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Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.

Purpose Facilitating the information exchange and interoperability between stakeholders during the life-cycle of an asset can be one of the fundamental necessities for developing an enhanced information exchange framework. Such a framework can also improve the successful accomplishment of building projects. This paper aims to use Semantic Web technologies for facilitating information exchange within existing building projects. Design/methodology/approach In real-world building projects, the construction industry’s information supply chain may initiate from near scratch when new building projects are started resulting in diverse data structures represented in unstructured data sources, like Excel spreadsheets and documents. Large-scale data generated throughout a building's life-cycle requires exchanging and processing during an asset's Operation and Maintenance (O&M) phase. Building information modelling (BIM) processes and related technologies can address some of the challenges and limitations of information exchange and interoperability within new building projects. However, the use of BIM in existing and retrofit assets has been hampered by the challenges surrounding the limitations of existing technologies. Findings The aim of this paper is twofold. Firstly, it briefly outlines the framework previously developed for generating semantically enriched 3D retrofit models. Secondly, a framework is proposed focussing on facilitating the information exchange and interoperability for existing buildings. Semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies are used to enhance and improve the proposed framework. Originality/value The proposed framework is evaluated by implementing an example application and the Resource Description Framework data produced by the previously developed framework. The proposed approach makes a valuable contribution to the asset/facilities management (AM/FM) domain. It should be of interest to various FM practices for existing assets, such as the building information/knowledge management for design, construction and O&M stages of an asset’s life-cycle.

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.

Due to the absence of a comprehensive knowledge system for modeling ocean circulation, there is ambiguity and diversity in the semantic expression of ocean circulation. This makes it difficult to organize and share relevant spatiotemporal data effectively. This paper addresses the issue of ocean circulation by introducing ontological theory and methodology based on a comprehensive analysis of domain knowledge. Through a comprehensive analysis of the conceptual and relational characteristics of different elements, we define classes, properties, spatiotemporal relationships, and inference conditions with which to formally express concepts and relationships in ocean circulation, and finally complete the construction of ocean circulation ontology. The formal expression of the Equatorial Counter Current is presented as an example with which to validate the effectiveness of ontological construction. Additionally, an ontology-based knowledge base of ocean circulation is proposed. The construction framework is described, and several examples of knowledge base queries are also illustrated. The results demonstrate that this ontology can effectively represent the relevant knowledge within ocean circulation and provide a meaningful reference for investigating knowledge sharing and semantic integration within this field.

Cloud manufacturing (CMfg) is a new service-oriented networked manufacturing paradigm inspired by cloud computing. It provides high-efficiency and intelligent manufacturing services by organizing isolated manufacturing resources in a collaborative manner. Since the proposition of this concept in 2010, relevant research has mainly focused on theoretical frameworks of the CMfg system. However, actual applications of the machining service, which is a key part in the CMfg service platform, are hardly ever studied. In order to explore a feasible machining service mode, prime granularities of machining services are analyzed based on the current state of the manufacturing industry. Then a novel part manufacturing service combined with working procedure manufacturing service (PMS + WPMS) prime collaboration mode is proposed, followed by research of machining resource integration methods. To facilitate prospective implementations, information models of machining services are constructed using Web ontology language (OWL). The prime collaboration mode is expanded to a complete CMfg machining service platform. Furthermore, a working procedure priority-based algorithm (WPPBA) is designed for resource selection in CMfg. Finally, simulation experiments based on actual manufacturing data are conducted, in which the test results demonstrate the feasibility of the proposed service mode and the effectiveness of WPPBA compared with genetic algorithm (GA) and particle swarm optimization (PSO). This research provides essential guidance for CMfg applications.

This review presents various perspectives on converting user keywords into a formal query. Without understanding the dataset’s underlying structure, how can a user input a text-based query and then convert this text into semantic protocol and resource description framework query language (SPARQL) that deals with the resource description framework (RDF) knowledge base? The user may not know the structure and syntax of SPARQL, a formal query language and a sophisticated tool for the semantic web (SEW) and its vast and growing collection of interconnected open data repositories. As a result, this study examines various strategies for turning natural language into formal queries, their workings, and their results. In an Internet search engine from a single query, such as on Google, numerous matching documents are returned, with several related to the inquiry while others are not. Since a considerable percentage of the information retrieved is likely unrelated, sophisticated information retrieval systems based on SEW technologies, such as RDF and web ontology language (OWL), can help end users organize vast amounts of data to address this issue. This study reviews this research field and discusses two different approaches to show how users with no knowledge of the syntax of semantic web technologies deal with queries.

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using and publishing useful databases. To address these issues, the authors developed a Resource Description Framework-based database platform, called the RIKEN MetaDatabase (http://metadb.riken.jp), that allows biologists to develop, publish and integrate multiple databases easily. The platform manages the metadata of both research and individual data described using standardised vocabularies and ontologies, and has a simple browser-based graphical user interface to view data including tabular and graphical forms. The platform was released in April 2015, and 113 databases, including mammalian, plant, bioresource and image databases, with 26 ontologies have been published using this platform as of January 2017. This paper describes the technical knowledge obtained through the development and operation of the RIKEN MetaDatabase to accelerate life science data distribution.

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments.

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.