Benchmarking in cluster analysis: A white paper

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

Only recently have engineers begun making use of Artificial Intelligence (AI) tools in the area of conceptual design. To continue filling this void in the design process, a prototype knowledge-based system, called STRUTEX has been developed to initially configure a structure to support point loads in two dimensions. This prototype was developed for testing the application of AI tools to conceptual design as opposed to being a testbed for new methods for improving structural analysis and optimization. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user. How the system is constructed to interact with the user is described. Of special interest is the information flow between the knowledge base and the data base under control of the algorithmic main program. Examples of computed and refined structures are presented during the explanation of the system

25 years development of knowledge graph theory: the results and the challenge

The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system. By the end of the 80's expert systems in medical and social science were developed successfully using knowledge graph theory. In the following stage, the goal of the project was broadened to represent natural language by knowledge graphs. Since then, this theory can be considered as one of the methods to deal with natural language processing. At the present time knowledge graph representation has been proven to be a method that is language independent. The theory can be applied to represent almost any characteristic feature in various languages.\ud The objective of the paper is to summarize the results of 25 years of development of knowledge graph theory and to point out some challenges to be dealt with in the next stage of the development of the theory. The paper will give some highlight on the difference between this theory and other theories like that of conceptual graphs which has been developed and presented by Sowa in 1984 and other theories like that of formal concept analysis by Wille or semantic networks

Supporting diagnostics and decision making in healthcare by modular methods of computational linguistics

We propose a new framework for development of modular computational methods to support processes of healthcare and health education in diverse settings. Motivated by an evaluation by The National Institute for Health and Welfare in Finland the proposed framework aims to address challenges of analyzing knowledge concerning healthcare services and patient records with computational linguistics. The framework aims to promote implementing personalized care in diagnostics, decision making, patient engagement and self-care. We describe some analysis methods of computational linguistics, natural language processing, statistics, algorithms and data mining. We have built a prototype program enabling representing and modifying health-related knowledge structures for purposes of prevention, diagnosis and care. For 25 most common diagnosis names we have identified dependencies of core symptom concepts in a conceptual co-occurrence network of 57 679 unique conceptual links about healthcare guidelines.Peer reviewe

Emergency Notification on Mobile Devices – A Trade-off between Protection Motivation and Privacy Concern

Traditional design methods, based on analytical rationale, often cannot address upcoming challenges e.g., related to the digital business transformation in volatile environments. Analytical rationale assumes a particular result and provides the methods and tools for achieving it. Nowadays, however, the result of a business transformation is often not precisely known nor the ways and means to achieve it. As a result, methods and tools are required that foster creativity while allowing customization to specific requirements or stakeholder needs. This paper proposes customized design thinking processes, realized with a conceptual modelling approach. The approach supports creativity in transformative business design. It shows how numerous design thinking tools can be integrated into a single conceptual modelling approach - supported by a modelling platform. The platform facilitates efficient and flexible design of novel business solutions. The created models moreover serve as a formalized knowledge base that enables knowledge processing and reuse

Supporting Customized Design Thinking Using a Metamodel-based Approach

Traditional design methods, based on analytical rationale, often cannot address upcoming challenges e.g., related to the digital business transformation in volatile environments. Analytical rationale assumes a particular result and provides the methods and tools for achieving it. Nowadays, however, the result of a business transformation is often not precisely known nor the ways and means to achieve it. As a result, methods and tools are required that foster creativity while allowing customization to specific requirements or stakeholder needs. This paper proposes customized design thinking processes, realized with a conceptual modelling approach. The approach supports creativity in transformative business design. It shows how numerous design thinking tools can be integrated into a single conceptual modelling approach - supported by a modelling platform. The platform facilitates efficient and flexible design of novel business solutions. The created models moreover serve as a formalized knowledge base that enables knowledge processing and reuse

Learning Conceptual-Contextual Embeddings for Medical Text

External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks just like pre-trained language models. Our model effectively encodes the huge UMLS database by leveraging semantic generalizability. Experiments on electronic health records (EHRs) and medical text processing benchmarks showed our model gives a major boost to the performance of supervised medical NLP tasks