Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.

Bianchi, F., Palmonari, M., Cremaschi, M., Fersini, E. (2017). Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs. In Proceedings, Part I 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017 (pp.120-135). Springer Verlag [10.1007/978-3-319-58068-5_8].

Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs

BIANCHI, FEDERICO
Primo
;
PALMONARI, MATTEO LUIGI
Secondo
;
CREMASCHI, MARCO
Penultimo
;
FERSINI, ELISABETTA
Ultimo
2017

Abstract

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.
paper
Active Learning, Knowledge Graph
English
European Semantic Web Conference ESWC 28 may - 1st June
2017
Proceedings, Part I 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017
9783319580678
2017
10249
120
135
partially_open
Bianchi, F., Palmonari, M., Cremaschi, M., Fersini, E. (2017). Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs. In Proceedings, Part I 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017 (pp.120-135). Springer Verlag [10.1007/978-3-319-58068-5_8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/153941
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