Two situations can be considered analogous if they share a common pattern. Analogical reasoning is the task of nding analogies and inferencing missing terms in them. Since natural language is ambiguous, as the same word can refer to different entities, the use of disambiguated entities from Knowledge Graphs for analogical reasoning might bring to better results. Also, entities have types, i.e. classes, in an ontology, from which they inherit characteristics and properties. In this work we focus on a method to represent entities and their types in a joint vector space to do analogical reasoning. We experiment our representations on a dataset that contains analogies on entities and we show that extending the entity representations with information coming from the types improves analogical reasoning results.

Bianchi, F., Palmonari, M. (2017). Joint learning of entity and type embeddings for analogical reasoning with entities. In NL4AI 2017 Natural Language for Artificial Intelligence. Proceedings of the 1st Workshop on Natural Language for Artificial Intelligence co-located with 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), Bari, Italy, November 16-17, 2017 (pp.57-68). CEUR-WS.

Joint learning of entity and type embeddings for analogical reasoning with entities

BIANCHI, FEDERICO
Primo
;
Palmonari, Matteo
Secondo
2017

Abstract

Two situations can be considered analogous if they share a common pattern. Analogical reasoning is the task of nding analogies and inferencing missing terms in them. Since natural language is ambiguous, as the same word can refer to different entities, the use of disambiguated entities from Knowledge Graphs for analogical reasoning might bring to better results. Also, entities have types, i.e. classes, in an ontology, from which they inherit characteristics and properties. In this work we focus on a method to represent entities and their types in a joint vector space to do analogical reasoning. We experiment our representations on a dataset that contains analogies on entities and we show that extending the entity representations with information coming from the types improves analogical reasoning results.
No
paper
Knowledge Representation, Artificial Intelligence, Knowledge Graphs
English
1st Workshop on Natural Language for Artificial Intelligence, NL4AI 2017
http://ceur-ws.org/
Bianchi, F., Palmonari, M. (2017). Joint learning of entity and type embeddings for analogical reasoning with entities. In NL4AI 2017 Natural Language for Artificial Intelligence. Proceedings of the 1st Workshop on Natural Language for Artificial Intelligence co-located with 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), Bari, Italy, November 16-17, 2017 (pp.57-68). CEUR-WS.
Bianchi, F; Palmonari, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/195343
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