Web Ontology Language (owl)

CAD models using a boundary representation (BREP) are widely used across industrial applications. However, extracting and interpreting information from these models typically requires expert knowledge. In this paper, we present a GraphRAG-based system that lowers this barrier by enabling natural language access to detailed geometric and topological information. The approach builds upon an ontological representation of geometries, which is accessible via the semantic query language SPARQL. Large language models are used to translate user questions into formal queries and generate natural language replies, allowing for flexible, domain-aware interaction with the geometric data. The effectiveness of the system is demonstrated through a systematic evaluation using diverse CAD models and a range of natural language queries. Our system empowers domain experts to explore and analyze complex CAD models without requiring expertise in query languages or ontology design. By reducing the complexity of geometric information retrieval, the GraphRAG-based system enables more accessible, automated, and intelligent use of CAD data in industrial applications.

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering.

The DrMO ontology is a domain ontology that represents knowledge underlying the composition, characterization and standardization of different materials involved in the dental restoration procedure. It will assist dentists in selecting appropriate materials based on up-to-date scientific knowledge to satisfy a patient's specific requirements, without jeopardizing their clinical time. It reuses several ontologies from the OBO foundry, especially the Oral Health and Disease (OHD) Ontology. However, the dental restoration domain is complex and also requires concepts from materials science and engineering. Thus, DrMO also incorporates knowledge from the Devices, Experimental scaffolds, and Biomaterials (DEB) and Functionally Graded Materials (FGM) ontologies to provide more comprehensive knowledge of this area of dental material than previous ontologies. However, much of the terminology from FGM is different than that used in clinical dentistry. Thus, DrMO has changed the appropriate classes to make them consistent with terminology common in dentistry. DrMO also follows ontology design best practices by reusing meta-data properties from the Dublin Core vocabulary. It captures knowledge from a set of the most recent and influential papers in Dental Materials and related fields. Links to these papers are included in the ontology as meta-data defined with Dublin Core. It is implemented in OWL2 and was developed with the Protégé 5.6 ontology editor. The ontology was created using the Ontology Development 101 methodology by Noy et. al. Several domain experts in addition to Dr. Dutta also provided their expertise. The ontology is available on GitHub and licensed via an open source license. The GitHub project includes a corresponding file of SPARQL queries that answer the competency questions defined as part of the ontology development methodology.

An important concept in Evolutionary Psychology is the cognitive module. Cognitive modules are hypothesized to be innate in the human genome and form the foundation for basic cognitive functions. Examples include language, causality, contact mechanics, and morality. This is an extension of the work in Biolinguistics pioneered by Chomsky where an innate language faculty is hypothesized to exist and is an alternative approach to the "blank slate" model of psychology that hypothesizes all learning is based on a single general learning process such as Stimulus Response or Hebbian conditioning. Although there has been extensive research on these modules, no one has created a formal model of them. This paper describes an ontology that creates a model based on structures and processes that are commonly ascribed to cognitive modules in evolutionary psychology. This illustrates how OWL can be a powerful tool for cognitive science. One of the biggest obstacles to scientific theories in the "soft" sciences is the difficulty of defining rigorous, testable models. OWL provides a tool that is abstract enough that it can model such concepts while at the same time being rigorous enough that it clarifies issues that go unobserved without a formal model. In this paper I present an ontology that models cognitive modules and describe the research I utilized to define the modules. I present a simple example that models an example of Hunter Gatherer behavior and beliefs. I discuss whether formal methods can be used to study a topic as full of contradictions as the human mind. Specifically, I address anticipated criticism from cognitive scientists such as Lakoff that it is a fantasy to think the universe, much less the mind, can be modeled by formal methods. I conclude with a brief discussion of the implications of the current model.

India’s legions of hard-working non-profit organizations strive to reduce absolute and relative poverty and improve the capabilities of communities and individuals but struggle for resources. With the introduction of a legal mandate in a 2013 revision to her Companies Act, India became the first country to mandate Corporate Social Responsibility (CSR) expenditure by large firms. Both corporations and Non-Governmental Organizations (NGOs) still struggle to meet the act’s requirements. CSR funding is unevenly distributed or unspent, neglecting many issues and regions in need. In 2021, 45% of mandated corporations remained non-compliant and less than 1% of India’s NGOs had received funding through the CSR mandate. Small, local NGOs are often extremely effective in terms of impact and outcome. They have the connections, local knowledge and agility to generate and implement sustainable solutions but are excluded from consideration if they struggle to meet transparency requirements. The goal of DaanMatch is to use technology to overcome these and other challenges in development. We are developing a system to facilitate localization of the United Nations Sustainable Development Goals (SDGs) and improve outcomes for Corporate Social Responsibility. This paper describes the DaanMatch system and the innovative technology it utilizes to match CSR with NGOs in a way that showcases small NGOs and helps level the playing field for them. Although the initial emphasis is on India, our goal is to reinvent the process of NGO evaluation, monitoring, auditing, and reporting for charitable giving and global development.

Based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers and that trust among friends positively correlates with user preference, we decided to refer to research conducted on the emerging field of trust-based recommender system. We propose to integrate the temporal factor in measuring trust between social network friends. A Trusted Friend’s calculation method is developed for determining social trusted friends in Facebook. We have, accordingly, demonstrated the importance of the interactions’ time between users. Afterwards, we have used this method in a semantic tourism recommender system as a smart e-tourism tool able to recommend items based on the users’ preferences and their trusted friends’ preferences. We have also applied our tourism recommender system for the case of medical tourism in Tunisia to help users interested in traveling to Tunisia for medical purposes. Finally, we have implemented the system and collect...

Based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers and that trust among friends positively correlates with user preference, we decided to refer to research conducted on the emerging field of trust-based recommender system. We propose to integrate the temporal factor in measuring trust between social network friends. A Trusted Friend’s calculation method is developed for determining social trusted friends in Facebook. We have, accordingly, demonstrated the importance of the interactions’ time between users. Afterwards, we have used this method in a semantic tourism recommender system as a smart e-tourism tool able to recommend items based on the users’ preferences and their trusted friends’ preferences. We have also applied our tourism recommender system for the case of medical tourism in Tunisia to help users interested in traveling to Tunisia for medical purposes. Finally, we have implemented the system and collect...

Healthcare Vehicular Cloud Computing (VCC) systems use heterogeneous network access technologies to fulfill the requirements of medical services and enhance the health monitoring and care of vehicular users. In such a system, efficient network selection algorithms are required to satisfy the constraints of critical medical services such as health monitoring using sensors, live streaming of medical video and clinical data transmission. Therefore, in this paper a network selection scheme for supporting medical services in VCC systems is proposed.

Virtual tourism is a novel trend that enhances the experience the users perceive from touristic places, such as archaeological sites. Drones are equipped with 360o video cameras and used for video capturing of the heritage sites. The video material is streamed to the users in real time, enriched with additional 3D, Augmented Reality (AR) or Mixed Reality (MR) material. Furthermore, the selection of the appropriate flying route for each drone should be performed, in order to provide a satisfactory tour experience to the user, considering his preferences about specific monuments. To address this issue, this paper describes a heritage route selection scheme for supporting real-time virtual tours in sites with cultural interest using drones. The proposed scheme applies a Fuzzy Multiple Attribute Decision Making (FMADM) algorithm, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS), to accomplish the ranking of the candidate heritage routes. The algorithm uses Interval-Va...

Ontology has been used as a knowledge representation method and it has been widely used to improve the information retrieval. The ontology is used to efficiently capture the semantics of the information which significantly improves the performance of the information retrieval process. The importance of ontology has been proved in the areas of semantic data mining, knowledge management and information retrieval. Protégé is the most commonly used tool for developing ontology to describe the concepts and the relationship between those concepts to create class hierarchy. In this paper, a methodology for the development of ontology for sports domain is proposed and it consists of 6 phases. An overview of these development phases has been described. Finally, using Jena the sports ontology Owl model is converted into Jena ontmodel which is further used for document clustering process.

Making the customers most preferable and building consumer centric structured models lead to the business process profitable. This paper mainly focuses on increasing customization features of service based business processes with the knowledge of existing web services. We present customization by a new Web Ontology Language (OWL) based on the semantic markup language for web based information. The ontology is used for composition of web services. The dynamicity behavior in the web data is proposed for increasing web performance during any changes in web data. The system can get the user needs and properly utilize source interfaces. The results show that the proposed method is more automatic and dynamic than the existing composite web service mechanisms.

The need for smart e-learning environments is resulting in new challenges for researchers and practitioners to develop intelligent systems that can be used to automate the Higher Education (HE) activities in an intelligent way. Some common examples of such activities are “analyzing, finding, and ranking the right resource to teach a course,” “analyzing and finding the people with common research interests to start joint research projects,” and “using data analytics and machine reasoning techniques for conducting the exams with different levels of complexities.” Ontological reasoning and smart data analytics can play an important role in analyzing and automating these HE activities and processes. In this paper, we present a framework named as Higher Education Activities and Processes Automation Framework (HEAPAF). The HEAPAF framework can be used to identify, extract, process, and produce the semantically enriched data in machine understandable format from different educational resources. We also present the Higher Education Ontology (HEO) that we designed and developed to accommodate the HE data and then to perform analysis and reasoning on it. As a proof of concept, we present a case study on the topic, “analyzing, finding, and ranking the right resources to teach a course,” which can dramatically improve the learning patterns of students in the growing smart educational environment. Finally, we provide the evaluation of our framework as evidence of its competency and consistency in improving academic analytics for educational activities and processes by using machine reasoning.

Virtual tours using drones enhance the experience the users perceive from a place with cultural interest. Drones equipped with 360 o cameras perform real-time video streaming of the cultural sites. The user preferences about each monument type should be considered in order the appropriate flying route for the drone to be selected. This paper describes a scheme for supporting personalized real-time virtual tours in sites with cultural interest using drones. The user preferences are modeled using the MPEG-21 and the MPEG-7 standards, while Web Ontology Language (OWL) ontologies are used for the description of the metadata structure and semantics. The Metadata-aware Analytic Network Process (MANP) algorithm is proposed in order the weights about the user preferences for each monument type to be estimated. Subsequently, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS) algorithm accomplishes ranks the candidate heritage routes. Finally, after each virtual tour, the user preferences metadata are updated in order the scheme to continuously learn about the user preferences.

Virtual tourism is a novel trend that enhances the experience the users perceive from touristic places, such as archaeological sites. Drones are equipped with 360 o video cameras and used for video capturing of the heritage sites. The video material is streamed to the users in real time, enriched with additional 3D, Augmented Reality (AR) or Mixed Reality (MR) material. Furthermore, the selection of the appropriate flying route for each drone should be performed, in order to provide a satisfactory tour experience to the user, considering his preferences about specific monuments. To address this issue, this paper describes a heritage route selection scheme for supporting real-time virtual tours in sites with cultural interest using drones. The proposed scheme applies a Fuzzy Multiple Attribute Decision Making (FMADM) algorithm, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS), to accomplish the ranking of the candidate heritage routes. The algorithm uses Interval-Valued Trapezoidal Fuzzy Numbers (IVTFN) for the representation of heritage routes evaluation values. Performance evaluation shows that the suggested method produces better results compared to the Fuzzy Topsis (FTOPSIS) by selecting the most appropriate flying route for the drone.

Healthcare Vehicular Cloud Computing (VCC) systems use heterogeneous network access technologies to fulfill the requirements of medical services and enhance the health monitoring and care of vehicular users. In such a system, efficient network selection algorithms are required to satisfy the constraints of critical medical services such as health monitoring using sensors, live streaming of medical video and clinical data transmission. Therefore, in this paper a network selection scheme for supporting medical services in VCC systems is proposed.

The Semantic Web is deemed to be the next major step in associating information in the web. It facilitates the linkage of data between two related sources and to be understood by the machine. Ontology is the essential building block of the semantic web for making the machine to communicate with other machines. Constructing the ontology for the general domain is very tedious and the construction of Medicinal plant ontology to be one of the most useful ontology for the average person to understand the usage of medicinal plants. Earlier research on ontology design methodologies shows that constructing ontology using manual process is tedious, but it is difficult to create the ontology automatically, since the sources of the input are in different forms, like, structured, semi structured, or unstructured data. In this paper, we present an automated ontology construction methodology for the medicinal plants. Java is used to extract the Wikipedia web pages related to medicinal plants, Java enabled application (Jena) is used for the automated construction of ontology, and Protégé is used for verifying the consistency of the ontology. Jena with SPARQL is used for inferring the usage of Indian medicinal plants. An Automated Knowledge framework for the Indian Medicinal Plants (AKIMP) is developed based on this proposed concept. The system is exhibited for fifty thousand concepts and gives the related information from Traditional Knowledge Digital Library (TKDL)[12] and Wikipedia web pages. Thus, it is possible to construct the automated knowledge base to retrieve the required resultant webpage based on the users’ need. This automated ontology construction methodology is an improvised method in analyzing the use of medicinal plants.

The microbial taxonomy is based on the characteristics of microorganisms that can be objectively observed and measured. There are many scheme of microbial classification, but the latest is the three domain system and is the most accepted. Ontologies are the new form of knowledge representation that acts in synergy with agents and Semantic Web Architecture. Ontologies define domain concepts and the relationships between them, and thus provide a domain language that is meaningful to both humans and machines. The relationships in Ontology are explicitly named and developed with specification of rules and constraints so that they reflect the context of domain for which the knowledge is modelled. Ontologies can be built by using various GUI based software tools, known as Ontology editors. Among all editors Protégé is widely supported by a huge research community. For effective use of Ontology, protégé provides a query interface known as SPARQL query panel. SPARQL is a syntactically-SQL-like language for querying RDF graphs. Microbial Taxonomy Ontology is developed for the three domain system of microbes for the domain Bacteria which will be helpful for the study of Agriculturally Important Microbes (Bacteria). This ontology is built in the Protégé OWL editor from Domain to Genus level. Using this ontology, a query interface can be developed that will help detailed study of microbial taxonomy, classification of microbes as well as exchange knowledge between software agents and systems.