Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Osborne, Francesco; Motta, Enrico

doi:10.1007/978-3-030-00671-6_29

Cited by 16 publications

(17 citation statements)

References 28 publications

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“…Three works focus on the identification of candidate evolutionary actions (Kondylakis and Papadakis 2018;Grandi 2016;Osborne and Motta 2018;Benomrane et al 2016). Of these, one (Osborne and Motta 2018) aims to ease this step by allowing the user to specify requirements for new candidates and another (Benomrane et al 2016) allows an ontologist to select actions they want to see executed.…”

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

confidence: 99%

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%

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

Canito

Corchado²,

Marreiros³

2021

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Augur [52]), ontology concepts (e.g. SIM [11], POE [43]), and technologies (e.g. TTF [41], TechMiner [40]).…”

Section: The Computer Science Ontologymentioning

confidence: 99%

CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

2021

Self Cite

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Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

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“…Ontology Evolution. The Pragmatic Ontology Evolution (POE) [15] is an approach for selecting the best set of new concepts to include in the evolved version of an ontology so that i) is consistent with user requirements, ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. POE tests different combinations of several parameters to weigh the candidate concepts by measuring to the performance of the resulting ontologies on a set of tasks, such as instance tagging and generation of recommendations.…”

Section: The Computer Science Ontology: a Comprehensive Automatically-generated Taxonomy Of Research Areasmentioning

confidence: 99%

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

et al. 2020

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Ontologies of research areas are important tools for characterizing, exploring, and analyzing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

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Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Cited by 16 publications

References 28 publications

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

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

CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

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