Knowledge discovery in ontologies
Intelligent Data Analysis, ISSN: 1088-467X, Vol: 16, Issue: 3, Page: 513-534
2012
- 4Citations
- 7Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit knowledge extracted is in the form of "Influence Rules" i.e. rules structured as: if property p-1 of concept c-1 has value v-1, then property p-2 of concept c-2 has value v-2 with probability π. The technique is completely general and applicable to whatever domain. The Influence Rules can be used to integrate existing knowledge or to support any other data mining process. A case study about an ontology that describes intrusion detection is used to illustrate how the method works. © 2012 - IOS Press and the authors. All rights reserved.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84861377428&origin=inward; http://dx.doi.org/10.3233/ida-2012-0536; https://journals.sagepub.com/doi/full/10.3233/IDA-2012-0536; https://dx.doi.org/10.3233/ida-2012-0536; https://content.iospress.com:443/articles/intelligent-data-analysis/ida00536
SAGE Publications
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know