Use Of Knowledge Models Research Articles

Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.

An ontology-based approach to knowledge representation for Computer-Aided Control System Design

Different approaches have been used in order to represent and build control engineering concepts for the computer. Software applications for these fields are becoming more and more demanding each day, and new representation schemas are continuously being developed. This paper describes a study of the use of knowledge models represented in ontologies for building Computer Aided Control Systems Design (CACSD) tools. The use of this approach allows the construction of formal conceptual structures that can be stated independently of any software application and be used in many different ones. In order to show the advantages of this approach, an ontology and an application have been built for the domain of design of lead/lag controllers with the root locus method, presenting the results and benefits found.

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

BackgroundThe use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline.MethodsAs a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources.ResultsA semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%.ConclusionThe results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain.

A Study of the Use of Ontologies for Building Computer-Aided Control Engineering Self-Learning Educational Software

This paper describes research on the use of knowledge models (ontologies) for building computer-aided educational software in the field of control engineering. Ontologies are able to represent in the computer a very rich conceptual model of a given domain. This model can be used later for a number of purposes in different software applications. In this study, domain ontology about the field of lead–lag compensator design has been built and used for automatic exercise generation, graphical user interface population and interaction with the user at any level of detail, including explanations about why things occur. An application called Onto-CELE (ontology-based control engineering learning environment) uses the ontology for implementing a learning environment that can be used for self and lifelong learning purposes. The experience has shown that the use of knowledge models as the basis for educational software applications is capable of showing students the whole complexity of the analysis and design processes at any level of detail. A practical experience with postgraduate students has shown the mentioned benefits and possibilities of the approach.

How to integrate technology‐enhanced learning with business process management

Purpose – The purpose of this paper is to propose an innovative approach for providing an answer to the emerging trends on how to integrate e-learning efficiently in the business value chain in medium and large enterprises. Design/methodology/approach – The proposed approach defines methodologies and technologies for integrating technology-enhanced learning with knowledge and human resources management based on a synergistic use of knowledge models, methods, technologies and approaches covering different steps of the knowledge life-cycle. Findings – The proposed approach makes explicit and supports, from the methodological, technological and organizational points of view, mutual dependencies between the enterprise’s organizational learning and the business processes, considering also their integration in order to allow the optimization of employees’ learning plans with respect to business processes and taking into account competencies, skills, performances and knowledge available inside the organization. Practical implications – This mutual dependency, bridging individual and organizational learning, enables an improvement loop to become a key aspect for successful business process improvement (BPI) and business process reengineering (BPR), enabling closure of, at the same time, the learning and knowledge loops at individual, group and organization levels. Originality/value – The proposed improvements are relevant with respect to the state of the art and respond to a real need felt by enterprises and further commercial solutions and research projects on the theme.

The role of knowledge modelling techniques in software development: a general approach based on a knowledge management tool

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modelling. Then, the current state of knowledge modelling techniques is described where increased reliability is available through the modern knowledge-acquisition techniques and supporting tools. The knowledge structure manager (KSM) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object-oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are made regarding the feasibility of using the knowledge modelling tools and methods for general application design.

Semantic video indexing

Providing concept level access to video data requires, video management systems tailored to the domain of the data. Effective indexing and retrieval for high-level access mandates the use of domain knowledge. This paper proposes an approach based on the use of knowledge models to building domain specific video information systems. The key issues in such systems are identified and discussed.

Process optimization in a supervision support system

We present details of the optimization task in a real-time (Thompson and Mertz 1993) knowledge-based (Moore et al. 1991, Larsson 1992) supervision support system in the coal washing domain. The Ash Control Model (AshMod) assists operators in maximizing clean coal yield while keeping ash (impurity) content within acceptable limits. AshMod assists the operator in plant situation assessment, fault diagnosis, and performance optimization. Situation assessment and fault diagnosis are mentioned briefly, since they have been described elsewhere in detail (Villanueva and Lamba 1997, 1998). We focus on the optimization task, which employs a hybrid artificial intelligence and operations research approach. The process is modelled through a set of extended states associated with the entire process and with individual components (circuits) within the plant. The scheduling optimizer continuously monitors the process, assesses the process state, and dynamically plans and performs integer and real optimization of a sequence of actions. The supervision support system captures domain knowledge through multiview models (Terspra et al. 1993) such as the goal tree success tree, plant schematic and fault cause network. AshMod performs deep reasoning through the use of knowledge models (Lind 1994) that capture purpose, function, structure, behaviour and heuristics. These knowledge models are mentioned briefly, since they have also been described elsewhere in detail (Villanueva and Lamba 1997, 1998). The supervision support system (Lamba 1995) is currently undergoing online validation at the B&C Coal Washing Plants operated by the Broken Hill Proprietary Limited (BHP 1998) at Port Kembla, Australia. The system is expected to be fully operational by the end of 1998.

Trends in knowledge modelling: report on the 7th KEML Workshop

The KEML (Knowledge Engineering Methods and Languages) workshop which took place on 22–24 January at the Open University in Milton Keynes (UK) was the seventh in a series of workshops on methods and languages for knowledge engineering. Although t he KEML acronym suggests a broad knowledge engineering connotation, in practice the main emphasis of these workshops is on the construction, formalisation, verification and use of knowledge models. The term “knowledge model” originate s from the work of Allen Newell (1982), who proposed a level of description — the knowledge level — which abstracts from implementation-related considerations to focus on the actions, goals and knowledge embodied by a problem solving agent.

Operator guidance system for industrial plant supervision

In this paper we present monitoring, diagnosis and optimization in the Ash Control Model (AshMod), a knowledge-based operator guidance system (OGS) in the coal washing domain. AshMod assists the operator in monitoring the plant, performing fault diagnosis, and in plant optimization. It assists the operators in maximizing clean coal yield while keeping ash (impurity) content within acceptable limits. AshMod performs deep reasoning through the use of knowledge models that capture purpose, function, structure, behaviour and heuristics. Knowledge validation and maintenance is facilitated through the use of graphical object-oriented knowledge models. AshMod has been developed for the B&C coal washing plants operated by Broken Hill Proprietary Limited (BHP) at Port Kembla, Australia (The Broken Hill Proprietary Company Limited, Home Page, http://www bhp.com.au/; BHP, 1995) Owing to our focus on generality and reuse during the development of this OGS, we expect that much of AshMod can be reused in future OGS developments in BHP operated coal washeries, sinter plants, blast furnaces, coke ovens etc. AshMod is currently undergoing of line testing at the coal washing plants.