International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013, Beijing, China, August 4 2013)
Identifieur interne : 001756 ( Main/Exploration ); précédent : 001755; suivant : 001757International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013, Beijing, China, August 4 2013)
Auteurs : Sergei O. Kuznetsov [Russie] ; Amedeo Napoli [France] ; Sebastian Rudolph [Allemagne]Source :
English descriptors
Abstract
This second edition of the FCA4AI workshop (the first edition was associated to the ECAI 2012 Conference, see http://www.fca4ai.hse.ru/), shows again that there are many AI researchers interested in FCA. Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, as well as information retrieval and text processing. Thus, there exist many natural links between FCA and AI. Accordingly, the focus in this workshop was on how can FCA support AI activities (knowledge processing) and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domains.
Url:
Affiliations:
- Allemagne, France, Russie
- District de Dresde, Grand Est, Lorraine (région), Saxe (Land)
- Dresde, Metz, Nancy
- Université de Lorraine, Université technique de Dresde
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Le document en format XML
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Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, as well as information retrieval and text processing. Thus, there exist many natural links between FCA and AI. Accordingly, the focus in this workshop was on how can FCA support AI activities (knowledge processing) and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domains.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>France</li><li>Russie</li></country><region><li>District de Dresde</li><li>Grand Est</li><li>Lorraine (région)</li><li>Saxe (Land)</li></region><settlement><li>Dresde</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li><li>Université technique de Dresde</li></orgName></list><tree><country name="Russie"><noRegion><name sortKey="Kuznetsov, Sergei O" sort="Kuznetsov, Sergei O" uniqKey="Kuznetsov S" first="Sergei O." last="Kuznetsov">Sergei O. Kuznetsov</name></noRegion></country><country name="France"><region name="Grand Est"><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name></region></country><country name="Allemagne"><region name="Saxe (Land)"><name sortKey="Rudolph, Sebastian" sort="Rudolph, Sebastian" uniqKey="Rudolph S" first="Sebastian" last="Rudolph">Sebastian Rudolph</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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