Difference between revisions of "Data and knowledge"
(Created page with "Data science involves many actions on data. Data mining, also known as knowledge extraction from data, requires many steps, before and after learning. A major specificity of o...") |
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Data science involves many actions on data. Data mining, also known as knowledge extraction from data, requires many steps, before and after learning. A major specificity of our team is not only to extract konwledge but also to use it, integrate knowledge in the learning process. Our research activities concern three areas. | Data science involves many actions on data. Data mining, also known as knowledge extraction from data, requires many steps, before and after learning. A major specificity of our team is not only to extract konwledge but also to use it, integrate knowledge in the learning process. Our research activities concern three areas. | ||
| − | =Data transformation= | + | ==Data transformation== |
We work with and learn from relational data, and more generally any data involving several "kinds" of objects. We study propositionalisation, ie. the automatic process to transform relational data into attribute-value data that can be used by any standard learner. | We work with and learn from relational data, and more generally any data involving several "kinds" of objects. We study propositionalisation, ie. the automatic process to transform relational data into attribute-value data that can be used by any standard learner. | ||
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We are specialised in data representation and model data using relational models or graphs. We particularly consider their adaptations to temporal and spatial data. | We are specialised in data representation and model data using relational models or graphs. We particularly consider their adaptations to temporal and spatial data. | ||
| − | = Knowledge modelling and semantic technologies= | + | == Knowledge modelling and semantic technologies== |
A first topic concerns qualitative spatial reasoning, and case-based reasoning. | A first topic concerns qualitative spatial reasoning, and case-based reasoning. | ||
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A second topic is on fuzzy reasoning using ontologies and on analogies between documents. | A second topic is on fuzzy reasoning using ontologies and on analogies between documents. | ||
| − | = Database construction and data quality= | + | == Database construction and data quality== |
Our activities include crowdsourcing, and the characterization and generation of suitable benchmarks. | Our activities include crowdsourcing, and the characterization and generation of suitable benchmarks. | ||
Latest revision as of 21:46, 2 June 2021
Data science involves many actions on data. Data mining, also known as knowledge extraction from data, requires many steps, before and after learning. A major specificity of our team is not only to extract konwledge but also to use it, integrate knowledge in the learning process. Our research activities concern three areas.
Data transformation
We work with and learn from relational data, and more generally any data involving several "kinds" of objects. We study propositionalisation, ie. the automatic process to transform relational data into attribute-value data that can be used by any standard learner.
We are specialised in data representation and model data using relational models or graphs. We particularly consider their adaptations to temporal and spatial data.
Knowledge modelling and semantic technologies
A first topic concerns qualitative spatial reasoning, and case-based reasoning.
A second topic is on fuzzy reasoning using ontologies and on analogies between documents.
Database construction and data quality
Our activities include crowdsourcing, and the characterization and generation of suitable benchmarks.