Semantic Technologies Research Articles

Application of Deep Learning in Land Use Classification for Soil Erosion Using Remote Sensing

China is suffering from serious soil and water loss due to improper land use, leading to flood and drought disasters, and industrial and agricultural reduction. The commonly used monitoring methods of soil and water loss rely on the visual interpretation of remote sensing images, which is time-consuming and laborious. In this study, deep learning semantic segmentation technology was applied to monitoring land use for soil and water conservation. Manual visual interpretation mark samples and land use products collected in recent years were combined to construct training samples for deep learning. Deep convolution neural network model based on ResNet152 and DeeplabV3+ was selected for performing land use classification. Then the soil and water loss were quantified using the Chinese Soil Loss Equation (CSLE) based on the hydrological, geographic, vegetation cover and land use information. The experimental results show that the deep learning model can quickly extract robust edge features from remote sensing images and has high pixel accuracy (83.4%). Although the model accuracy was affected by the land cover types and image quality in different study area, it can still achieve an accuracy higher than 70% in other counties through our experiment. It can further improve the information service level of soil and water conservation product, and provide a useful guideline for automatic land use interpretation and identification of soil and water conservation status based on high resolution remote sensing images.

Sentiment Analysis of Financial news

Sentiment Analysis deals with the computational treatment of opinions of expressed in written handbooks. The addition of the formerly mature semantic technologies to the field has proven to increase the results delicacy. In this a semantically- enhanced methodology for the reflection of sentiment opposition in fiscal news is presented. The term" Sentiment Analysis" was first defined in 2003 by Nasukawa and Yi as “ determining the subjectivity opposition ( positive or negative) and opposition strength ( explosively positive, mildly positive, weakly positiveetc.) of a given review textbook; in other words- determining the opinion of the pen.” Turney’s pioneering work on Sentiment Analysis applied an unsupervised approach to classify review data into positive class and negative class. The sum aggregate of information entered by the investors is reflected through the stock price of the enterprises. Through this process, information is converted from a textual form to a numerical form. This process of conversion is veritably useful, because it allows information to be fluently epitomized and enables us to compare the sentiments of news with the request returns. There may be variations about the exact meaning of a piece of news, but there can not be any variation about request returns. The fiscal news that makes a positive impact on the stock request returns is good and the bone that makes a negative impact on stock request returns is bad. In comparison to the work done in sentiment bracket applied to the review sphere or product reviews, veritably little work has been done in the field of operation of these ways in the fiscal sphere using unsupervised approach. This paper tries to address this exploration gap. The overall purpose of the study is to propose a semantic exposure grounded unsupervised approach for chancing sentiments strength of fiscal textbook.

MANAGEMENT OF INTERACTION IN THE EDUCATIONAL COMMUNITY NETWORK

The aim of the study is to develop the concept of a knowledge management system that integrates educational content into a single environment of university knowledge using an extensible mathematical model of the subject area of university knowledge in the form of an ontology. The study is being carried out as part of the development of a pilot project on the development of business education at the Institute of Business of the Belarusian State University, and a project in the State Research Program "Methods for updating the content of education based on semantic technologies" (state registration number 20212245). The research methodology is based on the development approaches to modeling knowledge areas of the network educational community of the Institute of Business of Belarusian State University and the ontological model of the subject area of business education. As a result, the architecture of the educational content management system was developed within the framework of the knowledge management system of the university. This proved the effectiveness of online educational communities in business education, a significant increase in the quality of content through the use of a learning content management system.

Agricultural Information Management Data Model Construction Based on Cloud Computing and Semantic Technology

In order to improve the data retrieval and mining ability of agricultural information management system, an agricultural information management data model based on cloud computing and semantic technology was proposed. Fuzzy C-means algorithm is used for adaptive fusion and clustering of semantic association features of distributed data in large-scale information management systems; feature compressors are used to reduce the storage space dimension of large-scale information management system to improve the target data mining capability and adaptive scheduling capability of information management system. The registration rate improves than the traditional method by 12.46% and computational cost by 23.76%. This method has higher accuracy in storage data mining and retrieval for large information management systems and superior performance than the traditional method. The introduction of the concept and technical methods of related data is the best practice to realize the fine disclosure, standardized description, semantic organization, and in-depth integration of massive agricultural science and technology information resources, which will play an important role in improving the visibility, visibility, and accessibility of agricultural information resources. It is innovative in the construction of the multidimensional semantic-related model and field knowledge service system driven by related data.

The Interoperability of Learning Object Design, Search and Adaptation Processes in the Repositories

Learning environments ensure successful implementation of the learning process but not always the effective design of the e-learning objects (ELOs) and moreover, search and adaptation. A technological solution for the design of the learning objects, repositories, and the semantic web is needed. There are many open educational resources, but not many platforms assure the possibility to adapt learning objects. The existing developed multifunctional platforms do not ensure the effective ELOs adaptation as well as the process of design and adaptation in the multifunctional environment. They do not have an automatic search of ELOs in the semantic web, which is directly targeted to the specific objects in repositories of open educational resources and do not allow for adaptation of the already developed ELO by automatically assigning reusable objects. The structure of the papers consists of the literature review and overview of existing practices, research methodology, research results description and conclusions provided by authors. The objective of the research is to suggest, to teachers, a model for effective e-learning objects design, automatic search and adaptation processes in the multifunctional environment by developing a platform based on semantic technologies for e-learning objects design and adaptation.

Adaptive tools for formation of the knowledge base of information and analytical system of training scientific staff

The author emphasizes the urgency of preserving the integrity of the scientific and educational process for the training of highly qualified personnel in higher education universities and research institutions. It is noted that the consequences of force majeure, including quarantine from the COVID-19 pandemic and the current martial law in Ukraine, cause some adjustments in the organization of training of scientific staff. In this regard, it is likely that the problem of creating a fundamentally new information-analytical system (IAS), which in this vector will include a specific knowledge base fol-lowing the requests of research users – subjects of scientific training. Thus, the article aims to present the results of the doctoral research, in particular on the intermediate content analysis of scientific, information and reference, educational and methodologi-cal and other literature sources and practice-oriented resources on the development of information technology and systems and their adaptive transfer, which will contribute to the accumulation of analytical information and its digitization. Emphasis is placed on the rationality of creation and use of intelligent IAS based on the formed specific knowledge bases using semantic technologies and ontological modelling. This is due to the existence of a large amount of diverse analytical information, based on which the issues of any research, which in the context of training is a mandatory component of sci-entific and educational activities of applicants for scientific education (undergraduates, graduate and doctoral researchers, etc.). It is claimed that a modern researcher will be able to master modern tools and expert technologies of information and analytical ac-tivities. A brief description of the adaptive tools with which the formation of a specific knowledge base in the IAS of training of scientific staff is presented. It is noted that se-mantic and ontological approaches are the most effective in this regard for building in-formation systems, in particular those related to the development of information-analytical competence of applicants for scientific education.

The Financial Data Services Domain: From Taxonomies to Ontologies

There are many different types of instruments and hundreds of different markets for investment, leading to an extremely large and hard-to-define universe of financial data. The related commercial offer is extremely heterogeneous and complex. In this scenario, it is difficult to source the most appropriate financial services providers. In the past, eProcurement was mainly focused on the use of ERP management tools to record and examine previous buying decisions and expenditure data. In recent years, machine learning and artificial intelligence have been applied to procurement workflows, introducing computation of external or third party unstructured data to achieve a higher level of market knowledge and decision automation. In order to exploit the possibilities provided by these new technologies to the full extent possible, theoretical models for understanding large amounts of unstructured data are essential. In this research-in-progress paper we propose a taxonomy of financial data services and depict the related prototype ontological model, providing a possible conceptualisation and specification of the domain of interest potentially useful for the development of applications based on semantic technologies.

A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics

The building industry consumes the most energy globally, making it a priority in energy efficiency initiatives. Heating, ventilation, and air conditioning (HVAC) systems create the heart of buildings. Stable air handling unit (AHU) functioning is vital to ensuring high efficiency and extending the life of HVAC systems. This research proposes a Digital Twin predictive maintenance framework of AHU to overcome the limitations of facility maintenance management (FMM) systems now in use in buildings. Digital Twin technology, which is still at an initial stage in the facility management industry, use Building Information Modeling (BIM), Internet of things (IoT) and semantic technologies to create a better maintenance strategy for building facilities. Three modules are implemented to perform a predictive maintenance framework: operating fault detection in AHU based on the APAR (Air Handling Unit Performance Assessment Rules) method, condition prediction using machine learning techniques, and maintenance planning. Furthermore, the proposed framework was tested in a real-world case study with data between August 2019 and October 2021 for an educational building in Norway to validate that the method was feasible. Inspection information and previous maintenance records are also obtained through the FM system. The results demonstrate that the continually updated data combined with APAR and machine learning algorithms can detect faults and predict the future state of Air Handling Unit (AHU) components, which may assist in maintenance scheduling. Removing the detected operating faults resulted in annual energy savings of several thousand dollars due to eliminating the identified operating faults.

Building a Super Smart Nation: Scenario Analysis and Framework of Essential Stakeholders, Characteristics, Pillars, and Challenges

Globally, countries are increasingly facing challenges regarding their national future post the COVID-19 pandemic with respect to decreasing and aging populations; dwindling workforces; trade wars due to restricted movement of goods, people, and services; and overcoming economic development and societal problems. Accordingly, we identify the challenges and corresponding solutions that act as pillars for a framework to build a super smart nation. We analyze 73 peer-reviewed research papers from Scopus index databases and use the Delphi methodology to identify the challenges, which include people and society, robots, technology, research and innovation, digital infrastructure, data, politics, governance, and sustainability. Further, we discuss the relevant solutions, including top leadership motivation and commitment; proactive steps from the government, development of policies; legal frameworks and laws; creation of awareness programs; use of advanced technologies (such as robotics and semantic technologies); and development of interoperable infrastructure; innovation ecosystem; sustainable energy sources; and global standards for education system through transformation of the education system. These novel insights have valuable practical and theoretical implications for guiding policymakers, industry leaders, and researchers in building a super smart nation.

Semantic Integration of Raster Data for Earth Observation on Territorial Units

Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to territorial units (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a semantic extraction, transformation and load (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach.

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Knowledge Graph Based Hard Drive Failure Prediction.

The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.

Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images.

Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.

ABiMed: Towards an Innovative Clinical Decision Support System for Medication Reviews and Polypharmacy Management.

Polypharmacy in elderly is a public health problem with both clinical (increase of adverse drug events) and economic issues. One solution is medication review, a structured assessment of patients' drug orders by the pharmacist for optimizing the therapy. However, this task is tedious, cognitively complex and error-prone, and only a few clinical decision support systems have been proposed for supporting it. Existing systems are either rule-based systems implementing guidelines, or documentary systems presenting drug knowledge. In this paper, we present the ABiMed research project, and, through literature reviews and brainstorming, we identified five candidate innovations for a decision support system for medication review: patient data transfer from GP to pharmacists, use of semantic technologies, association of rule-based and documentary approaches, use of machine learning, and a two-way discussion between pharmacist and GP after the medication review.

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.

Semantic Web Technologies: Issues and Possible Ways of Development

Semantic digital technologies are as useful as they are difficult to implement. During its existence, the concept of the Semantic Web has still found very limited application and extremely slow development. The reasons for this are serious differences in the structure of hypertext and the Semantic Web as a superstructure above it, and the rules that circumscribed the development of the internet. Semantic search engines still do not implement semantic search in its pure form: various data organization schemes and data visualization formats make it a little easier to analyze facts, add them to databases, but do not make knowledge bases out of the latter, do not allow working with facts as with knowledge. In addition, the user interfaces of such systems tend to be either very complex or counter-intuitive. Nevertheless, there is every reason to believe that with the use of modern technologies, the ideas of semantization of the web and its content can already be improved and gradually implemented: for this it is necessary to get rid of some false expectations of semantic technologies, to direct efforts towards the development of web native formats of semantic markup and try to take advantage of the increasingly common voice input of household devices.

Methodology for the design of didactic tools for the future transport engineers’ competencies formation on the ontology basis

The change in the essence of engineers’ functional activity activates the problem of creating new didactic tools adequate to the conditions of digital transformation of production and university education. The authors consider the ontological-semantic approach based on the use of ontological models and semantic technologies, as well as the principles of interoperability and interdisciplinarity corresponding to this approach as the methodological basis for the development of such tools. The feasibility of developing didactic tools on the ontology basis was determined using the questionnaire survey method, which was conducted among future railway transport engineers from 2016 to 2021. The results of the data analysis indicated a tendency for respondents to use compression tools for better assimilation of new learning material. Among such means students indicated not only tables, drawings and diagrams, but also a fundamentally new form of educational knowledge presentation - ontology. The conclusions made on the results of the questionnaire served as a basis for the development of didactic tools for the formation of communicative competence of future railway transport engineers. The novelty of this educational solution consisted in the complex use of textual, hypertext and ontological forms of branch knowledge representation, including the foreign language studied by students.

A Semantic Approach to Ranking Techniques: Improving Web Page Searches for Educational Purposes

The Web offers an unprecedented number of resources and has become the most popular source of information for students shaping their understanding of a new topic, and for instructors selecting relevant material for learning and teaching activities. Even though search engines are the most widely used tools for searching for educational content, the realities of the learning and teaching processes make the retrieval and evaluation of educational resources more complex than they are for other goods or services. The lack of recourse to educational metadata in web pages, as well as the size of the Web itself, call for specific techniques to be adopted for a more effective ranking of educational content. In this study, we propose an innovative approach based on semantic technologies. The <i>SemanticSearch</i> approach described in this paper leverages knowledge graph representation of teaching contexts and proposes a new ranking method for rating educational web content. In the literature we find an Educational Ranking Principle that ranks web pages for a specific teaching context. In this study, we integrate the Educational Ranking Principle with semantic data to extend the experimentation and analyse performance further. We undertake an evaluation involving university teachers, considering more than 70 queries to measure the <i>SemanticSearch</i> performance against the Educational Ranking Principle in addition to two state-of-the-art methodologies: Tf-Idf and BM25F. Paired t-tests of four accuracy measures provide statistical evidence for improvements made by using <i>SemanticSearch</i> method when compared to the three baselines.

Knowledge graph for manufacturing cost estimation of gear shafts - a case study on the availability of product and manufacturing information in practice

Growing cost pressure forces companies to actively manage their product costs to secure profitability. Here, manufacturing cost estimation within product development estimates manufacturing and material costs. As most products are developed in generations, needed product and manufacturing information can origin from reference system elements (RSE), for example similar components of prior product generations. Problematically, this product and manufacturing information as well as the knowledge of its interrelation is often stored in an unstructured way, document based or at least not machine-readable. This makes manufacturing cost estimation an effortful, time consuming and mainly manual activity with low traceability, where a wide manufacturing knowledge is required. Trends in production, like new manufacturing processes and production systems further increase the need for manufacturing information and knowledge. Knowledge graphs as semantic technologies can improve the findability and reusability of reference system elements and enable automatic information processing.Within this research, cost estimation of research and development of a large automotive supplier was used as research environment. Guided by the model of PGE an ontology for the manufacturing cost estimation domain was developed. Then, a knowledge graph was instantiated based on product and manufacturing information from gear shafts of electric axles. A case study was carried out to evaluate process-specific cycle time calculation as exemplary use case of the knowledge graph. Process-specific cycle times are generally effortful estimated based on detailed manufacturing information and then used together with machine hourly rates to estimate manufacturing costs. Here, the structured and machine-readable manufacturing information of identified reference system elements is extracted from the knowledge graph to reduce the effort, increase the traceability and enable future automation. The case study shows exemplary, how a knowledge graph can support manufacturing cost estimation of gear shafts where product and manufacturing information is automatically identified using reference system elements.

A new approach to impact case study analytics

Abstract The 2014 Research Excellence Framework (REF) assessed the quality of university research in the UK. 20% of the assessment was allocated according to peer review of the impact of research, reflecting the growing importance of impact in UK government policy. Beyond academia, impact is defined as a change or benefit to the economy, society, culture, public policy or services, health, the environment, or quality of life. Each institution submitted a set of four-page impact case studies. These are predominantly free-form descriptions and evidences of the impact of study. Numerous analyses of these case studies have been conducted, but they have utilised either qualitative methods or primary forms of text searching. These approaches have limitations, including the time required to manually analyse the data and the frequently inferior quality of the answers provided by applying computational analysis to unstructured, context-less free text data. This paper describes a new system to address these problems. At its core is a structured, queryable representation of the case study data. We describe the ontology design used to structure the information and how semantic web related technologies are used to store and query the data. Experiments show that this gives two significant advantages over existing techniques: improved accuracy in question answering and the capability to answer a broader range of questions, by integrating data from external sources. Then we investigate whether machine learning can predict each case study’s grade using this structured representation. The results provide accurate predictions for computer science impact case studies.