Ontology-based Knowledge Representation Research Articles

An ontology-based knowledge representation using OWL for Indonesian local regulations

<div class="WordSection1"><p>Inconsistencies in legislation can significantly hinder the effectiveness and efficiency of the central and local government administrations. The Indonesian government requires standard harmonization of each piece of legislation to prevent such problems. However, this process is often manual and requires the involvement of multiple experts with varying backgrounds. This leads to high resource expenses regarding human resources, cost, and time. To address this, a software system should be developed to detect potential disharmony among legislation. However, the system requires a well-constructed legislation conceptual model represented in an appropriate modeling language. This research aims to develop the Indonesian local regulation ontology in web ontology language (OWL), where no such ontology exists. The ontology was created using the Ontology Development 101 methodology and evaluated using competency questions and expert judgment approaches. The resulting ontology becomes a basis for developing an automatic recommendation system to detect potentially inconsistent legislation in future works.</p></div>

Knowledge-based product and process design synthesis of additively manufactured non-assembly mechanisms

Additive Manufacturing increasingly becomes a viable alternative to conventional manufacturing technologies. However, the knowledge and awareness to achieve a ready-for-manufacturing and quality-ensured design of additively manufactured products are mostly lacking. Knowledge from the manufacturing process on how certain process variables affect the product requirements must already be considered in product design to assure and potentially enhance the quality a priori, thus saving time and cost-intensive iterations. Therefore, this knowledge must be front-loaded as a basis to suitably adapt product and process design simultaneously. This contribution therefore proposes a framework for the simultaneous product and process design synthesis of existing designs by providing insight knowledge about product and process design specific to Fused Layer Modeling. This is achieved via a consolidated ontology-based knowledge representation and its integration in a design environment. Thus, the aim, in contrast to most existing approaches, is to provide this knowledge on a product- and application-specific basis to parallelise product and process design for non-assembly mechanisms and exploit currently unused potential concerning the interaction between the different domains. This can shorten development times and also compensate for a lack of background knowledge in certain areas. A practical use case highlights the general procedure, benefits, and applicability.

Ontology-based knowledge representation for traditional martial arts

Abstract Traditional martial arts are treasures of humanity’s knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries on the UNESCO list of intangible cultural heritage. In tackling the urgency of knowledge preservation through digital means, this project employs an ontology-based approach to model the conceptual realm of traditional martial arts. Accordingly, it creates the Martial Art Ontology (MAon), a comprehensive domain ontology with an annotated data resource incorporating entities and relations from embodied, epistemic, and sociocultural facets. MAon underlines the significance of embodied qualities and addresses relevant dimensions, such as kinesthetics, techniques, mnemonics, and tactics, along with stylistic, interpretative, and ideological components. It features scholarly terminology developed through literature analysis, interviews with masters, and expert validations. The instantiation of MAon is realized through annotating three archetypal Southern Chinese styles, offering exhaustive descriptions concerning techniques, forms, principles, and form sets, amongst others. In summary, the reported approach encodes the manifold of martial arts into a structured vocabulary and an interlinked data resource, accessible to both human-reading and machine-operating applications. By applying it to manifest a range of knowledge concepts, we demonstrate the potential of ontology-based datafication to create coherent representations for intangible cultural entities and to enable an interoperable data infrastructure across multimodal cultural archives.

Enhancing Learning Personalization in Educational Environments through Ontology-Based Knowledge Representation

In the digital age, the personalization of learning has become a critical priority in education. This article delves into the cutting-edge of educational innovation by exploring the essential role of ontology-based knowledge representation in transforming the educational experience. This research stands out for its significant and distinctive contribution to improving the personalization of learning. For this, concrete examples of use cases are presented in various academic fields, from formal education to corporate training and online learning. It is identified how ontologies capture and organize knowledge semantically, allowing the intelligent adaptation of content, the inference of activity and resource recommendations, and the creation of highly personalized learning paths. In this context, the novelty lies in the innovative approach to designing educational ontologies, which exhaustively considers different use cases and academic scenarios. Additionally, we delve deeper into the design decisions that support the effectiveness and usefulness of these ontologies for effective learning personalization. Through practical examples, it is illustrated how the implementation of ontologies transforms education, offering richer educational experiences adapted to students’ individual needs. This research represents a valuable contribution to personalized education and knowledge management in contemporary educational environments. The novelty of this work lies in its ability to redefine and improve the personalization of learning in a constantly evolving digital world.

An Ontology-Based Knowledge Representation Method for Typhoon Events from Chinese News Reports

Typhoons are destructive weather events. News media reports contain large amounts of typhoon information. Transforming this information into useful knowledge to provide a basis for mining typhoon knowledge and supporting disaster prevention and relief is urgently required to solve this problem. Knowledge representation can be used to address this problem, although it presents several challenges. These challenges lie in expressing the static and dynamic characteristics of typhoons and formalizing the knowledge representation method and making it suitable for machine processing. Moreover, the general Chinese time and space representation method is overly cumbersome for use in ontologies. The present study proposes an ontology-based typhoon event representation method that solves the representation problems of the typhoon static concept and dynamic features. Furthermore, it summarizes the fixed patterns of time and space in Chinese news and designs a time and space model suitable for typhoon event ontologies. From the ontology population, typhoon event ontology instances are created, and the typhoon event ontology model is applied to the analysis of typhoon processes, verifying the effectiveness of the typhoon event ontology model.

Ontology-based knowledge representation of industrial production workflow

Industry 4.0 is helping to unleash a new age of digitalization across industries, leading to a data-driven, interoperable, and decentralized production process. To achieve this major transformation, one of the main requirements is to achieve interoperability across various systems and multiple devices. Ontologies have been used in numerous industrial projects to tackle the interoperability challenge in digital manufacturing. However, there is currently no semantic model in the literature that can be used to represent the industrial production workflow comprehensively while also integrating digitalized information from a variety of systems and contexts.To fill this gap, this paper proposed industrial production workflow ontologies (InPro) for formalizing and integrating production process information. We implemented the 5 M model (manpower, machine, material, method, and measurement) for InPro partitioning and module extraction. The InPro comprises seven main domain ontology modules including Entities, Agents, Machines, Materials, Methods, Measurements, and Production Processes. The Machines ontology module was developed leveraging the OPC Unified Architecture (OPC UA) information model. The presented InPro ontology was further evaluated by a hybrid combination of approaches. Additionally, the InPro ontology was implemented with practical use cases to support production planning and failure analysis by retrieving relevant information via SPARQL queries. The validation results also demonstrated that using the proposed InPro ontology allows for efficiently formalizing, integrating, and retrieving information within the industrial production process context.

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

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

Research on knowledge representation and modeling of health management based on fuzzy ontology

The long-term maintenance of good condition for equipment is the basis of carrying out combat missions under the high technology and fast pace of modern war. However, the knowledge in the health management field at present has the characteristics of distribution, multi-source, heterogeneity and uncertainty, which seriously affects the efficiency of knowledge sharing and reuse. In order to improve the utilization of health management knowledge, an ontology-based knowledge representation method is proposed to describe knowledge in a unified and standardized way, and the classical ontology is extended to express the uncertain knowledge in the field of health management. In addition, to improve the maintenance and knowledge updating efficiency, a global ontology model and a hierarchy, time and activity (HTA) ontology model are constructed. This paper takes the guidance subsystem of a missile as an example to illustrate the process of knowledge modeling. The results show this method realizes knowledge sharing in the health management field and can provide decision support for health management of equipment.

An Ontology Based Knowledge Representation for Coordinated SS-bots

Collective behaviour empowers biological species to achieve remarkable swarm level results through distributed actions. For example, social spiders achieve coordinated activities that can lead to plausible outcomes such as preying, mating, or building webs. Developers of simulated swarm systems are increasingly moving away from insinuating individualistic robotic devices, gravitating towards collective swarms to achieve common goals. However, for this to happen, it is imperative to understand the principles that underpin successful simulated coordination of robotic devices in swarms. In this article, we investigate the principles behind artificial swarm systems built on the instinctive behaviours of simulated social spider-like devices (SS-bots). We classify these principles into six interconnected knowledge domains, including (a) environment, (b) SS-bot architecture, (c) SS-bot mission planning, (d) SS-bot communication, (e) SS-bot operators, and (f) metadata and swarm-level data. The key features of each domain are discussed, and an SS-bot ontology is proposed.

An ontology-based KBE application for supply chain sustainability assessment

Product Lifecycle Management (PLM) plays a key role in digital transformation demanded by Industry 4.0 and life cycle assessment, including sustainability assessment. Knowledge Based Engineering (KBE) applications can support PLM by integrating heterogeneous knowledge from different stages throughout the product life. However, the integration of knowledge from different stages and teams can cause misunderstanding if not represented in a unified form. Furthermore, different forms of knowledge used by different software are neither machine-readable nor human-readable, which also sets obstacles to knowledge integration in KBE applications. Supply chain sustainability assessment is such a scenario that entails integrating knowledge from different sources. This paper firstly implements a sustainability assessment method from other scholar to calculate the supply chain sustainability performance and adapts a sustainability assessment ontology for supply chain sustainability assessment. Then, an example KBE application is developed by implementing the sustainability assessment ontology and calculation method to simulate the knowledge sharing and integration between different teams. Finally, through this example application, it is discussed that the implementation of ontology to represent knowledge in PLM application for collaborative tasks like sustainability assessment can increase the efficiency of data sharing and integration. This paper is a proof of concept for the ontology-based framework. This framework can facilitate to represent knowledge but not create new knowledge, which means it can increase the efficiency of the software development, but can not provide a better calculation method and assessment framework for supply chain sustainability assessment. • An ontology-based application simulates the knowledge sharing and integration in sustainability assessment. • Ontology-based knowledge representation provides a unified and flexible data format. • Ontology-based knowledge representation makes a unified knowledge base possible. • Ontology increases the efficiency of data sharing and integration in collaborative tasks. • High-efficient software development contributes to supply chain sustainability management.

A Decision-Making Framework to Support Urban Heat Mitigation by Local Governments

Local governments have made extensive efforts to mitigate urban overheating, cool streetscapes and cities, and protect vulnerable people. However, there is uncertainty about which urban heat mitigation strategies (UHMSs) can provide better solutions for a specific urban context. There is a compelling need for local governments to automate the decision-making process and optimise the combination of UHMSs to maximise the mitigation outcomes for their cities. We develop a novel decision-making framework that incorporates artificial intelligence (AI) techniques into urban heat mitigation in the built environment to enable an automated process of decision making. The novel decision-making framework comprises: the ontology-based knowledge representation of UHMSs and their relationships with urban contexts and performance assessment to share knowledge in urban heat mitigation domain; sensitivity analysis of the environmental, social and economic performance of UHMSs to get key variables for UHMSs; and genetic algorithm-based multi-objective optimisation of UHMSs. The novel decision-making framework enables generating the context-based optimised UHMS combinations to support local governments’ decision-making. The research outcomes will advance interdisciplinary knowledge about using AI techniques in the decision-making process for urban heat mitigation.

Uncertainty Analysis in Ontology-Based Knowledge Representation

Uncertainty Analysis in Ontology-Based Knowledge Representation

Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research

An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results.

A graph-based representation of knowledge for managing land administration data from distributed agencies – A case study of Colombia

ABSTRACT Multiple efforts have been performed worldwide around diverse aspects of land administration. However, land administration data and systems’ notorious heterogeneity remains a longstanding challenge to develop a harmonized vision. In this sense, the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources’ heterogeneity implies needs related to harmonization interoperability, sharing, and integration in land administration development. This paper proposes a graph-based representation of knowledge for integrating multiple and heterogeneous data sources (tables, shapefiles, geodatabases, and WFS services) belonging to two Colombian agencies within a decentralized land administration scenario. These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration. Our approach aims to prevent data isolation, enable cross-datasets integration, accomplish machine-processable data, and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach. A real case study demonstrates the applicability of the land administration data cycle deployed.

Ontology-based assembly knowledge representation and process file generation

Aiming at the problems of low assembly knowledge shareability and reusability as well as long generation cycle of assembly process, this paper proposes an ontology-based assembly knowledge representation method, and generates assembly process file based on this method. The assembly ontology, modelling through protégé software, has three central classes: AssemblyObject, AssemblyElement, and AssemblyTool. The assembly ontology is described in OWL language and the assembly knowledge concepts including classes and individuals are linked through properties. In addition, the assembly ontology in OWL language is parsed through Python's RDFLib library, and it is called and displayed in LabVIEW. Finally, the assembly process file containing assembly sequence and assembly process parameters is generated. This method realizes the formal description of assembly process knowledge at the semantic level and improves the shareability and reusability of assembly knowledge. Besides, the corresponding assembly process knowledge can be quickly queried and obtained through this method, improving the efficiency of assembly process planning, and providing intelligent assembly basic knowledge.

Methods and techniques for management of ontolodgy-based knowledge representation models in the context of BIG data

Ontology-based knowledge representation models in the context of big data are one way to reduce complexity for data processing across methods of semantic description. This research paper aims at providing an overview of the methods and techniques for efficient management of the ontology-based models that improve big data systems. For this case, the shapes constraint language (SHACL) for information validation was reviewed as the key method. The knowledge representation systems and reasoners are studied and reviewed in the paper as well. It describes approaches based on ontologies in the context of big data. The proper management of ontology-based knowledge representation models through offered methods and techniques brings improved data integration, big data quality, and business process integration.

Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

Ontology based knowledge representation: Case study from agriculture domain

The growth of web has touched everyone’s life from an economist, entrepreneur, and academician to a farmer. The agriculture sector is quite important for any country’s growth. Farmers can also be benefited if they are provided with relevant information they need. The reason that it is not happening on a large scale is due to many reasons. Information is available in different formats, platforms and it is highly unstructured. The availability and real time usage of this information is prohibited mainly by the way it is represented. Recently, Ontology has emerged as one of very expressive knowledge representation scheme which enables gathering information from heterogeneous sources and creates a common data model that is shared and agreed upon in diverse domains. The interoperability and deduction capabilities that Ontology offers are very useful for generating new knowledge. Most of the work pertaining to ontology development in agriculture domain is crop specific, where information about fertilizers is confined to a particular crop only. In this paper, we have designed and developed a generic ontology for fertilizers. Fertilizer itself is a concept which should be modeled independently. Even for crop specific ontology, fertilizer is one of the most important concept to be captured. So, it needs to be represented independently. Finding this research gap, an ontology in fertilizer subdomain is developed, taking into account the various issues that are faced while constructing an ontology and the enormous amount of data that is available but is not easy to structure and present in the form of ontology. We have also validated the Ontology using an available tool and domain experts have also validated the developed Ontology.

Design and Implementation of Cloud Platform Knowledge Service Mode for High-end Equipment Manufacturing

With the advancement of intelligent high-end equipment manufacturing, knowledge in the field of high-end equipment manufacturing provides decision support and knowledge innovation services for all aspects of the product life cycle, and plays an increasingly important role in design and production. However, the multi-source, heterogeneous and fragmented nature of domain knowledge brings difficulties to the organization and management of knowledge. To solve this problem, this paper proposes a cloud platform knowledge service mode for high-end equipment manufacturing throughout the product life cycle, including three-dimensional knowledge classification standard, ontology-based knowledge representation and integration, knowledge sharing through knowledge navigation, knowledge retrieval and knowledge reasoning. The application of aerospace manufacturing related fields shows that this mode can provide effective knowledge support for the product life cycle, strengthen the production collaboration between upstream and downstream enterprises in high-end equipment manufacturing, and provide a comprehensive guarantee for the aeroengine manufacturing industry chain.

Ontology-based knowledge representation for industrial megaprojects analytics using linked data and the semantic web

The fourth industrial revolution has affected most industries, including construction and those within the delivery chain of megaprojects. These major paradigm shifts, however, did not considerably improve the track record in predicting project outcomes and estimating required resources. One reason is the lack of unified data definitions and expandable knowledge representation across project lifecycle to represent megaprojects for analytics. This paper proposes and evaluates a unified ontology for project knowledge representation that facilitates data collection, processing, and utilization for industrial megaprojects through their lifecycle. The proposed Uniform Project Ontology, or UPonto, provides a data infrastructure for project analytics by enabling logical deductions and inferences, and flexible expansion and partitioning of the data utilizing linked data and the semantic web. The ontology facilitates cost normalization processes, temporal queries, and graph queries using SPARQL, while defining universal semantics for a wide range of project risk factors and characteristics based on comprehensive research of the empirical project risk and success literature augmented by practical considerations gained through expert consultations. UPonto forms the basis for a project knowledge graph to utilize unstructured data; it as well provides semantic definitions for smart IoT agents to consume project risk data and knowledge.