Semantic Ontology Research Articles

Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

The rapid increase of available data in different complex contexts needs automatic tasks to manage and process contents. Semantic Web technologies represent the silver bullet in the digital Internet ecosystem to allow human and machine cooperation in achieving these goals. Specific technologies as ontologies are standard conceptual representations of this view. It aims to transform data into an interoperability format providing a common vocabulary for a given domain and defining, with different levels of formality, the meaning of informative objects and their possible relationships. In this work, we focus our attention on Ontology Population in the multimedia realm. An automatic and multi-modality framework for images ontology population is proposed and implemented. It allows the enrichment of a multimedia ontology with new informative content. Our multi-modality approach combines textual and visual information through natural language processing techniques, and convolutional neural network used the features extraction task. It is based on a hierarchical methodology using images descriptors and semantic ontology levels. The results evaluation shows the effectiveness of our proposed approach.

A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis

Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.

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.

Novel approach for semantic similarity cross ontology

Measuring <span>semantic similarity between terms is a crucial step in information retrieval and integration since it necessitates semantic content matching. Even though several models have been proposed to measure semantic similarity, these models are not able to effectively quantify the weight of relevant items that affect the semantic similarity judgment process. In this study, we present a new method for measuring semantic similarity between cross-ontologies, that consists of hybridizing node-based approaches such as WuP and Reda with the weight of similarity computed using WordNet. The proposed approach has been experimented to show its efficiency with two ontologies, configuration management tool (CMT) and ConfOf, from the conference domaine in the web ontology language (OWL) ontologies benchmark OAEI 2015 and evaluated using two metrics: density and cohesion.</span>

Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research

This special issue is on Canonical Workflow Frameworks for Research (CWFR). A workflow refers to a sequence of activities, which may be more or less computer-based, used with regularity in the research process. CWFR aim to identify common patterns in such scientifically motivated workflows and to offer libraries of components based on FAIR Digital Objects as the integrative standard. Such CWFR components can be reusable independent of particular technologies, benefitting researchers in their daily work by making recurring activities more efficient, using automated workflow methods that would immediately create FAIR compliant data without adding burden.It is the goal of this special issue to provide readers with a deep exploration of CWFR and how it relates to research driven workflows, to existing workflow technologies, and to the use of FAIR Digital Objects. This issue covers articles examining core research activities including experimentation, data processing and analysis, data management, reproducibility, and publication. The articles comment on CWFR and its relation to these workflows, either conceptually in view of the current research ecosystem and infrastructure or more practically, focusing on a specific implementation, design, tool, or context relating to CWFR.The contributing authors are experts in their area. They include researchers, data professionals, data managers and curators, IT specialists and others who are using, developing, or experimenting with the effective use of canonical workflows and workflow patterns for data intensive research. As guest editors, it has been a privilege to work with such accomplished authors.It is our hope that this issue will stimulate further exploration of this subject. The papers in this issue address timely questions such as, what are the recurring patterns of work within or across institutions and research communities? What are the core elements of workflow technologies and how can they relate to the core ideas of CWFR? How well do existing integration standards and best practices address this? What is the potential of FDOs to support the goals of CWFR? How can research be protected against the ever-changing technological fashions?Finally, we are grateful to the journal for the opportunity to publish this special issue and to Dr. Fenghong Liu, Managing Editor-in-Chief, for her skilled guidance and support.Yann Le Franc, PhD is the CEO and Scientific Director of e-Science Data Factory S.A.S.U., a French R&D company aiming at proposing innovative solutions for FAIR data management to accelerate growth and progress. Yann Le Franc has a PhD in Neurosciences and Pharmacology (2004). After a postdoctoral experience in the US, he worked on data management projects in Neurosciences at the University of Antwerp (Belgium) and in the context of the International Neuroinformatics Coordinating Facility (INCF) where he developed a strong expertise in ontology design and semantic web technologies. He then contributed to several Horizon 2020 Research Infrastructure projects (EUDAT, EOSC-Hub, …) as an expert on Semantic Web and ontology design. He is co-chairing the Research Data Alliance Vocabulary and Semantic Service Interest Group and the FDO Semantic Group. He is also a member of the EOSC Semantic Interoperability Task Force. He is actively involved in the FAIRification and standardization of semantic artefacts in the context of FAIRsFAIR and OntoCommons projects. In parallel, he is the technical manager of the EOSC-Pillar project for the French National Computing Center for Higher Education (CINES).

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers’ efforts to move toward the digital transformation of job shops.

Retracted: QGMS: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence

AbstractThe inclusion of collective intelligence through a semantic focused affective computing can incorporate intelligence to web search and ensure its compliance with the Web 3.0. In this article, a query growth model with inclusive and exclusive ontology semantics has been proposed for diversification of query recommendation in semantic search. The ontology semantics include query augmented ontology generation, agent‐driven attractor‐distractor generation to yield a merged ontology, and endowment of merged ontology by using hybridization of a series of knowledge bases. The strategy further includes the formulation of a semantic network and entity leveraging based on description logics (DLs) to improve the quality of query recommendation. A novel hierarchical entropy cognitive similarity covariance model has been proposed for yielding the most appropriate recommendable query words. The strategy also encompasses the user‐click information for capturing the current user intents to improve the quality queries recommended in semantic search, and thereby incorporate personalization. Experimentations are conducted for the CHiC dataset and the Spring 2006 Query Log dataset and an average accuracy of 96.27% and 92.01%, respectively, with a very low false discovery rate of 0.06 and 0.1 for the respective datasets.

Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

Observational/hydrographic data of the South Atlantic Ocean published as LOD

This article describes the publication of occurrences of Southern Elephant Seals Mirounga leonina (Linnaeus, 1758) as Linked Open Data in two environments (marine and coastal). The data constitutes hydrographic measurements of instrumented animals and observation data collected during censuses between 1990 and 2017. The data scheme is based on the previously developed ontology BiGe-Onto and the new version of the Semantic Sensor Network ontology (SSN). We introduce the network of ontologies used to organize the data and the transformation process to publish the dataset. In the use case, we develop an application to access and analyze the dataset. The linked open dataset and the related visualization tool turned data into a resource that can be located by the international community and thus increase the commitment to its sustainability. The data, coming from Península Valdés (UNESCO World Heritage), is available for interdisciplinary studies of management and conservation of marine and coastal protected areas which demand reliable and updated 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-Driven Learning Assessment Using the Script Concordance Test

Assessing the level of domain-specific reasoning acquired by students is one of the major challenges in education particularly in medical education. Considering the importance of clinical reasoning in preclinical and clinical practice, it is necessary to evaluate students’ learning achievements accordingly. The traditional way of assessing clinical reasoning includes long-case exams, oral exams, and objective structured clinical examinations. However, the traditional assessment techniques are not enough to answer emerging requirements in the new reality due to limited scalability and difficulty for adoption in online education. In recent decades, the script concordance test (SCT) has emerged as a promising tool for assessment, particularly in medical education. The question is whether the usability of SCT could be raised to a level high enough to match the current education requirements by exploiting opportunities that new technologies provide, particularly semantic knowledge graphs (SCGs) and ontologies. In this paper, an ontology-driven learning assessment is proposed using a novel automated SCT generation platform. SCTonto ontology is adopted for knowledge representation in SCT question generation with the focus on using electronic health records data for medical education. Direct and indirect strategies for generating Likert-type scores of SCT are described in detail as well. The proposed automatic question generation was evaluated against the traditional manually created SCT, and the results showed that the time required for tests creation significantly reduced, which confirms significant scalability improvements with respect to traditional approaches.

Modelling of Child Seat Dependencies for Digital Information Services using Semantic Technologies

With the increasing importance of knowledge management and the growing amount of data in industry, a field of application for semantic technologies has emerged with the representation of knowledge in the area of technical communication. Semantic networks and ontologies offer a high degree of intelligence and can make complex relationships interpretable for humans and machines alike. This aspect can lead to an improvement in the classification, connection, and delivery possibilities of content. Technical documentation is one of many areas where semantic technologies and knowledge graphs can add value to industry. This paper should give an outlook on how semantic technologies can be used in different use cases by companies in the domain of technical communication in the future. Based on a use case of a German company of the child seat industry, the use of semantic technologies and their various use cases in technical communication is shown. The company has started to use semantic technologies to simplify processes and to provide their digital information products in a user- and context-oriented way. This paper focuses on the possibilities that the use of semantic technologies, especially ontologies, provides in a company. It also highlights the difficulties that can arise in the transition and transformation of data.

The Development of an Ontology for Information Retrieval about Ethnic Groups in Chiang Mai Province

This study aims to develop the semantic ontology of information knowledge about ethnic groups by analyzing information from the collection of documentary sources from libraries, research, and the museum for learning about people on the highlands located in Chiang Mai Province. The study is based on the classification theory of ethnic groups in Chiang Mai Province with the intention of establishing the relationship between knowledge structure regarding ethnic groups. The study procedures consist of three stages: 1)Establishing ontology requirements from online data to analyze the data of the keyword from the research database of Chiang Mai University Library's Online Information Resource Database (OPAC) and Ratchamangkhalaphisek National Library, Chiang Mai to group the words by studying information resources in Thai language, such as books, textbooks, research papers, theses, research articles, academic articles, and reference books related to ethnic groups. Stage 2) Designing classes, defining main classes, subclasses, hierarchies, and properties in order to establish the relationship of data in each class using the Protégé program. Stage 3) Ontology evaluation, which is divided into two parts: an expert's evaluation of the suitability of the ontology structure using the Inter-Class Relational Accuracy Assessment Scale and an examination of ethnic grouping data. The findings reveal that specifying, definition, scope, and objectives of development are appropriate (average score = 0.97) in three areas: grouping and ordering of classes within the ontology (score value = 0.98), defining affinity names and class properties (score value = 0.96), and suitable overall ontology content (score value = 0.97).

Research on Literary Translation Based on the Improved Optimization Model

Machine translation is widely used in people’s daily lives and production, occupying an important position. In order to improve the accuracy of the literary intelligent translation, research on literary intelligent translation is based on the improved optimization model. Based on semantic features, the semantic ontology optimization model including an encoder and a decoder is created by machine translation. In order to improve the accuracy of the intelligent translation literature of the semantic ontology optimization model, the conversion layer, including the forward neural network layer, residual connection layer, and normalization layer, is added between the encoder and decoder of the semantic ontology optimization model. An improved optimization model is established, and syntax conversion is realized by using the conversion layer, which completes the intelligent translation of literature. It is found that the BLEU value of using this method to translate literary sentences can reach 17.23 when the number of training steps is set as 8000, and the training time is low. The translation result has a low correlation misalignment rate, which can meet the user’s literary translation needs.

Semantic product personalization based on the CogniPy environment

Purpose: The aim of the publication was to visualize the process of creating knowledge on the example of the CP Factory production line. For this purpose, the data contained in the relational databases and data of the operating production line were used. This data was converted to the form required by the CogniPy environment to create a semantic ontology for product personalization. Design/methodology/approach: With the available software based on ontologies and knowledge charts, the possibility of common human-computer reasoning has been opened, especially in production management. Findings: During the activities carried out, the on-conceptualization of the knowledge contained in the production management system was brought to an ontological form. At the moment, the information contained in the database is not very different from the data existing in the relational database. However, further modeling of ontology can be directed towards the creation of rules, logic and axioms of production processes. Practical implications: The operations and transformations performed presented the operation of CogniPy in the process of creating ontology and materializing the graph and queries. The created ontology takes the form of a universal set of knowledge that makes it open to wide integration with other systems. Originality/value: The publication shows the possibilities of using the CogniPy environment in the construction of ontology and semantic product personalization. Keywords: ontology, semantics, CogniPy, CP Factory.

A core ontology for modeling life cycle sustainability assessment on the Semantic Web

AbstractThe use of Semantic Web and linked data increases the possibility of data accessibility, interpretability, and interoperability. It supports cross‐domain data and knowledge sharing and avoids the creation of research data silos. Widely adopted in several research domains, the use of the Semantic Web has been relatively limited with respect to sustainability assessments. A primary barrier is that the framework of the principles and technologies required to link and query data from the Semantic Web is often beyond the scope of industrial ecologists. Linking of a dataset to Semantic Web requires the development of a semantically linked core ontology in addition to the use of existing ontologies. Ontologies provide logical meaning to the data and the possibility to develop machine‐readable data format. To enable and support the uptake of semantic ontologies, we present a core ontology developed specifically to capture the data relevant for life cycle sustainability assessment. We further demonstrate the utility of the ontology by using it to integrate data relevant to sustainability assessments, such as EXIOBASE and the Yale Stocks and Flow Database to the Semantic Web. These datasets can be accessed by the machine‐readable endpoint using SPARQL, a semantic query language. The present work provides the foundation necessary to enhance the use of Semantic Web with respect to sustainability assessments. Finally, we provide our perspective on the challenges toward the adoption of Semantic Web technologies and technical solutions that can address these challenges.

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.

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem.

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

Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome

The increasing number of scientific research documents published keeps growing at an unprecedented rate, making it increasingly difficult to access practical information within a target domain. This situation is motivating a growing interest in applying text mining techniques for the automatic processing of text resources to structure the information that helps researchers to find information of interest and infer knowledge of practical use. However, the automatic processing of research documents requires the previous existence of large, manually annotated text corpora to develop robust and accurate text mining processing methods and machine learning models. In this context, semi-automatic extraction techniques based on structured data and state-of-the-art biomedical tools appear to have significant potential to enhance curator productivity and reduce the costs of document curation. In this line, this work proposes a semi-automatic machine learning workflow and a NER + Ontology boosting technique for the automatic classification of biomedical literature. The practical relevance of the proposed approach has been proven in the curation of 4,115 gluten-related documents extracted from PubMed and contrasted against the word embedding alternative. Comparing the results of the experiments, the proposed NER + Ontology technique is an effective alternative to other state-of-the-art document representation techniques to process the existing biomedical literature.