OWL. DODDLE-OWL refers to existing ontologies and supports the semi-automatic construction of taxonomic and other relationships in domain ontologies from documents. Integrating several modules, DODDLE-OWL is a practical and interactive domain ontology development environment. In order to evaluate the efficiency of DODDLE-OWL, we compared DODDLE-OWL with popular manual-building method. In order to evaluate the scalability of DODDLE-OWL, we constructed a large sized ontology over 34,000 concepts in the field of rocket operation using DODDLE-OWL. Through the above evaluation, we confirmed the efficiency and the scalability of DODDLE-OWL. Currently, DODDLE-OWL is open source software in Java and has 100 and more users from 20 and more countries.

Ontologies for Supporting Engineering Design Optimization

This paper presents an optimization ontology and its implementation into a prototype computational knowledge-based tool dubbed ONTOP (ontology for optimization). Salient feature of ONTOP include a knowledge base that incorporates both standardized optimization terminology, formal method definitions, and often unrecorded optimization details, such as any idealizations and assumptions that may be made when creating an optimization model, as well as the model developer’s rationale and justification behind these idealizations and assumptions. ONTOP was developed using Protégé, a Java-based, free open-source ontology development environment created by Stanford University. Two engineering design optimization case studies are presented. The first case study consists of the optimization of a structural beam element and demonstrates ONTOP’s ability to address the variations in an optimal solution that may arise when different techniques and approaches are used. A second case study, a more complex design problem that deals with the optimization of an impeller of a pediatric left ventricular heart assist device, demonstrates the wealth of knowledge ONTOP is able to capture. Together, these test beds help illustrate the potential value of an ontology in representing application-specific knowledge while facilitating both the sharing and exchanging of this knowledge in engineering design optimization.

Debugging unsatisfiable classes in OWL ontologies

As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. Existing ontology development environments provide some limited support, in conjunction with a reasoner, for reporting errors in OWL ontologies. Typically, these are restricted to the mere detection of, for example, unsatisfiable concepts. However, the diagnosis and resolution of the bug is not supported at all. For example, no explanation is given as to why the error occurs (e.g., by pinpointing the root clash, or axioms in the ontology responsible for the clash) or how dependencies between classes cause the error to propagate (i.e., by distinguishing root from derived unsatisfiable classes). In the former case, information from the internals of a description logic tableaux reasoner can be extracted and presented to the user (glass box approach); while in the latter case, the reasoner can be used as an oracle for a certain set of questions and the asserted structure of the ontology can be used to help isolate the source of the problems (black box approach). Based on the two approaches, we have integrated a number of debugging cues generated from our reasoner, Pellet, in our hypertextual ontology development environment, Swoop. A conducted usability evaluation demonstrates that these debugging cues significantly improve the OWL debugging experience, and point the way to more general improvements in the presentation of an ontology to users.

Law and order: Assessing and enforcing compliance with ontological modeling principles in the Foundational Model of Anatomy

The objective of this study is to provide an operational definition of principles with which well-formed ontologies should comply. We define 15 such principles, related to classification (e.g., no hierarchical cycles are allowed; concepts have a reasonable number of children), incompatible relationships (e.g., two concepts cannot stand both in a taxonomic and partitive relation), dependence among concepts, and the co-dependence of equivalent sets of relations. Implicit relations—embedded in concept names or inferred from a combination of explicit relations—are used in this process in addition to the relations explicitly represented. As a case study, we investigate the degree to which the Foundational Model of Anatomy (FMA)—a large ontology of anatomy—complies with these 15 principles. The FMA succeeds in complying with all the principles: totally with one and mostly with the others. Reasons for non-compliance are analyzed and suggestions are made for implementing effective enforcement mechanisms in ontology development environments. The limitations of this study are also discussed.

Debugging Unsatisfiable Classes in OWL Ontologies

As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. Existing ontology development environments provide some limited support, in conjunction with a reasoner, for reporting errors in OWL ontologies. Typically these are restricted to the mere detection of, for example, unsatisfiable concepts. However, the diagnosis and resolution of the bug is not supported at all. For example, no explanation is given as to why the error occurs (e.g., by pinpointing the root clash, or axioms in the ontology responsible for the clash) or how dependencies between classes cause the error to propagate (i.e., by distinguishing root from derived unsatisfiable classes). In the former case, information from the internals of a description logic tableaux reasoner can be extracted and presented to the user (glass box approach); while in the latter case, the reasoner can be used as an oracle for a certain set of questions and the asserted structure of the ontology can be used to help isolate the source of the problems (black box approach). Based on the two approaches, we have integrated a number of debugging cues generated from our reasoner, Pellet, in our hypertextual ontology development environment, Swoop. A conducted usability evaluation demonstrates that these debugging cues significantly improve the OWL debugging experience, and point the way to more general improvements in the presentation of an ontology to users.

Building a chemical ontology using Methontology and the Ontology Design Environment

Methontology provides guidelines for specifying ontologies at the knowledge level, as a specification of a conceptualization. ODE enables ontology construction, covering the entire life cycle and automatically implementing ontologies. To meet the challenge of building ontologies, we have developed Methontology, a framework for specifying ontologies at the knowledge level, and the Ontology Development Environment. We present our experience in using Methontology and ODE to build the chemical ontology.