Semantic Web Ontologies Research Articles

Battery testing ontology: An EMMO-based semantic framework for representing knowledge in battery testing and battery quality control

The demand for advanced battery management systems (BMSs) and battery test procedures is growing due to the rising importance of electric vehicles (EVs) and energy storage systems. The diversity of battery types, chemistries and application scenarios presents challenges in designing and optimizing BMSs and determining optimal battery test strategies. To address these challenges, semantic web technologies and ontologies offer a structured and common vocabulary for information sharing and reuse in battery management and testing. This work introduces the Battery Testing Ontology (BTO), a standardized, comprehensive, and semantically flexible framework for representing knowledge in electrical battery testing and quality control. BTO models a variety of electrical battery cell tests, specifying required test hardware and calibration procedures, mechanical fixturing of batteries, and referencing electrical measurement data. For example, it supports electrochemical impedance spectroscopy, self-discharge and high-voltage separator tests, the latter specifically demonstrating separator requirements, hardware specifications, and measurement details. Positioned within the ontology ecosystem of materials science, BTO aligns with the Elementary Multiperspective Material Ontology (EMMO) and related domain ontologies such as the Characterization Methodology Ontology (CHAMEO). This work elaborates on BTO’s development, structure, components and applications, highlighting its significant contributions to the field of battery testing.

A Hybrid Approach to Ontology Construction for the Badini Kurdish Language

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology.

Integrated representation of technical systems with BIM and linked data: TUBES system ontology

Technical systems in the Architecture, Engineering, Construction, and Operations (AECO) industry are complex interconnected structures. To cope with this high complexity, large amounts of data must be processed and exchanged throughout the whole life cycle of these systems. Building Information Modeling (BIM) and Semantic Web Technologies (SWT) offer the possibility to structure and link this data as well as to define common terminology. Semantic Web ontologies describing HVAC systems in the AECO industry have been developed, but an ontology for an integrated representation based on general system theory is missing. Therefore, this study proposes the TUBES System Ontology (TSO) as a knowledge representation for an integrated, explicit representation of linked technical systems, their hierarchical subdivision, structural and functional interconnections, and relationships to spatial entities. The contribution is validated on two example models, and the results are critically discussed, pointing out corresponding limitations as well as the need for further research.

Semantic Web Usage in the Tourism Industry in Andalusia, Spain

Andalusia got the 4th place of tourists’ favorite destinations among 17 autonomous communities in Spain in September 2023 and almost returned to pre-pandemic normality regarding tourism. Although Spain is famous for its “sun and beach”, its tourist industry is facing new challenges in socio-economic, environmental and territorial issues; therefore, the role of artificial intelligence cannot be ignored in the evolution and transformation of the tourism business. AI algorithms reinforce decision-making capabilities through analysing real-time data. Effectiveness and efficiency of AI algorithms are dependent on well-structured, well-described and machine-readable meta data network which can be provided by Semantic Web standards. This article empirically study the current usage of Semantic Web formats, ontologies and touristic topics in 65 tourist-relevant web sites in Andalusia. Results show that just 57 % of examined sites used semantic markup with most of the semantic annotations (98.5%) made in three formats: Microdata, JSON-LD and RDFa. Furthermore, the Ontologies used in almost 90% of markups are ‘Schema.org’, ‘Data Vocabulary’ and ‘RDF’, but none of the special tourism ontologies appeared in examined tourism web sites. Therefore, Andalusia tourism websites should be enhanced with much more widespread Semantic Web in order to achieve a better performance in a future in which AI technologies will be widespread.

Exploring the Landscape of Smart Cities: A Comprehensive Review of IoT and Cyber-Physical Systems

They focus on housing, well-being, equality, clean energy and fair conditions. The cyber-physical approach involves the development of IoT and Cyber-Things. Smart cities have a variety of use cases, including electricity and transportation. Automating is used for efficiency in industrial manufacturing. An integrated supply and demand side management system is required for the reliability, security and ability to manage the power grid. This paper introduces an integrated energy approach, enhances existing standards, and establishes a shared basis for multidisciplinary planning. It also introduces new semantic network ontologies to provide a comprehensive framework for solving resource-related challenges. This new approach aims to fill the gaps in current standards and create an integrated environment for multi-stakeholder collaboration, using a semantic web ontology for communication and improved decision making in energy systems Provides information integrated, including various forms of smart cities With flexibility for flexibility and inclusion in the energy industry, can accommodate the specific characteristics and needs of various smart city applications In this study, computing -physical system (CPS), software-defined network (SDN), internet (IoT). ), and analyze how smart cities are connected. CPS combines physical channels with electronic systems to provide increased network management efficiency and flexibility. SDN improves dynamic capacity and flexibility, while IoT is more connected for real-time data exchange and decision-making.

A state-of-the-art universal machine learning framework for decoding suspect coded messages

This proposed framework aims to mitigate global terrorist threats, suspicious activities, and legal violations by introducing an innovative system that monitors encrypted short-text messages across instant messaging programs. In the current landscape, criminals, terrorists, and clandestine entities extensively employ encrypted messages for illicit endeavours on a global scale. Despite the escalating use of encrypted messages on social media platforms, a comprehensive solution for combating online crime is notably absent.The outlined framework addresses this gap by employing a cryptographic system that integrates semantic web ontology, a Predefined Dataset, and machine learning technology for encryption, decryption, and cryptanalysis. A key feature is the framework's ability to predict potential crimes by analyzing blogs for suspicious terminology conveyed through coded information. The identified information is then promptly communicated to law enforcement agencies, thereby alleviating the burden on numerous security entities and offering an efficient strategy against global crime.What sets this proposed framework apart is its unique utilization of encrypted text, a dimension that has remained unexplored in previous studies. The cohesive integration of its components expedites the process of anticipating crime types and significantly reduces the incidence of encrypted crimes on a global scale.

Data integration for digital twins in the built environment based on federated data models

Improving the efficiency of operations is a major challenge in facility management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in silos that are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails or bureaucratic processes. Digital twins represent an emerging approach to returning awareness and control to facility managers by automating all levels of information access (from granular data to defined key performance indicators and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in facility management, including construction, operation and maintenance data, and Internet of things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece Building (Cambridge, UK) demonstrates the approach by enabling fault detection and diagnosis of the heating, ventilation and air-conditioning system for facility management.

SCMD suspicious cryptographic message detection

The proposed framework aims to prevent global crimes, specifically terrorist attacks, bomb blasts, and drone attacks, by using the Suspicious Cryptographic Message Detection (SCMD) system to detect and decrypt suspicious messages. Terrorists often use encrypted messages to communicate with their teammates in different parts of the world, making it difficult for authorities to monitor their activities. The framework addresses this issue by detecting with gradient boosting algorithm and cryptanalyzing by using Simple Substitution Cipher techniques to decrypt suspicious messages and predict potential criminal activity. The framework utilizes various emerging trends in information technology, including machine learning techniques, pre-defined decision rules, wordnet, and semantic web ontology, to predict the type of crime, the criminal's name and location, and other criminal details. It comprises multiple components, including Semantic web Ontology, Suspicious Database, and machine learning techniques, to facilitate the detection and reporting of criminal activity. When the server receives a suspicious encrypted message, the framework detect and decrypts it and predicts the type of crime based on microblogs before it can be executed by the criminals. The details of the criminals are then alerted to the cybercrime department, reducing the burden on security departments and enhancing public safety. In summary, the framework provides a comprehensive solution to the challenges posed by encrypted messages in criminal activities. It uses advanced technologies to identify and predict potential criminal activities, enabling law enforcement agencies to take preemptive actions to prevent major and minor attacks, including terrorist attacks.

Capabilities and skills for manufacturing planning in an automotive use case scenario

Abstract The necessity for increased flexibility in production and handling of high-variant product families is a strong trend in Industry 4.0 scenarios. A promising approach to achieve this is to replace the rigid programming of the manufacturing management systems by a declarative description of machines and their functionality. This allows for a dynamic allocation of tasks to resources. This paper reports the ongoing work on applying the concept of capabilities and skills to an industrial application example in order to investigate its potential benefits. To apply the abstract concept of capabilities and skills to the production scenario it is implemented using specific technologies. These include Semantic Web ontologies, constraint solving methods and OPC UA for skill invocation and communication.

Defeasible RDFS via rational closure

In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a notable approach. In recent years, RC has gained popularity in the context of Description Logics (DLs), the logic underpinning the standard semantic Web Ontology Language OWL 2, whose main ingredients are classes, the relationship among classes and roles, which are used to describe the properties of classes.In this work, we show instead how to integrate RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language.To do so, we start from ρdf, a minimal, but significant RDFS fragment that covers the essential features of RDFS, and then extend it to ρdf⊥, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible ρdf⊥ via a typical RC construction allowing to state default class/property inclusions.Furthermore, to overcome the main weaknesses of RC in our context, i.e., the “drowning problem” (viz. the “inheritance blocking problem”), we further extend our construction by leveraging Defeasible Inheritance Networks (DIN) defining a new non-monotonic inference relation that combines the advantages of both (RC and DIN). To the best of our knowledge this is the first time of such an attempt.In summary, the main features of our approach are: (i) the defeasible ρdf⊥ we propose here remains syntactically a triple language by extending it with new predicate symbols with specific semantics; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra non-monotonic capabilities; (iii) the defeasible entailment decision procedure is built on top of the ρdf⊥ entailment decision procedure, which in turn is an extension of the one for ρdf via some additional inference rules favouring a potential implementation; (iv) the computational complexity of deciding entailment in ρdf and ρdf⊥ are the same; and (v) defeasible entailment can be decided via a polynomial number of calls to an oracle deciding ground triple entailment in ρdf⊥ and, in particular, deciding defeasible entailment can be done in polynomial time.

FAIR Digital Objects in Official Statistics

FAIR Digital Objects in Official Statistics

Covid-19 Vaccination Classification of Opinion Mining with Semantic Knowledge-based Decision Making

The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning, which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool. The data will be retrieved through SPARQL which helps to retrieve complex queries, followed by the output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment plans for the respective individuals i.e., students based on their given details which include diagnosis, symptoms, and vaccination history. The information given by the students can be automatically processed and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the students are inferred from the given knowledge.

Knowledge Graphs, Metadata Practices, and Badiou's Mathematical Ontology

Metadata practices in libraries have been shifting towards a graph-centric data model for a number of years due to the influence of the Semantic Web on metadata standards as well as the ongoing engagement of libraries with linked data. This trend is likely to be sustained by the growth of the knowledge graph domain, which is animated by the interests of large technology companies and which represents a continuation of earlier programmes such as expert systems and the Semantic Web. Given the role of Semantic Web ontologies in knowledge graph development and the relevance of philosophical questions of ontology to cataloguing theory, metadata practitioners require theoretical frameworks suitable for conceptualizing the knowledge graph data model’s mixture of data and ontology. To that end, this paper considers the mathematical ontology of philosopher Alain Badiou, which employs set theory to schematize a theory of the multiple. It outlines how Badiou’s ontology is compatible with the graph data model and what it offers to metadata practitioners seeking to critically engage the knowledge graph paradigm.

Meaning Negotiation Based on Merged Individual Context Ontology and Part of Semantic Web Ontology

The meaning negotiation process has become a confusing term in the pragmatic web. It does not depend only on convenience to agree, but requires an excellent choice of terms used in the negotiated domain. The presented paper proposes a meaning negotiation model using ontologies merging into several domains and contextual situations. The idea is to design meaning negotiation scenarios using ontologies merging extended into several domains. The results obtained show the impact of merging in meaning negotiation on the multi-agent systems. The influence of merging is interpreted by the dependence between two variables: the meaning negotiation step numbers and the merging ontologies. The authors observe the difference between meaning negotiation step numbers with and without merging. They conclude that merging ontologies has a positive influence on meaning negotiation in different contextual situations of scenarios. To demonstrate the influence, an observable analytical study proof under statistical control is applied.

Application of Rough Concept Lattice Model in Construction of Ontology and Semantic Annotation in Semantic Web of Things

In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data.

DEVELOPMENT OF A VISUAL SEMANTIC WEB ONTOLOGY BASED LEARNING MANAGEMENT SYSTEM

This paper specifically aim at developing a visual semantic web ontology based e-Learning management system. The existing e-learning platforms have failed to address some challenges including managing the huge e-Learning content that is continuously grows on the web, meeting the learner's requirements when searching for an electronic learning content, representing the knowledge in a format that is easy to read and making it capable of thinking and thus allowing the re-use of e-Learning objects. In addition, most search results contain some irrelevant documents that cannot serve the need of the learner. Therefore, this thesis designed a common interface for learners, instructors, and administrators of academic institutions to upload and access learning materials. The new system created an ontology repository to maintain learners’ personalization details and taxonomy of the learning resources and integrate the knowledge embedded in many remote ontologies for improved e -Learning management system. In addition, an intelligent search engine through which learners can semantically search their learning materials and filter the search results returned by the search engine based on some semantics and the user’s preferences created. These designs simulated using a web-system developed with PHP, MySQL and JavaScript. The System Design followed the Object Oriented Design and Analysis Methodology for componentization of the system modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language extensively used to simplify the explanation of the system modules. The software performance wastested; the result shows that visual semantic web ontology-based e-Learning management system achieved 95% accuracy in returning the desired web search result.

An ontology to support flow system descriptions from design to operation of buildings

The interoperability of information from design to operations is an acknowledged challenge in the fields of architecture, engineering and construction (AEC). As a potential solution to the interoperability issues, there has been increasing interest in how linked data and semantic web technologies can be used to establish an extendable data model. Semantic web ontologies have been developed for the AEC domain, but an ontology for describing the energy and mass flow between systems and components is missing. This study proposes the Flow Systems Ontology (FSO) for describing the composition of flow systems, and their mass and energy flows. Two example models are expressed using FSO vocabulary. SPARQL Protocol and RDF Query Language (SPARQL) queries are performed to further demonstrate and validate the ontology. The main contribution consists of developing FSO as an ontology complementary to the existing ontologies. Finally, the paper introduces a roadmap for future developments building on FSO.

A Genealogy Ontology Containing Turkish Kinship Terms for Traditional Families

Today, with the innovations brought by technology, many information, official documents, correspondence, and evaluation data kept in archives have been transferred to digital environment. One of the areas where the digitalization process has started and continues rapidly is population and registry information. However, the existing systems are sometimes insufficient to present the log information clearly. Existing systems used in relevant government institutions and libraries can only present kinship ties in a hierarchical structure due to the lack of necessary infrastructure resources. The family tree created is insufficient to establish parallel relationships. However, it is obvious that in most judicial and institutional cases, it will shed light on the current situation in parallel kinship relations. In addition, existing genealogies are insufficient to reveal semantic patterns between the concepts of family, person, place, time, and event while listing kinship relations. In addition to all these, the existence of cultural differences between nations and the expression of kinship relations with different terms require the definition of a common family tree. In this study, semantic web technologies, ontologies, and related data, which have been widely used in many fields in recent years, are structured to provide a clearer conceptualization of kinship relations. The study also discusses the alignment and reusability of the developed genealogical ontology with upper-level ontologies so that it can be used more broadly. Keywords: Genealogical Ontology, Semantic Web Technologies, Linked Data, Protégé, Ontology Methodologies DOI: 10.7176/JSTR/7-10-10

Towards Social Semantic Journalism

User-generated content has become a valuable journalistic tool for news coverage and production. This convergence of new and old media, however, poses several challenges to established news organisations. Social media sites produce a wealth of data in the form of text, images and video that must be processed, compiled and verified within a very short timespan before being incorporated into a news story. This unstructured data that lies scattered across the web can be formalised and organised into a ‘web of data’ by Semantic Web technologies. Specifically, Semantic Web technologies have the potential to formalise and integrate artifacts produced and shared across the Social Web. Social Semantic Journalism proposes the utilisation of Semantic Web technologies, and specifically Social Semantic Web ontologies such as FOAF and SIOC, in the process of news production. This potentially provides a journalistic tool to assist in finding, aggregating and verifying user-generated content for news production.

Ontology-based approach to data exchanges for robot navigation on construction sites

As the use of autonomous Unmanned Ground Vehicles (UGV) for automated data collection from construction projects increases, construction stakeholders have become aware of a problem with inter-disciplinary semantic data sharing and exchanges between construction and robotic. Cross-domain data translation requires detailed specifications especially when it comes to semantic data translation. Building Information Modeling (BIM) and Geographic Information System (GIS) are the two digital building technologies used to capture and store semantic information for indoor structures and outdoor environments respectively. In the absence of a standard format for data exchanges between the construction and robotic domains, the tools of both industries have yet to be integrated into a coherent deployment infrastructure. In other words, the semantics of BIM-GIS cannot be automatically integrated by the robotic platforms currently being used. To enable semantic data transfer across domains, semantic web technology has been widely used in multi-disciplinary areas for interoperability. This paves the way to smarter, quicker and more precise robot navigation on construction sites. This paper develops a semantic web ontology integrating robot navigation and data collection to convey the meanings from BIM-GIS to the robot. The proposed Building Information Robotic System (BIRS) provides construction data that are semantically transferred to the robotic platform and can be used by the robot navigation software stack on construction sites. To meet this objective, first, knowledge representation between construction and robotic domains is bridged. Then, a semantic database integrated with the Robot Operating System (ROS) is developed, which can communicate with the robot and the navigation system to provide the robot with semantic building data at each step of data collection. Finally, the BIRS proposed system is validated through four case studies.