The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

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Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high‐quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level.

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A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts

Electronic laboratory notebooks are gaining in popularity in industry and academia. Various types of electronic laboratory notebooks exist, and a variety of tools can be used for these forms of notebooks. Some tools are commercial products dedicated to being electronic notebooks, while individual organizations can develop ones for their own needs with tools such a OneNote and Google Docs. Google Docs was used as an electronic notebook in organic chemistry, and student perceptions were evaluated using surveys. These tools are used to reduce students’ time preparing for lab and writing reports. The implementation of the notebooks and the findings of students’ perceptions on the implementation are discussed.

Implementation and Student Perceptions on Google Docs as an Electronic Laboratory Notebook in Organic Chemistry.

Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.

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KGen: a knowledge graph generator from biomedical scientific literature.

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a ‘key player’ in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption

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Electronic lab notebooks: can they replace paper?

Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to de...

https://www.tandfonline.com/doi/pdf/10.1080/17460441.2019.1586880

A new wave of innovation in semantic web tools for drug discovery

The use of artificial intelligence (AI) is growing in food supply chains. The ethical language associated with food supply and technology is contextualised and framed by the meaning given to it by stakeholders. Failure to differentiate between these nuanced meanings can create a barrier to technology adoption and reduce the benefit derived. The aim of this review paper is to consider the embedded ethical language used by stakeholders who collaborate in the adoption of AI in food supply chains. Ethical perspectives frame this literature review and provide structure to consider how to shape a common discourse to build trust in, and frame more considered utilisation of, AI in food supply chains to the benefit of users, and wider society. Whilst the nature of data within the food system is much broader than the personal data covered by the European Union General Data Protection Regulation (GDPR), the ethical issues for computational and AI systems are similar and can be considered in terms of particular aspects: transparency, traceability, explainability, interpretability, accessibility, accountability and responsibility. The outputs of this research assist in giving a more rounded understanding of the language used, exploring the ethical interaction of aspects of AI used in food supply chains and also the management activities and actions that can be adopted to improve the applicability of AI technology, increase engagement and derive greater performance benefits. This work has implications for those developing AI governance protocols for the food supply chain as well as supply chain practitioners. • AI applications are increasingly being adopted in food supply chains. • AI empowers decision-making, but its use must be framed by ethical considerations. • Benefits/risks of using AI are constantly evaluated in the AI development cycle. • Improving explainability, interpretability and accessibility enables transparency. • Responsibility and accountability relate to governance structures for use of AI. 

https://doi.org/10.1016/j.tifs.2022.04.025

Artificial intelligence and ethics within the food sector: Developing a common language for technology adoption across the supply chain

In today’s world of advancing technology, pervasive and ubiquitous systems have become more common, and have been adapted for many different areas of use. The Internet-of-Things (IoT) movement has increased in popularity, and there are now many affordable, customizable smart devices available for various purposes. These devices are commonly found in the home, but have yet to infiltrate the scientific laboratory in the same way. Some laboratories do use IoT devices to control and monitor equipment, but smart laboratories just do not exist in the same way as smart homes, until now. This article introduces the use cases for a smart integrated laboratory, and describes a prototype system, Talk2Lab, that was implemented in an experimental laser facility. Talk2Lab uses a combination of sensors, Raspberry Pis, camera feeds, and multiple interaction methods (voice, text, and visual dashboards) to facilitate laboratory communication. It was evaluated by the laboratory users for usability, and to identify potential areas of expansion. The work to date illustrates the type of systems that can be created using IoT devices. From the results of the user evaluation, we have conceptualized a future vision for Talk2Lab: a fully interconnected laboratory of the future.

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Talk2Lab: The Smart Lab of the Future

Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of “tagging and searching” documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.

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Samantha Kanza

Papers

Electronic lab notebooks: can they replace paper?

A new wave of innovation in semantic web tools for drug discovery

Artificial intelligence and ethics within the food sector: Developing a common language for technology adoption across the supply chain

Talk2Lab: The Smart Lab of the Future

Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies.