Similarity reasoning in formal concept analysis: from one- to many-valued contexts

Formica, Anna

doi:10.1007/s10115-018-1252-4

Cited by 25 publications

(14 citation statements)

References 52 publications

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“…Information retrieval, e-learning, expert systems and the semantic web often use FCA methods. In one of them [43], techniques based on different attribute-scaling methods are presented together with the concept of measuring cluster similarity and extending the method's applicability by similarity reasoning. This extension is shown in the example of the automatic generation of ontologies using the identification of overlapping knowledge in the common domain.…”

Section: Discussionmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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Background The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. Methods We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. Results The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients’ and physicians’ decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. Conclusion This theoretical model introduces future HT management as an understandable way of conceiving patients’ futures with a view to positively (or negatively) changing their behaviour. The model’s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

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Section: Discussionmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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“…Within the semantic similarity approaches, as for instance the one recently presented in [47] for Neural Networks, or hybrid similarity measures combining the shortest path lengths and the depths of subsumers [30], below we restrict our attention to the methods based on the information content [14], [33], Formal Concept Analysis [13], [43], IFCA (Formal Concept Analysis with Interval Type-2 Fuzzy sets) [15], Geographical Information Systems [16], [40], and different application domains, such as health [1], [24], and network security [44], just to mention a few examples. However the IC approach, although recognized as "the state of the art on semantic similarity" [3], [8], has shown some limitations, as discussed below.…”

Section: Related Workmentioning

confidence: 99%

An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy

Formica

Taglino

2021

IEEE Access

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This paper addresses the notion of semantic similarity between concepts organized according to a taxonomy, based on the well-known information content approach. This approach has been widely experimented in the literature over the years and, in general, outperforms other proposals which do not originate from it. However, it shows some limitations related to the notion of generic sense of a concept. In this paper we illustrate the problem arising by using the traditional approach, and a novel informationtheoretic definition of semantic similarity in a taxonomy is proposed which also takes into account the intended sense of a concept in a given context. This proposal has been applied to some among the most representative state-of-the-art similarity measures based on the information content approach, and the experiment shows that it achieves very high correlation values with human judgment.

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“…Over the past decade, a wide range of application fields in the international community have been developed, for example, psychology, AI, data, and data analysis, and some specialists use other kinds of graphs in a text representation; in particular, ''formal concept analysis'' (FCA) was recently enhanced by [16] and [17]. FCA is the basic method used for an arrangement of objects and properties in a hierarchy or formal ontology.…”

Section: A Formal Concept Analysis (Fca)mentioning

confidence: 99%

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

Osman

Barukub

2020

IEEE Access

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Graph-based text representation is one of the important preprocessing steps in data and text mining, Natural Language Processing (NLP), and information retrieval approaches. The graph-based methods focus on how to represent text documents in the shape of a graph to exploit the best features of their characteristics. This study reviews and lists the advantages and disadvantages of such methods employed or developed in graph-based text representations. The literature shows that some of the proposed graph-based methods suffer from a lack of representing texts in certain situations. Currently, several techniques are commonly used in graph-based text representation. However, there are still some weaknesses and shortages in these techniques and tools that significantly affect the success of graph representation and graph matching. In this review, we conduct an inclusive survey of the state of the art in graph-based text representation and learning. We provide a formal description of the problem of graph-based text representation and introduce some basic concepts. More significantly, this study proposes a new taxonomy of graph-based text representation, categorizing the existing studies based on representation characteristics and scheme techniques. In terms of the representation scheme taxonomy, we introduce four main types of conceptual graph schemes and summarize the challenges faced in each scheme. The main issues of graph representation, such as research topics and the sub-taxonomy of graph models for web documents, are introduced and categorized. This research also covers some tasks of understanding natural language processing (NLP) that depend on different types of graph structures. In addition, the graph matching taxonomy implements three main categories based on the matching approach, including structural-, semantic-, and similarity-based approaches. Moreover, a deep comparison of these approaches is discussed and reported in terms of methods and tools, the concepts of matching and locality, and the application domains that use these tools. Finally, the paper recommends seven promising future study directions in the graph-based text representation field. These recommendation points are summarized and highlighted as open problems and challenges of graphbased text representation and learning to facilitate and fill the research gaps for scientific researchers in this field.

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Similarity reasoning in formal concept analysis: from one- to many-valued contexts

Cited by 25 publications

References 52 publications

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

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