In this article, we present the E-sch approach for exploration of large scholarly datasets based on topic summary views. The goal of E-sch is to semantically summarize the dataset related to a potentially very large number of scholar publications (e.g., millions) by a list of few thousands topics, up to an ultimate list of hundreds of topic summaries to use for analyzing research dynamics and evolution at a more semantic, high-level of inquiry. Filter and Slice operators are defined in E-sch to enforce interactive scholarly data exploration along thematic and temporal perspectives.

Topic Summary Views for Exploration of Large Scholarly Datasets / S. Castano, A. Ferrara, S. Montanelli. - In: JOURNAL ON DATA SEMANTICS. - ISSN 1861-2032. - 7:3(2018 Sep), pp. 155-170.

Topic Summary Views for Exploration of Large Scholarly Datasets

S. Castano
Primo
;
A. Ferrara
Secondo
;
S. Montanelli
Ultimo
2018

Abstract

In this article, we present the E-sch approach for exploration of large scholarly datasets based on topic summary views. The goal of E-sch is to semantically summarize the dataset related to a potentially very large number of scholar publications (e.g., millions) by a list of few thousands topics, up to an ultimate list of hundreds of topic summaries to use for analyzing research dynamics and evolution at a more semantic, high-level of inquiry. Filter and Slice operators are defined in E-sch to enforce interactive scholarly data exploration along thematic and temporal perspectives.
Information Systems; Computer Networks and Communications; Artificial Intelligence
Settore INF/01 - Informatica
set-2018
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/596159
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