Forming a data ecosystem: international experience of infrastructure asset management

Based on the research conducted on the European policy of data ecosystem formation, the feasibility of regulatory alignment of the components of the digital ecosystem model in Ukraine to measure and control the parameters on economic sustainability is substantiated. The article presents the approaches of the EU, international standardization organizations and scientists to understanding the essence of the data ecosystem, identifies factors that impact the complexity of network assets administration on the example of infrastructure assets. Emphasis is placed on ensuring sustainability and assurance of existing network infrastructure assets throughout their lifecycle. The problems of digital transformation related to the increasing strain on all infrastructure systems, which are solved by the model of network infrastructure formation, are outlined. The need to accelerate the introduction of semantic technologies in IoT, in particular artificial intelligence, which expands the possibilities of data analysis and control and support of economic indicators of the state and the creation of added value in production and services, is justified.

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.

Perspectives on automated composition of workflows in the life sciences

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Ontology-Based Approach of E-government for Interoperability

There is a need for the exchange and sharing of knowledge between the department of government in the e-government system. Therefore, this paper ‘Ontology-Based Approach of E-government’ will discuss the scope of interoperability. With the e-government ontology, there will be proper semantics by using web ontology language (OWL) which helps to give clearer relation and semantics. To have an ontology, knowledge management is important and architecture/framework design is essential. The development of the e-government system is to serve citizens and organizations. However, e-government systems with heterogeneous database and distributed in nature have made difficult to integrate or interoperate. Therefore, developing a knowledge base (KB) is the major task that e-government focuses on. With KB definition and description, it will ensure clarity about e-government services. Knowledge management is important for e-government and the use of ontology is an effective way in semantic technologies. This ontology will enhance the processing of services and data between different departments in government. This kind of ontology will give a common understanding of knowledge and interoperability between the different departments of government. It will also offer effective and efficient value towards the e-government services by which citizens will be benefitted eventually.

Interoperability of electronic health records using Semantic Knowledge Graphs. A use case applied at the UTPL University Hospital

Patient medical information is diverse, extensiveand of high value in supporting informed medical decision-making.This information is highly complex, is distributed among differentsystems, presents high heterogeneity, is stored in different formats,and has different structuring levels. The management of thisinformation poses interoperability challenges in tasks related to dataintegration and reuse. In this paper, an alternative is presented toface these challenges using semantic technologies. We propose totransform this heterogeneous, distributed, and unstructuredinformation in a way that ensures high interoperability, reuse, anddirect processing by machine agents. The pilot of this proposal wasdeveloped at the UTPL Hospital.

An approach to creating freeform spreadsheet ontology

The use of semantic technologies including ontologies is a widespread practice in modern intelligent system engineering. Spreadsheets are one of the most accessible and common ways of representing and storing information which are characterized by a wide variety and heterogeneity of layouts, styles and content while remaining a valuable source of domain knowledge. The paper proposes to automate the process of ontology engineering based on the analysis and transformation of spreadsheets with an arbitrary layout. For this purpose a new approach is presented that provides the restoration of the semantics of tabular data, conceptualization, and formalization of tabular content in the form of ontology. The main stages of the proposed approach and a description of the software are presented. The developed software was used to solve the practical problem of ontology engineering for diagnosing and assessing the technical condition of petrochemical equipment. Spreadsheets extracted from reports on industrial safety inspection of petrochemical complexes were used as the initial data. Based on the results of approbation, it was concluded that it is advisable to use the proposed approach when prototyping subject ontologies.

Archaeological Digital Archiving in Turkey

This article provides a brief overview of archaeological digital archiving in Turkey. It introduces the legal framework and the stakeholders involved in conducting archaeological excavations and surveys. The current situation in archiving born-digital and digitised documentation produced during archaeological fieldwork is then introduced. Existing repositories serving as hubs for archaeological and heritage archiving are listed and briefly discussed. Analysis of online publishing practices for archaeological digital resources points to an eclectic landscape that only minimally complies with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We conclude that guidelines for best practice in metadata and semantic technologies, locally applicable standards (especially controlled vocabularies), technical know-how, and a larger acceptance of open data and scholarship remain much-needed assets for archaeological digital archiving in Turkey. We also conclude that the future promises progress towards more interoperable archaeological digital archives thanks to international training, network and knowledge transfer opportunities (e.g. SEADDA Project).

Linked Data Tools

Semantic Web technologies have immense potential to transform the Internet into a distributed reasoning machine that will not only execute extremely precise searches, but will also have the ability to analyze the data it finds to create new knowledge. This paper examines the state of Semantic Web (also known as Linked Data) tools and infrastructure to determine whether semantic technologies are sufficiently mature for non–expert use, and to identify some of the obstacles to global Linked Data implementation.