Managing changes in distributed biomedical ontologies using hierarchical distributed graph transformation

Shaban-Nejad, Arash; Haarslev, Volker

doi:10.1504/ijdmb.2015.066334

Cited by 10 publications

(8 citation statements)

References 53 publications

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“…Of the retrieved results, four describe change-based approaches (Kondylakis and Papadakis 2018;Tsalapati et al 2017;Shaban-Nejad and Haarslev 2015;Peixoto et al 2016), while one (Grandi 2016) describes a naïve approach. Bayoudhi et al (2017) tries to bridge the gap between the approaches by materializing both the reference and latest versions.…”

Section: Rq1: What Methods Have Been Used To Represent the Evolution Of Knowledge Over Time?mentioning

confidence: 99%

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A systematic review on time-constrained ontology evolution in predictive maintenance

Canito

Corchado²,

Marreiros³

2021

Artif Intell Rev

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With the modernization of industry and introduction of IoT, maintenance practices have been moving from reactive to proactive and predictive approaches. The identification of faults often relies on the analysis of real-time data provided by streams and unstructured sources. Ontologies have been applied to the maintenance field in order to add a semantic layer to the data and facilitate interoperability, and combined with other approaches for explainability and fault diagnosis, among others. In such a time-sensitive domain, it is important that ontologies go beyond static representations of the domain and allow not only for the incorporation of time related knowledge, but must also be able to adapt to new knowledge and evolve. This systematic review presents four research questions to provide a general understanding of the state of the art of the representation of time and ontology evolution in the predictive maintenance field. The results have shown that there are several ways of representing the evolution of knowledge that are fairly established and several specific evolutionary actions are discriminated and analyzed. Similarly, there is a diverse group of metrics that can be exploited to measure change and to establish evolutionary trends and even predict future stages of the ontology. Studies on the representation of time show us that it can be done either quantitative or qualitatively, with some approaches combining the two. Applications of these to the problem of ontology evolution are still in the open. Finally, results show that while applications of ontologies to the field of predictive maintenance are plenty, there are not many studies focusing on their evolution or in the effective application of their ability to reason with time constraints. The results obtained in this systematic review are particularly relevant for devising solutions that make use of the ontology's potential for time representation and evolution in the predictive maintenance field.

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Section: Rq1: What Methods Have Been Used To Represent the Evolution Of Knowledge Over Time?mentioning

confidence: 99%

“…Ontology evolution, therefore, becomes a necessity and can be employed to identify and model these changes, ensuring that the domain description is up-to-date and remains accurate. Many existing approaches to ontology evolution, however, fail to provide formalisms for temporal abstract notions (Shaban-Nejad and Haarslev 2015).…”

Section: Introductionmentioning

confidence: 99%

A systematic review on time-constrained ontology evolution in predictive maintenance

Canito

Corchado²,

Marreiros³

2021

Artif Intell Rev

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show abstract

“…All updates in the underlying ontologies and semantic structure will be managed through our previously implemented framework [23,24]. As for the target audience, the system is intended for a variety of users depending on the domain, including social workers, health care providers (eg, nurses, physicians), and caregivers (child-parent).…”

Section: Principal Findingsmentioning

confidence: 99%

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

Ammar¹,

Shaban-Nejad²

2020

JMIR Med Inform

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Background The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. Objective In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. Methods We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. Results To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. Conclusions This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

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“…Distributed hierarchical graphs form the basis upon which the agent-based OWL semantic web ontological model is built and in which graph transformations can take place at various levels of abstraction [42]. Other interesting hierarchical representations include that of L-Graphs in which edges and nodes are labelled by graphs [41], M -adhesive Graph Transformation Systems [38] that provide a generic algebraic definition to cater for varying graphs types, that of the verbose property graphs used in graph analytics [28], and the implementation found in [45]; that also makes use of attributes and multi-typed sub-graphs.…”

Section: H-graphsmentioning

confidence: 99%

Attributed Hierarchical Port Graphs and Applications

Ene

Fernández

Pinaud

2018

Electron. Proc. Theor. Comput. Sci.

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We present attributed hierarchical port graphs (AHP) as an extension of port graphs that aims at facilitating the design of modular port graph models for complex systems. AHP consist of a number of interconnected layers, where each layer defines a port graph whose nodes may link to layers further down the hierarchy; attributes are used to store user-defined data as well as visualisation and run-time system parameters. We also generalise the notion of strategic port graph rewriting (a particular kind of graph transformation system, where port graph rewriting rules are controlled by user-defined strategies) to deal with AHP following the Single Push-out approach. We outline examples of application in two areas: functional programming and financial modelling.

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Managing changes in distributed biomedical ontologies using hierarchical distributed graph transformation

Cited by 10 publications

References 53 publications

A systematic review on time-constrained ontology evolution in predictive maintenance

A systematic review on time-constrained ontology evolution in predictive maintenance

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

Attributed Hierarchical Port Graphs and Applications

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